Killzones And Macros LibraryKillzones & Macros Library for Trading Sessions
This Pine Script library is designed to help traders identify and act during high-volatility trading windows, commonly referred to as "Killzones." These are specific times during the day when institutional traders are most active, resulting in increased liquidity and price movement. The library provides boolean fields that return true when the current time falls within one of the killzones or macroeconomic event windows, allowing for enhanced trade timing and precision.
Killzones Include:
London Open, New York Open, Midnight Open, London Lunch, New York PM, and more.
Capture high-volume periods like Power Hour, Equities Open, and Asian Range.
Macros:
Identify key moments like London 02:33, New York 08:50, and other significant times aligned with market movements or events.
This library is perfect for integrating into your custom strategies, backtesting, or setting alerts for optimal trade execution during major trading sessions and events.
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ICT Asian Range and KillzonesThis TradingView indicator highlights key trading sessions and their price ranges on a chart. It identifies the Asian Range and the Killzones for both the London Open and New York Open sessions. Here’s a brief breakdown:
Asian Range:
Defines the high and low price levels during the Asian trading session (between the specified start and end hours, default 00:00 to 04:00 UTC).
Plots horizontal lines to mark the highest and lowest prices reached during the Asian session.
Adds labels showing the values of these high and low points after the session ends.
London and New York Killzones:
Identifies the “Killzones” or key trading windows for the London Open (default 06:00 to 09:00 UTC) and the New York Open (default 11:00 to 14:00 UTC).
Tracks the high and low price levels within these windows and plots rectangles ("boxes") on the chart to visualize these ranges.
The boxes are color-coded and customizable, indicating potential areas of high market activity or volatility.
Customizable Visuals:
Users can adjust the colors, border widths, and other visual properties for better clarity and chart integration.
[ALGOA+] Markov Chains Library by @metacamaleoLibrary "MarkovChains"
Markov Chains library by @metacamaleo. Created in 09/08/2024.
This library provides tools to calculate and visualize Markov Chain-based transition matrices and probabilities. This library supports two primary algorithms: a rolling window Markov Chain and a conditional Markov Chain (which operates based on specified conditions). The key concepts used include Markov Chain states, transition matrices, and future state probabilities based on past market conditions or indicators.
Key functions:
- `mc_rw()`: Builds a transition matrix using a rolling window Markov Chain, calculating probabilities based on a fixed length of historical data.
- `mc_cond()`: Builds a conditional Markov Chain transition matrix, calculating probabilities based on the current market condition or indicator state.
Basically, you will just need to use the above functions on your script to default outputs and displays.
Exported UDTs include:
- s_map: An UDT variable used to store a map with dummy states, i.e., if possible states are bullish, bearish, and neutral, and current is bullish, it will be stored
in a map with following keys and values: "bullish", 1; "bearish", 0; and "neutral", 0. You will only use it to customize your own script, otherwise, it´s only for internal use.
- mc_states: This UDT variable stores user inputs, calculations and MC outputs. As the above, you don´t need to use it, but you may get features to customize your own script.
For example, you may use mc.tm to get the transition matrix, or the prob map to customize the display. As you see, functions are all based on mc_states UDT. The s_map UDT is used within mc_states´s s array.
Optional exported functions include:
- `mc_table()`: Displays the transition matrix in a table format on the chart for easy visualization of the probabilities.
- `display_list()`: Displays a map (or array) of string and float/int values in a table format, used for showing transition counts or probabilities.
- `mc_prob()`: Calculates and displays probabilities for a given number of future bars based on the current state in the Markov Chain.
- `mc_all_states_prob()`: Calculates probabilities for all states for future bars, considering all possible transitions.
The above functions may be used to customize your outputs. Use the returned variable mc_states from mc_rw() and mc_cond() to display each of its matrix, maps or arrays using mc_table() (for matrices) and display_list() (for maps and arrays) if you desire to debug or track the calculation process.
See the examples in the end of this script.
Have good trading days!
Best regards,
@metacamaleo
-----------------------------
KEY FUNCTIONS
mc_rw(state, length, states, pred_length, show_table, show_prob, table_position, prob_position, font_size)
Builds the transition matrix for a rolling window Markov Chain.
Parameters:
state (string) : The current state of the market or system.
length (int) : The rolling window size.
states (array) : Array of strings representing the possible states in the Markov Chain.
pred_length (int) : The number of bars to predict into the future.
show_table (bool) : Boolean to show or hide the transition matrix table.
show_prob (bool) : Boolean to show or hide the probability table.
table_position (string) : Position of the transition matrix table on the chart.
prob_position (string) : Position of the probability list on the chart.
font_size (string) : Size of the table font.
Returns: The transition matrix and probabilities for future states.
mc_cond(state, condition, states, pred_length, show_table, show_prob, table_position, prob_position, font_size)
Builds the transition matrix for conditional Markov Chains.
Parameters:
state (string) : The current state of the market or system.
condition (string) : A string representing the condition.
states (array) : Array of strings representing the possible states in the Markov Chain.
pred_length (int) : The number of bars to predict into the future.
show_table (bool) : Boolean to show or hide the transition matrix table.
show_prob (bool) : Boolean to show or hide the probability table.
table_position (string) : Position of the transition matrix table on the chart.
prob_position (string) : Position of the probability list on the chart.
font_size (string) : Size of the table font.
Returns: The transition matrix and probabilities for future states based on the HMM.
Rolling Calmar Ratio with Ref Ticker V1.0 [ADRIDEM]Overview
The Rolling Calmar Ratio with Ref Ticker script is designed to offer a comprehensive view of the Calmar ratios for a selected reference ticker and the current ticker. This script helps investors compare risk-adjusted returns between two assets over a rolling period, providing insights into their relative performance and risk. Below is a detailed presentation of the script and its unique features.
Unique Features of the New Script
Reference Ticker Comparison : Allows users to compare the Calmar ratio of the current ticker with a reference ticker, providing a relative performance analysis. Default ticker is BTCUSDT but can be changed.
Customizable Rolling Window : Enables users to set the length for the rolling window, adapting to different market conditions and timeframes. The default value is 252 bars, which approximates one year of trading days, but it can be adjusted as needed.
Smoothing Option : Includes an option to apply a smoothing simple moving average (SMA) to the Calmar ratios, helping to reduce noise and highlight trends. The smoothing length is customizable, with a default value of 14 bars.
Visual Indicators : Plots the smoothed Calmar ratios for both the reference ticker and the current ticker, with distinct colors for easy comparison. Additionally, a horizontal line helps identify key levels.
Dynamic Background Color : Adds a gray-blue transparent background between the Calmar ratio levels of 0 and 1, highlighting the critical region where risk-adjusted returns are assessed.
Originality and Usefulness
This script uniquely combines the analysis of Calmar ratios for a reference ticker and the current ticker, providing a comparative view of their risk-adjusted returns. The inclusion of a customizable rolling window and smoothing option enhances its adaptability and usefulness in various market conditions.
Signal Description
The script includes several features that highlight potential insights into the risk-adjusted returns of the assets:
Reference Ticker Calmar Ratio : Plotted as a red line, this represents the smoothed Calmar ratio for the user-selected reference ticker.
Current Ticker Calmar Ratio : Plotted as a white line, this represents the smoothed Calmar ratio for the current ticker.
Horizontal Lines and Background Color : A line at 0 helps to quickly identify the regions of positive and negative risk-adjusted returns.
These features assist in identifying relative performance differences and confirming the strength or weakness of risk-adjusted returns between the two tickers.
Detailed Description
Input Variables
Length for Rolling Window (`length`) : Defines the range for calculating the rolling Calmar ratio. Default is 252.
Smoothing Length (`smoothing_length`) : The number of periods for the smoothing SMA. Default is 14.
Reference Ticker (`ref_ticker`) : The ticker symbol for the reference asset. Default is "BINANCE:BTCUSDT".
Functionality
Calmar Ratio Calculation : The script calculates the cumulative returns and maximum drawdown for both the reference ticker and the current ticker. These values are used to compute the Calmar ratio.
```pine
ref_cumulativeReturn = (ref_close / ta.valuewhen(ta.lowest(ref_close, length) == ref_close, ref_close, 0)) - 1
ref_rollingMax = ta.highest(ref_close, length)
ref_drawdown = (ref_close - ref_rollingMax) / ref_rollingMax
ref_maxDrawdown = ta.lowest(ref_drawdown, length)
ref_calmarRatio = ref_cumulativeReturn / math.abs(ref_maxDrawdown)
current_cumulativeReturn = (close / ta.valuewhen(ta.lowest(close, length) == close, close, 0)) - 1
current_rollingMax = ta.highest(close, length)
current_drawdown = (close - current_rollingMax) / current_rollingMax
current_maxDrawdown = ta.lowest(current_drawdown, length)
current_calmarRatio = current_cumulativeReturn / math.abs(current_maxDrawdown)
```
Smoothing : A simple moving average is applied to the Calmar ratios to smooth the data.
```pine
smoothed_ref_calmarRatio = ta.sma(ref_calmarRatio, smoothing_length)
smoothed_current_calmarRatio = ta.sma(current_calmarRatio, smoothing_length)
```
Plotting : The script plots the smoothed Calmar ratios for both the reference ticker and the current ticker, along with a horizontal line.
```pine
plot(smoothed_ref_calmarRatio, title="Ref Ticker Calmar Ratio", color=color.rgb(255, 82, 82, 50), linewidth=2)
plot(smoothed_current_calmarRatio, title="Current Ticker Calmar Ratio", color=color.white, linewidth=2)
h0 = hline(0, "Zero Line", color=color.gray)
fill(h0, h1, color=color.rgb(33, 150, 243, 90), title="Background")
```
How to Use
Configuring Inputs : Adjust the detection length and smoothing length as needed. Set the reference ticker to the desired asset for comparison.
Interpreting the Indicator : Use the plotted Calmar ratios and horizontal line to assess the relative risk-adjusted returns of the reference and current tickers.
Signal Confirmation : Look for differences in the Calmar ratios to identify potential performance advantages or weaknesses. The horizontal line helps to highlight key levels of risk-adjusted returns.
This script provides a detailed comparative view of risk-adjusted returns, aiding in more informed decision-making by highlighting key differences between the reference ticker and the current ticker.
Rolling Sortino Ratio with Ref Ticker V1.0 [ADRIDEM]Overview
The Rolling Sortino Ratio with Ref Ticker script is designed to offer a comprehensive view of the Sortino ratios for a selected reference ticker and the current ticker. This script helps investors compare risk-adjusted returns between two assets over a rolling period, providing insights into their relative performance and risk. Below is a detailed presentation of the script and its unique features.
Unique Features of the New Script
Reference Ticker Comparison : Allows users to compare the Sortino ratio of the current ticker with a reference ticker, providing a relative performance analysis. Default ticker is BTCUSDT but can be changed.
Customizable Rolling Window : Enables users to set the length for the rolling window, adapting to different market conditions and timeframes. The default value is 252 bars, which approximates one year of trading days, but it can be adjusted as needed.
Smoothing Option : Includes an option to apply a smoothing simple moving average (SMA) to the Sortino ratios, helping to reduce noise and highlight trends. The smoothing length is customizable, with a default value of 4 bars.
Visual Indicators : Plots the smoothed Sortino ratios for both the reference ticker and the current ticker, with distinct colors for easy comparison. Additionally, horizontal lines and a shaded background help identify key levels.
Dynamic Background Color : Adds a gray-blue transparent background between the Sortino ratio levels of 0 and 1, highlighting the critical region where risk-adjusted returns are assessed.
Originality and Usefulness
This script uniquely combines the analysis of Sortino ratios for a reference ticker and the current ticker, providing a comparative view of their risk-adjusted returns. The inclusion of a customizable rolling window and smoothing option enhances its adaptability and usefulness in various market conditions.
Signal Description
The script includes several features that highlight potential insights into the risk-adjusted returns of the assets:
Reference Ticker Sortino Ratio : Plotted as a red line, this represents the smoothed Sortino ratio for the user-selected reference ticker.
Current Ticker Sortino Ratio : Plotted as a white line, this represents the smoothed Sortino ratio for the current ticker.
Horizontal Lines and Background Color : Lines at 0 and 1, along with a shaded background between these levels, help to quickly identify the regions of positive and strong risk-adjusted returns.
These features assist in identifying relative performance differences and confirming the strength or weakness of risk-adjusted returns between the two tickers.
Detailed Description
Input Variables
Length for Rolling Window (`length`) : Defines the range for calculating the rolling Sortino ratio. Default is 252.
Smoothing Length (`smoothing_length`) : The number of periods for the smoothing SMA. Default is 4.
Annual Risk-Free Rate (`riskFreeRate`) : The annual risk-free rate used in the Sortino ratio calculation. Default is 0.02 (2%).
Reference Ticker (`ref_ticker`) : The ticker symbol for the reference asset. Default is "BINANCE:BTCUSDT".
Functionality
Sortino Ratio Calculation : The script calculates the daily returns, mean return, and downside deviation for both the reference ticker and the current ticker. These values are used to compute the annualized Sortino ratio.
```pine
ref_dailyReturn = ta.change(ref_close) / ref_close
ref_meanReturn = ta.sma(ref_dailyReturn, length)
ref_downsideDeviation = ta.stdev(math.min(ref_dailyReturn, 0), length)
ref_annualizedReturn = ref_meanReturn * length
ref_annualizedDownsideDev = ref_downsideDeviation * math.sqrt(length)
ref_sortinoRatio = (ref_annualizedReturn - riskFreeRate) / ref_annualizedDownsideDev
```
Smoothing : A simple moving average is applied to the Sortino ratios to smooth the data.
```pine
smoothed_ref_sortinoRatio = ta.sma(ref_sortinoRatio, smoothing_length)
smoothed_current_sortinoRatio = ta.sma(current_sortinoRatio, smoothing_length)
```
Plotting : The script plots the smoothed Sortino ratios for both the reference ticker and the current ticker, along with horizontal lines and a shaded background.
```pine
plot(smoothed_ref_sortinoRatio, title="Ref Ticker Sortino Ratio", color=color.rgb(255, 82, 82, 50), linewidth=2)
plot(smoothed_current_sortinoRatio, title="Current Ticker Sortino Ratio", color=color.white, linewidth=2)
h0 = hline(0, "Zero Line", color=color.gray)
h1 = hline(1, "One Line", color=color.gray)
fill(h0, h1, color=color.rgb(33, 150, 243, 90), title="Background")
```
How to Use
Configuring Inputs : Adjust the detection length, smoothing length, and risk-free rate as needed. Set the reference ticker to the desired asset for comparison.
Interpreting the Indicator : Use the plotted Sortino ratios and background shading to assess the relative risk-adjusted returns of the reference and current tickers.
Signal Confirmation : Look for differences in the Sortino ratios to identify potential performance advantages or weaknesses. The background shading helps to highlight key levels of risk-adjusted returns.
This script provides a detailed comparative view of risk-adjusted returns, aiding in more informed decision-making by highlighting key differences between the reference ticker and the current ticker.
Rolling Sharpe Ratio with Ref Ticker V1.0 [ADRIDEM]Overview
The Rolling Sharpe Ratio with Ref Ticker script is designed to offer a comprehensive view of the Sharpe ratios for a selected reference ticker and the current ticker. This script helps investors compare risk-adjusted returns between two assets over a rolling period, providing insights into their relative performance and risk. Below is a detailed presentation of the script and its unique features.
Unique Features of the New Script
Reference Ticker Comparison : Allows users to compare the Sharpe ratio of the current ticker with a reference ticker, providing a relative performance analysis. Default ticker is BTCUSDT but can be changed.
Customizable Rolling Window : Enables users to set the length for the rolling window, adapting to different market conditions and timeframes. The default value is 252 bars, which approximates one year of trading days, but it can be adjusted as needed.
Smoothing Option : Includes an option to apply a smoothing simple moving average (SMA) to the Sharpe ratios, helping to reduce noise and highlight trends. The smoothing length is customizable, with a default value of 4 bars.
Visual Indicators : Plots the smoothed Sharpe ratios for both the reference ticker and the current ticker, with distinct colors for easy comparison. Additionally, horizontal lines and a shaded background help identify key levels.
Dynamic Background Color : Adds a gray-blue transparent background between the Sharpe ratio levels of 0 and 1, highlighting the critical region where risk-adjusted returns are assessed.
Originality and Usefulness
This script uniquely combines the analysis of Sharpe ratios for a reference ticker and the current ticker, providing a comparative view of their risk-adjusted returns. The inclusion of a customizable rolling window and smoothing option enhances its adaptability and usefulness in various market conditions.
Signal Description
The script includes several features that highlight potential insights into the risk-adjusted returns of the assets:
Reference Ticker Sharpe Ratio : Plotted as a red line, this represents the smoothed Sharpe ratio for the user-selected reference ticker.
Current Ticker Sharpe Ratio : Plotted as a white line, this represents the smoothed Sharpe ratio for the current ticker.
Horizontal Lines and Background Color : Lines at 0 and 1, along with a shaded background between these levels, help to quickly identify the regions of positive and strong risk-adjusted returns.
These features assist in identifying relative performance differences and confirming the strength or weakness of risk-adjusted returns between the two tickers.
Detailed Description
Input Variables
Length for Rolling Window (`length`) : Defines the range for calculating the rolling Sharpe ratio. Default is 252.
Smoothing Length (`smoothing_length`) : The number of periods for the smoothing SMA. Default is 4.
Annual Risk-Free Rate (`riskFreeRate`) : The annual risk-free rate used in the Sharpe ratio calculation. Default is 0.02 (2%).
Reference Ticker (`ref_ticker`) : The ticker symbol for the reference asset. Default is "BINANCE:BTCUSDT".
Functionality
Sharpe Ratio Calculation : The script calculates the daily returns, mean return, and standard deviation for both the reference ticker and the current ticker. These values are used to compute the annualized Sharpe ratio.
```pine
ref_dailyReturn = ta.change(ref_close) / ref_close
ref_meanReturn = ta.sma(ref_dailyReturn, length)
ref_stdDevReturn = ta.stdev(ref_dailyReturn, length)
ref_annualizedReturn = ref_meanReturn * length
ref_annualizedStdDev = ref_stdDevReturn * math.sqrt(length)
ref_sharpeRatio = (ref_annualizedReturn - riskFreeRate) / ref_annualizedStdDev
```
Smoothing : A simple moving average is applied to the Sharpe ratios to smooth the data.
```pine
smoothed_ref_sharpeRatio = ta.sma(ref_sharpeRatio, smoothing_length)
smoothed_current_sharpeRatio = ta.sma(current_sharpeRatio, smoothing_length)
```
Plotting : The script plots the smoothed Sharpe ratios for both the reference ticker and the current ticker, along with horizontal lines and a shaded background.
```pine
plot(smoothed_ref_sharpeRatio, title="Ref Ticker Sharpe Ratio", color=color.rgb(255, 82, 82, 50), linewidth=2)
plot(smoothed_current_sharpeRatio, title="Current Ticker Sharpe Ratio", color=color.white, linewidth=2)
h0 = hline(0, "Zero Line", color=color.gray)
h1 = hline(1, "One Line", color=color.gray)
fill(h0, h1, color=color.rgb(33, 150, 243, 90), title="Background")
```
How to Use
Configuring Inputs : Adjust the detection length, smoothing length, and risk-free rate as needed. Set the reference ticker to the desired asset for comparison.
Interpreting the Indicator : Use the plotted Sharpe ratios and background shading to assess the relative risk-adjusted returns of the reference and current tickers.
Signal Confirmation : Look for differences in the Sharpe ratios to identify potential performance advantages or weaknesses. The background shading helps to highlight key levels of risk-adjusted returns.
This script provides a detailed comparative view of risk-adjusted returns, aiding in more informed decision-making by highlighting key differences between the reference ticker and the current ticker.
Double Vegas SuperTrend Enhanced - Strategy [presentTrading]
█ Introduction and How It Is Different
The "Double Vegas SuperTrend Enhanced" strategy is a sophisticated trading system that combines two Vegas SuperTrend Enhanced. Very Powerful!
Let's celebrate the joy of Children's Day on June 1st! Enjoyyy!
BTCUSD LS performance
The strategy aims to pinpoint market trends with greater accuracy and generate trades that align with the overall market direction.
This approach differentiates itself by integrating volatility adjustments and leveraging the Vegas Channel's width to refine the SuperTrend calculations, resulting in a dynamic and responsive trading system.
Additionally, the strategy incorporates customizable take-profit and stop-loss levels, providing traders with a robust framework for risk management.
-> check Vegas SuperTrend Enhanced - Strategy
█ Strategy, How It Works: Detailed Explanation
🔶 Vegas Channel and SuperTrend Calculations
The strategy initiates by calculating the Vegas Channel, which is derived from a simple moving average (SMA) and the standard deviation (STD) of the closing prices over a specified window length. This channel helps in measuring market volatility and forms the basis for adjusting the SuperTrend indicator.
Vegas Channel Calculation:
- vegasMovingAverage = SMA(close, vegasWindow)
- vegasChannelStdDev = STD(close, vegasWindow)
- vegasChannelUpper = vegasMovingAverage + vegasChannelStdDev
- vegasChannelLower = vegasMovingAverage - vegasChannelStdDev
SuperTrend Multiplier Adjustment:
- channelVolatilityWidth = vegasChannelUpper - vegasChannelLower
- adjustedMultiplier = superTrendMultiplierBase + volatilityAdjustmentFactor * (channelVolatilityWidth / vegasMovingAverage)
The adjusted multiplier enhances the SuperTrend's sensitivity to market volatility, making it more adaptable to changing market conditions.
BTCUSD Local picture.
🔶 Average True Range (ATR) and SuperTrend Values
The ATR is computed over a specified period to measure market volatility. Using the ATR and the adjusted multiplier, the SuperTrend upper and lower levels are determined.
ATR Calculation:
- averageTrueRange = ATR(atrPeriod)
**SuperTrend Calculation:**
- superTrendUpper = hlc3 - (adjustedMultiplier * averageTrueRange)
- superTrendLower = hlc3 + (adjustedMultiplier * averageTrueRange)
The SuperTrend levels are continuously updated based on the previous values and the current market trend direction. The market trend is determined by comparing the closing prices with the SuperTrend levels.
Trend Direction:
- If close > superTrendLowerPrev, then marketTrend = 1 (bullish)
- If close < superTrendUpperPrev, then marketTrend = -1 (bearish)
🔶 Trade Entry and Exit Conditions
The strategy generates trade signals based on the alignment of both SuperTrends. Trades are executed only when both SuperTrends indicate the same market direction.
Entry Conditions:
- Long Position: Both SuperTrends must signal a bullish trend.
- Short Position: Both SuperTrends must signal a bearish trend.
Exit Conditions:
- Positions are exited if either SuperTrend reverses its trend direction.
- Additional conditions include holding periods and configurable take-profit and stop-loss levels.
█ Trade Direction
The strategy allows traders to specify the desired trade direction through a customizable input setting. Options include:
- Long: Only enter long positions.
- Short: Only enter short positions.
- Both: Enter both long and short positions based on the market conditions.
█ Usage
To utilize the "Double Vegas SuperTrend Enhanced" strategy, traders need to configure the input settings according to their trading preferences and market conditions. The strategy includes parameters for ATR periods, Vegas Channel window lengths, SuperTrend multipliers, volatility adjustment factors, and risk management settings such as hold days, take-profit, and stop-loss percentages.
█ Default Settings
The strategy comes with default settings that can be adjusted to fit individual trading styles:
- trade Direction: Both (allows trading in both long and short directions for maximum flexibility).
- ATR Periods: 10 for SuperTrend 1 and 5 for SuperTrend 2 (shorter ATR period results in more sensitivity to recent price movements).
- Vegas Window Lengths: 100 for SuperTrend 1 and 200 for SuperTrend 2 (longer window length results in smoother moving averages and less sensitivity to short-term volatility).
- SuperTrend Multipliers: 5 for SuperTrend 1 and 7 for SuperTrend 2 (higher multipliers lead to wider SuperTrend channels, reducing the frequency of trades).
- Volatility Adjustment Factors: 5 for SuperTrend 1 and 7 for SuperTrend 2 (higher adjustment factors increase the responsiveness to changes in market volatility).
- Hold Days: 5 (defines the minimum duration a position is held, ensuring trades are not exited prematurely).
- Take Profit: 30% (sets the target profit level to lock in gains).
- Stop Loss: 20% (sets the maximum acceptable loss level to mitigate risk).
7 hours a day by Yasser (YWMAAAWORLD)Hey there, traders! Today, we're diving into a nifty Pine Script called "7 hours a day," crafted by me Yasser (YWMAAAWorld). So, what's the scoop?
Imagine having a tool that highlights specific times on your chart like clockwork, making your trading day a breeze. That's precisely what this script does. It's like having a personal assistant reminding you of the important moments in the market.
Picture this: as the clock strikes 8:00 PM and 3:00 AM, our script draws these magical lines on your chart. These aren't just any lines; they're your guides, marking the boundaries of a crucial 7-hour period. Think of it as your trading sanctuary within the chaos of the market.
But wait, there's more! Our script isn't just about pretty lines. It's smart too. It knows when it's a weekend or Monday morning, so you can kick back and relax without unnecessary clutter on your chart.
Now, here's where the magic really happens. Within these 7-hour windows, our script calculates the highest and lowest price points, giving you a clear picture of market dynamics during those crucial hours. It's like having a crystal ball revealing the market's secrets.
So, whether you're a seasoned trader or just starting, "7 hours a day" is your trusty sidekick, guiding you through the twists and turns of the market with style and precision. Say goodbye to guesswork and hello to clarity in your trading journey!
it is believed that market ranges within these 7-hour windows, and when broken up or down you could expect a momentum price movement.
utilsLibrary "utils"
Provides a set of utility functions for use in strategies or indicators.
colorGreen(opacity)
Parameters:
opacity (int)
colorRed(opacity)
Parameters:
opacity (int)
colorTeal(opacity)
Parameters:
opacity (int)
colorBlue(opacity)
Parameters:
opacity (int)
colorOrange(opacity)
Parameters:
opacity (int)
colorPurple(opacity)
Parameters:
opacity (int)
colorPink(opacity)
Parameters:
opacity (int)
colorYellow(opacity)
Parameters:
opacity (int)
colorWhite(opacity)
Parameters:
opacity (int)
colorBlack(opacity)
Parameters:
opacity (int)
trendChangingUp(emaShort, emaLong)
Signals when the trend is starting to change in a positive direction.
Parameters:
emaShort (float)
emaLong (float)
Returns: bool
trendChangingDown(emaShort, emaLong)
Signals when the trend is starting to change in a negative direction.
Parameters:
emaShort (float)
emaLong (float)
Returns: bool
percentChange(start, end)
Returns the percent change between a start number and end number. A positive change returns a positive value and vice versa.
Parameters:
start (float)
end (float)
Returns: float
percentOf(percent, n)
Returns the number that's the percentage of the provided value.
Parameters:
percent (float) : Use 0.2 for 20 percent, 0.35 for 35 percent, etc.
n (float) : The number to calculate the percentage of.
Returns: float
targetPriceByPercent(percent, n)
Parameters:
percent (float)
n (float)
hasNegativeSlope(start, end)
Parameters:
start (float)
end (float)
timeinrange(resolution, session, timezone)
Returns true when the current time is within a given session window. Note, the time is calculated in the "America/New_York" timezone.
Parameters:
resolution (simple string) : The time interval to use to start/end the background color. Use "1" for the coloring the background up to the minute.
session (simple string) : The session string to use to identify the time window. Example: "0930-1600:23456" means normal market hours on weekdays.
timezone (simple string)
Returns: series bool
barsSinceLastEntry()
Returns the number of bars since the last entry order.
Returns: series int
barsSinceLastExit()
Returns the number of bars since the last exit order.
Returns: series int
calcSlope(ln, lookback)
Calculates the slope of the provided line based on its x,y coordinates in the previous bar to the current bar.
Parameters:
ln (float)
lookback (int)
Returns: series float
openPL()
Returns slope of the line given the start and end x,y coordinates.
Returns: series float
hasConsecutiveNegativeCandles(lookbackInput)
Returns true if the number of consecutive red candles matches the provided count.
Parameters:
lookbackInput (int) : The amount of bars to look back to check for consecutive negative bars. Default = 1.
Returns: series bool
stdevPercent(stdev, price)
Returns the standard deviation as a percentage of price.
Parameters:
stdev (float) : The standard deviation value
price (float) : The current price of the target ticker.
Returns: series float
Momentum Ghost Machine [ChartPrime]Momentum Ghost Machine (ChartPrime) is designed to be the next generation in momentum/rate of change analysis. This indicator utilizes the properties of one of our favorite filters to create a more accurate and stable momentum oscillator by using a high quality filtered delayed signal to do the momentum comparison.
Traditional momentum/roc uses the raw price data to compare current price to previous price to generate a directional oscillator. This leaves the oscillator prone to false readings and noisy outputs that leave traders unsure of the real likelihood of a future movement. One way to mitigate this issue would be to use some sort of moving average. Unfortunately, this can only go so far because simple moving average algorithms result in a poor reconstruction of the actual shape of the underlying signal.
The windowed sinc low pass filter is a linear phase filter, meaning that it doesn't change the shape or size of the original signal when applied. This results in a faithful reconstruction of the original signal, but without the "high frequency noise". Just like any filter, the process of applying it requires that we have "future" samples resulting in a time delay for real time applications. Fortunately this is a great thing in the context of a momentum oscillator because we need some representation of past price data to compare the current price data to. By using an ideal low pass filter to generate this delayed signal we can super charge the momentum oscillator and fix the majority of issues its predecessors had.
This indicator has a few extra features that other momentum/roc indicators dont have. One major yet simple improvement is the inclusion of a moving average to help gauge the rate of change of this indicator. Since we included a moving average, we thought it would only be appropriate to add a histogram to help visualize the relationship between the signal and its average. To go further with this we have also included linear extrapolation to further help you predict the momentum and direction of this oscillator. Included with this extrapolation we have also added the histogram in the extrapolation to further enhance its visual interpretation. Finally, the inclusion of a candle coloring feature really drives how the utility of the Momentum Machine .
There are three distinct options when using the candle coloring feature: Direct, MA, and Both. With direct the candles will be colored based on the indicators direction and polarity. When it is above zero and moving up, it displays a green color. When it is above zero and moving down it will display a light green color. Conversely, when the indicator is below zero and moving down it displays a red color, and when it it moving up and below zero it will display a light red color. MA coloring will color the candles just like a MACD. If the signal is above its MA and moving up it will display a green color, and when it is above its MA and moving down it will display a light green color.
When the signal is below its MA and moving down it will display a red color, and when its below its ma and moving up it will display a light red color. Both combines the two into a single color scheme providing you with the best of both worlds. If the indicator is above zero it will display the MA colors with a slight twist. When the indicator is moving down and is below its MA it will display a lighter color than before, and when it is below zero and is above its MA it will display a darker color color.
Length of 50 with a smoothing of 100
Length of 50 with a smoothing of 25
By default, the indicator is set to a momentum length of 50, with a post smoothing of 2. We have chosen the longer period for the momentum length to highlight the performance of this indicator compared to its ancestors. A major point to consider with this indicator is that you can only achieve so much smoothing for a chosen delay. This is because more data is required to produce a smoother signal at a specified length. Once you have selected your desired momentum length you can then select your desired momentum smoothing . This is made possible by the use of the windowed sinc low pass algorithm because it includes a frequency cutoff argument. This means that you can have as little or as much smoothing as you please without impacting the period of the indicator. In the provided examples above this paragraph is a visual representation of what is going on under the hood of this indicator. The blue line is the filtered signal being compared to the current closing price. As you can see, the filtered signal is very smooth and accurately represents the underlying price action without noise.
We hope that users can find the same utility as we did in this indicator and that it levels up your analysis utilizing the momentum oscillator or rate of change.
Enjoy
aproxLibrary "aprox"
It's a library of the aproximations of a price or Series float it uses Fourier transform and
Euler's Theoreum for Homogenus White noice operations. Calling functions without source value it automatically take close as the default source value.
Copy this indicator to see how each approximations interact between each other.
import Celje_2300/aprox/1 as aprox
//@version=5
indicator("Close Price with Aproximations", shorttitle="Close and Aproximations", overlay=false)
// Sample input data (replace this with your own data)
inputData = close
// Plot Close Price
plot(inputData, color=color.blue, title="Close Price")
dtf32_result = aprox.DTF32()
plot(dtf32_result, color=color.green, title="DTF32 Aproximation")
fft_result = aprox.FFT()
plot(fft_result, color=color.red, title="DTF32 Aproximation")
wavelet_result = aprox.Wavelet()
plot(wavelet_result, color=color.orange, title="Wavelet Aproximation")
wavelet_std_result = aprox.Wavelet_std()
plot(wavelet_std_result, color=color.yellow, title="Wavelet_std Aproximation")
DFT3(xval, _dir)
Parameters:
xval (float)
_dir (int)
//@version=5
import Celje_2300/aprox/1 as aprox
indicator("Example - DFT3", shorttitle="DFT3 Example", overlay=true)
// Sample input data (replace this with your own data)
inputData = close
// Apply DFT3
result = aprox.DFT3(inputData, 2)
// Plot the result
plot(result, color=color.blue, title="DFT3 Result")
DFT2(xval, _dir)
Parameters:
xval (float)
_dir (int)
//@version=5
import Celje_2300/aprox/1 as aprox
indicator("Example - DFT2", shorttitle="DFT2 Example", overlay=true)
// Sample input data (replace this with your own data)
inputData = close
// Apply DFT2
result = aprox.DFT2(inputData, inputData, 1)
// Plot the result
plot(result, color=color.green, title="DFT2 Result")
//@version=5
import Celje_2300/aprox/1 as aprox
indicator("Example - DFT2", shorttitle="DFT2 Example", overlay=true)
// Sample input data (replace this with your own data)
inputData = close
// Apply DFT2
result = aprox.DFT2(inputData, 1)
// Plot the result
plot(result, color=color.green, title="DFT2 Result")
FFT(xval)
FFT: Fast Fourier Transform
Parameters:
xval (float)
Returns: Aproxiated source value
//@version=5
import Celje_2300/aprox/1 as aprox
indicator("Example - FFT", shorttitle="FFT Example", overlay=true)
// Sample input data (replace this with your own data)
inputData = close
// Apply FFT
result = aprox.FFT(inputData)
// Plot the result
plot(result, color=color.red, title="FFT Result")
DTF32(xval)
DTF32: Combined Discrete Fourier Transforms
Parameters:
xval (float)
Returns: Aproxiated source value
//@version=5
import Celje_2300/aprox/1 as aprox
indicator("Example - DTF32", shorttitle="DTF32 Example", overlay=true)
// Sample input data (replace this with your own data)
inputData = close
// Apply DTF32
result = aprox.DTF32(inputData)
// Plot the result
plot(result, color=color.purple, title="DTF32 Result")
whitenoise(indic_, _devided, minEmaLength, maxEmaLength, src)
whitenoise: Ehler's Universal Oscillator with White Noise, without extra aproximated src
Parameters:
indic_ (float)
_devided (int)
minEmaLength (int)
maxEmaLength (int)
src (float)
Returns: Smoothed indicator value
//@version=5
import Celje_2300/aprox/1 as aprox
indicator("Example - whitenoise", shorttitle="whitenoise Example", overlay=true)
// Sample input data (replace this with your own data)
inputData = close
// Apply whitenoise
result = aprox.whitenoise(aprox.FFT(inputData))
// Plot the result
plot(result, color=color.orange, title="whitenoise Result")
whitenoise(indic_, dft1, _devided, minEmaLength, maxEmaLength, src)
whitenoise: Ehler's Universal Oscillator with White Noise and DFT1
Parameters:
indic_ (float)
dft1 (float)
_devided (int)
minEmaLength (int)
maxEmaLength (int)
src (float)
Returns: Smoothed indicator value
//@version=5
import Celje_2300/aprox/1 as aprox
indicator("Example - whitenoise with DFT1", shorttitle="whitenoise-DFT1 Example", overlay=true)
// Sample input data (replace this with your own data)
inputData = close
// Apply whitenoise with DFT1
result = aprox.whitenoise(inputData, aprox.DFT1(inputData))
// Plot the result
plot(result, color=color.yellow, title="whitenoise-DFT1 Result")
smooth(dft1, indic__, _devided, minEmaLength, maxEmaLength, src)
smooth: Smoothing source value with help of indicator series and aproximated source value
Parameters:
dft1 (float)
indic__ (float)
_devided (int)
minEmaLength (int)
maxEmaLength (int)
src (float)
Returns: Smoothed source series
//@version=5
import Celje_2300/aprox/1 as aprox
indicator("Example - smooth", shorttitle="smooth Example", overlay=true)
// Sample input data (replace this with your own data)
inputData = close
// Apply smooth
result = aprox.smooth(inputData, aprox.FFT(inputData))
// Plot the result
plot(result, color=color.gray, title="smooth Result")
smooth(indic__, _devided, minEmaLength, maxEmaLength, src)
smooth: Smoothing source value with help of indicator series
Parameters:
indic__ (float)
_devided (int)
minEmaLength (int)
maxEmaLength (int)
src (float)
Returns: Smoothed source series
//@version=5
import Celje_2300/aprox/1 as aprox
indicator("Example - smooth without DFT1", shorttitle="smooth-NoDFT1 Example", overlay=true)
// Sample input data (replace this with your own data)
inputData = close
// Apply smooth without DFT1
result = aprox.smooth(aprox.FFT(inputData))
// Plot the result
plot(result, color=color.teal, title="smooth-NoDFT1 Result")
vzo_ema(src, len)
vzo_ema: Volume Zone Oscillator with EMA smoothing
Parameters:
src (float)
len (simple int)
Returns: VZO value
vzo_sma(src, len)
vzo_sma: Volume Zone Oscillator with SMA smoothing
Parameters:
src (float)
len (int)
Returns: VZO value
vzo_wma(src, len)
vzo_wma: Volume Zone Oscillator with WMA smoothing
Parameters:
src (float)
len (int)
Returns: VZO value
alma2(series, windowsize, offset, sigma)
alma2: Arnaud Legoux Moving Average 2 accepts sigma as series float
Parameters:
series (float)
windowsize (int)
offset (float)
sigma (float)
Returns: ALMA value
Wavelet(src, len, offset, sigma)
Wavelet: Wavelet Transform
Parameters:
src (float)
len (int)
offset (simple float)
sigma (simple float)
Returns: Wavelet-transformed series
//@version=5
import Celje_2300/aprox/1 as aprox
indicator("Example - Wavelet", shorttitle="Wavelet Example", overlay=true)
// Sample input data (replace this with your own data)
inputData = close
// Apply Wavelet
result = aprox.Wavelet(inputData)
// Plot the result
plot(result, color=color.blue, title="Wavelet Result")
Wavelet_std(src, len, offset, mag)
Wavelet_std: Wavelet Transform with Standard Deviation
Parameters:
src (float)
len (int)
offset (float)
mag (int)
Returns: Wavelet-transformed series
//@version=5
import Celje_2300/aprox/1 as aprox
indicator("Example - Wavelet_std", shorttitle="Wavelet_std Example", overlay=true)
// Sample input data (replace this with your own data)
inputData = close
// Apply Wavelet_std
result = aprox.Wavelet_std(inputData)
// Plot the result
plot(result, color=color.green, title="Wavelet_std Result")
[S] Rolling TrendlineThe Rolling Linear Regression Trendline is a sophisticated technical analysis tool designed to offer traders a dynamic view of market trends over a selectable period. This indicator employs linear regression to calculate and plot a trendline that best fits the closing prices within a specified window, either defined by a number of bars or a set period in days, independent of the chart's timeframe.
Key Features:
Dynamic Window Selection: Users can choose the calculation window based on a fixed number of bars or days, providing flexibility to adapt to different trading strategies and timeframes. For the 'days' option, the indicator calculates the equivalent number of bars based on the chart's timeframe, ensuring relevance across various market conditions and trading sessions.
Linear Regression Analysis: At its core, the indicator uses linear regression to identify the trend direction by calculating the slope and intercept of the trendline. This method offers a statistical approach to trend analysis, highlighting potential uptrends or downtrends based on the positioning and direction of the trendline.
Customizable Period: Traders can input their desired period (N), allowing for tailored analysis. Whether it's short-term movements or longer-term trends, the indicator can adjust to focus on specific time horizons, enhancing its utility across different trading styles and objectives.
Applications:
Trend Identification: By plotting a trendline that mathematically fits the closing prices over the chosen period, traders can quickly identify the prevailing market trend, aiding in bullish or bearish decision-making.
Support and Resistance: The trendline can also serve as a dynamic level of support or resistance, offering potential entry or exit points based on the price's interaction with the trendline.
Strategic Planning: With the ability to adjust the calculation window, traders can align the indicator with their trading strategy, whether focusing on intraday movements or broader swings.
Using this indicator with other parameters can widen you view of the market and help identifying trends
MA+ ProjectionThe "MA+ Projection" indicator is designed to visualize the potential future direction of a moving average, taking into account the impact of historical data loss. It is primarily aimed at providing a practical perspective on how moving averages could evolve as older data points are no longer considered.
Key Features:
Supported Moving Averages: SMA, EMA, WMA, VWMA, and VAWMA (Volume Adjusted WMA).
Flexible Time Span Settings: Customize the moving average length in bars, minutes, or days.
Adjustable Projection Scope: Set a percentage of the measurement to project forward.
Projection 'Cone': Show/hide the deviation and control the multiple.
Use Last Source Value: An option to add the latest known value to the moving window instead of only letting the window shrink. (Enabled by default.)
How It Works:
Given the specified parameters, it takes the selected moving average type (a known formula like SMA, EMA, or WMA), and projects the future data points by continuing to move the data 'window' forward without adding any more data. By default, it extends the average by assuming the price hasn't changed after the last bar. Alternatively, the projection can be the result of shrinking the window as it moves forward without adding any new data points.
Note:
This tool is for visual projection, not prediction. Its purpose is to aid in the analysis of potential future trends based on historical data, not to provide definitive market forecasts.
savitzkyGolay, KAMA, HPOverview
This trading indicator integrates three distinct analytical tools: the Savitzky-Golay Filter, Kaufman Adaptive Moving Average (KAMA), and Hodrick-Prescott (HP) Filter. It is designed to provide a comprehensive analysis of market trends and potential trading signals.
Components
Hodrick-Prescott (HP) Filter
Purpose: Smooths out the price data to identify the underlying trend.
Parameters: Lambda: Controls the smoothness. Range: 50 to 1600.
Impact of Parameters:
Increasing Lambda: This makes the trend line more responsive to short-term market fluctuations, suitable for short-term analysis. A higher Lambda value decreases the degree of smoothing, making the trend line follow recent market movements more closely.
Decreasing Lambda: A lower Lambda value makes the trend line smoother and less responsive to short-term market fluctuations, ideal for longer-term trend analysis. Decreasing Lambda increases the degree of smoothing, thereby filtering out minor market movements and focusing more on the long-term trend.
Kaufman Adaptive Moving Average (KAMA):
Purpose: An adaptive moving average that adjusts to price volatility.
Parameters: Length, Fast Length, Slow Length: Define the sensitivity and adaptiveness of KAMA.
Impact of Parameters:
Adjusting Length affects the base period for efficiency ratio, altering the overall sensitivity.
Fast Length and Slow Length control the speed of KAMA’s adaptation. A smaller Fast Length makes KAMA more sensitive to price changes, while a larger Slow Length makes it less sensitive.
Savitzky-Golay Filter:
Purpose: Smooths the price data using polynomial regression.
Parameters: Window Size: Determines the size of the moving window (7, 9, 11, 15, 21).
Impact of Parameters:
A larger Window Size results in a smoother curve, which is more effective for identifying long-term trends but can delay reaction to recent market changes.
A smaller Window Size makes the curve more responsive to short-term price movements, suitable for short-term trading strategies.
General Impact of Parameters
Adjusting these parameters can significantly alter the signals generated by the indicator. Users should fine-tune these settings based on their trading style, the characteristics of the traded asset, and market conditions to optimize the indicator's performance.
Signal Logic
Buy Signal: The trend from the HP filter is below both the KAMA and the Savitzky-Golay SMA, and none of these indicators are flat.
Sell Signal: The trend from the HP filter is above both the KAMA and the Savitzky-Golay SMA, and none of these indicators are flat.
Usage
Due to the combination of smoothing algorithms and adaptability, this indicator is highly effective at identifying emerging trends for both initiating long and short positions.
IMPORTANT : Although the code and user settings incorporate measures to limit false signals due to lateral (sideways) movement, they do not completely eliminate such occurrences. Users are strongly advised to avoid signals that emerge during simultaneous lateral movements of all three indicators.
Despite the indicator's success in historical data analysis using its signals alone, it is highly recommended to use this code in combination with other indicators, patterns, and zones. This is particularly important for determining exit points from positions, which can significantly enhance trading results.
Limitations and Recommendations
The indicator has shown excellent performance on the weekly time frame (TF) with the following settings:
Savitzky-Golay (SG): 11
Hodrick-Prescott (HP): 100
Kaufman Adaptive Moving Average (KAMA): 20, 2, 30
For the monthly TF, the recommended settings are:
SG: 15
HP: 100
KAMA: 30, 2, 35
Note: The monthly TF is quite variable. With these settings, there may be fewer signals, but they tend to be more relevant for long-term investors. Based on a sample of 40 different stocks from various countries and sectors, most exhibited an average trade return in the thousands of percent.
It's important to note that while these settings have been successful in past performance, market conditions vary and past performance is not indicative of future results. Users are encouraged to experiment with these settings and adjust them according to their individual needs and market analysis.
As this is my first developed trading indicator, I am very open to and appreciative of any suggestions or comments. Your feedback is invaluable in helping me refine and improve this tool. Please feel free to share your experiences, insights, or any recommendations you may have.
[MAD] Position starter & calculatorThe tool you're using is a financial instrument trading planner and analyzer.
Here is how to use it:
Trade Planning: You can plan your trade entries and exits, calculating potential profits, losses, and their ratio (P/L ratio).
You can define up to five target closing prices with varying volumes, which can be individually activated or deactivated (volume set to 0%).
Risk Management: There's a stop-loss function to calculate and limit potential losses.
Additionally, it includes a liquidation pre-calculation for adjustable leverages and position maintenance(subject to exchange variation).
Customization: You can customize the tool's appearance with five adjustable color schemes, light and dark.
-----------------
Initiation: This tool functions as an indicator.
To start, add it as an indicator.
Once added, you can close the indicator window.
Now wait, till you'll see a blue box at the bottom of the input window.
Parameter Input:
Enter your parameters (SL, box left, box right, TP1, TP2, TP3, TP4, TP5) in the direction of the desired trade.
Click from top to bottom for a short trade or bottom to top for a long trade.
Adjustment: If you want to move the box in the future, adjust the times in the indicator settings directly as click input is not yet platform-supported.
This tool functions as a ruler and doesn't offer alerts (for now).
Here is another examples of how to set up a Position-calculation but here for a short:
Have fun trading
Delta Volume Channels [LucF]█ OVERVIEW
This indicator displays on-chart visuals aimed at making the most of delta volume information. It can color bars and display two channels: one for delta volume, another calculated from the price levels of bars where delta volume divergences occur. Markers and alerts can also be configured using key conditions, and filtered in many different ways. The indicator caters to traders who prefer chart visuals over raw values. It will work on historical bars and in real time, using intrabar analysis to calculate delta volume in both conditions.
█ CONCEPTS
Delta Volume
The volume delta concept divides a bar's volume in "up" and "down" volumes. The delta is calculated by subtracting down volume from up volume. Many calculation techniques exist to isolate up and down volume within a bar. The simplest techniques use the polarity of interbar price changes to assign their volume to up or down slots, e.g., On Balance Volume or the Klinger Oscillator . Others such as Chaikin Money Flow use assumptions based on a bar's OHLC values. The most precise calculation method uses tick data and assigns the volume of each tick to the up or down slot depending on whether the transaction occurs at the bid or ask price. While this technique is ideal, it requires huge amounts of data on historical bars, which usually limits the historical depth of charts and the number of symbols for which tick data is available.
This indicator uses intrabar analysis to achieve a compromise between the simplest and most precise methods of calculating volume delta. In the context where historical tick data is not yet available on TradingView, intrabar analysis is the most precise technique to calculate volume delta on historical bars on our charts. TradingView's Volume Profile built-in indicators use it, as do the CVD - Cumulative Volume Delta Candles and CVD - Cumulative Volume Delta (Chart) indicators published from the TradingView account . My Volume Delta Columns Pro indicator also uses intrabar analysis. Other volume delta indicators such as my Realtime 5D Profile use realtime chart updates to achieve more precise volume delta calculations. Indicators of that type cannot be used on historical bars however; they only work in real time.
This is the logic I use to assign intrabar volume to up or down slots:
• If the intrabar's open and close values are different, their relative position is used.
• If the intrabar's open and close values are the same, the difference between the intrabar's close and the previous intrabar's close is used.
• As a last resort, when there is no movement during an intrabar and it closes at the same price as the previous intrabar, the last known polarity is used.
Once all intrabars making up a chart bar have been analyzed and the up or down property of each intrabar's volume determined, the up volumes are added and the down volumes subtracted. The resulting value is volume delta for that chart bar, which can be used as an estimate of the buying/selling pressure on an instrument.
Delta Volume Percent (DV%)
This value is the proportion that delta volume represents of the total intrabar volume in the chart bar. Note that on some symbols/timeframes, the total intrabar volume may differ from the chart's volume for a bar, but that will not affect our calculations since we use the total intrabar volume.
Delta Volume Channel
The DV channel is the space between two moving averages: the reference line and a DV%-weighted version of that reference. The reference line is a moving average of a type, source and length which you select. The DV%-weighted line uses the same settings, but it averages the DV%-weighted price source.
The weight applied to the source of the reference line is calculated from two values, which are multiplied: DV% and the relative size of the bar's volume in relation to previous bars. The effect of this is that DV% values on bars with higher total volume will carry greater weight than those with lesser volume.
The DV channel can be in one of four states, each having its corresponding color:
• Bull (teal): The DV%-weighted line is above the reference line.
• Strong bull (lime): The bull condition is fulfilled and the bar's close is above the reference line and both the reference and the DV%-weighted lines are rising.
• Bear (maroon): The DV%-weighted line is below the reference line.
• Strong bear (pink): The bear condition is fulfilled and the bar's close is below the reference line and both the reference and the DV%-weighted lines are falling.
Divergences
In the context of this indicator, a divergence is any bar where the slope of the reference line does not match that of the DV%-weighted line. No directional bias is assigned to divergences when they occur.
Divergence Channel
The divergence channel is the space between two levels (by default, the bar's low and high ) saved when divergences occur. When price has breached a channel and a new divergence occurs, a new channel is created. Until that new channel is breached, bars where additional divergences occur will expand the channel's levels if the bar's price points are outside the channel.
Prices breaches of the divergence channel will change its state. Divergence channels can be in one of five different states:
• Bull (teal): Price has breached the channel to the upside.
• Strong bull (lime): The bull condition is fulfilled and the DV channel is in the strong bull state.
• Bear (maroon): Price has breached the channel to the downside.
• Strong bear (pink): The bear condition is fulfilled and the DV channel is in the strong bear state.
• Neutral (gray): The channel has not been breached.
█ HOW TO USE THE INDICATOR
Load the indicator on an active chart (see here if you don't know how).
The default configuration displays:
• The DV channel, without the reference or DV%-weighted lines.
• The Divergence channel, without its level lines.
• Bar colors using the state of the DV channel.
The default settings use an Arnaud-Legoux moving average on the close and a length of 20 bars. The DV%-weighted version of it uses a combination of DV% and relative volume to calculate the ultimate weight applied to the reference. The DV%-weighted line is capped to 5 standard deviations of the reference. The lower timeframe used to access intrabars automatically adjusts to the chart's timeframe and achieves optimal balance between the number of intrabars inspected in each chart bar, and the number of chart bars covered by the script's calculations.
The Divergence channel's levels are determined using the high and low of the bars where divergences occur. Breaches of the channel require a bar's low to move above the top of the channel, and the bar's high to move below the channel's bottom.
No markers appear on the chart; if you want to create alerts from this script, you will need first to define the conditions that will trigger the markers, then create the alert, which will trigger on those same conditions.
To learn more about how to use this indicator, you must understand the concepts it uses and the information it displays, which requires reading this description. There are no videos to explain it.
█ FEATURES
The script's inputs are divided in four sections: "DV channel", "Divergence channel", "Other Visuals" and "Marker/Alert Conditions". The first setting is the selection method used to determine the intrabar precision, i.e., how many lower timeframe bars (intrabars) are examined in each chart bar. The more intrabars you analyze, the more precise the calculation of DV% results will be, but the less chart coverage can be covered by the script's calculations.
DV Channel
Here, you control the visibility and colors of the reference line, its weighted version, and the DV channel between them.
You also specify what type of moving average you want to use as a reference line, its source and length. This acts as the DV channel's baseline. The DV%-weighted line is also a moving average of the same type and length as the reference line, except that it will be calculated from the DV%-weighted source used in the reference line. By default, the DV%-weighted line is capped to five standard deviations of the reference line. You can change that value here. This section is also where you can disable the relative volume component of the weight.
Divergence Channel
This is where you control the appearance of the divergence channel and the key price values used in determining the channel's levels and breaching conditions. These choices have an impact on the behavior of the channel. More generous level prices like the default low and high selection will produce more conservative channels, as will the default choice for breach prices.
In this section, you can also enable a mode where an attempt is made to estimate the channel's bias before price breaches the channel. When it is enabled, successive increases/decreases of the channel's top and bottom levels are counted as new divergences occur. When one count is greater than the other, a bull/bear bias is inferred from it.
Other Visuals
You specify here:
• The method used to color chart bars, if you choose to do so.
• The display of a mark appearing above or below bars when a divergence occurs.
• If you want raw values to appear in tooltips when you hover above chart bars. The default setting does not display them, which makes the script faster.
• If you want to display an information box which by default appears in the lower left of the chart.
It shows which lower timeframe is used for intrabars, and the average number of intrabars per chart bar.
Marker/Alert Conditions
Here, you specify the conditions that will trigger up or down markers. The trigger conditions can include a combination of state transitions of the DV and the divergence channels. The triggering conditions can be filtered using a variety of conditions.
Configuring the marker conditions is necessary before creating an alert from this script, as the alert will use the marker conditions to trigger.
Markers only appear on bar closes, so they will not repaint. Keep in mind, when looking at markers on historical bars, that they are positioned on the bar when it closes — NOT when it opens.
Raw values
The raw values calculated by this script can be inspected using a tooltip and the Data Window. The tooltip is visible when you hover over the top of chart bars. It will display on the last 500 bars of the chart, and shows the values of DV, DV%, the combined weight, and the intermediary values used to calculate them.
█ INTERPRETATION
The aim of the DV channel is to provide a visual representation of the buying/selling pressure calculated using delta volume. The simplest characteristic of the channel is its bull/bear state. One can then distinguish between its bull and strong bull states, as transitions from strong bull to bull states will generally happen when buyers are losing steam. While one should not infer a reversal from such transitions, they can be a good place to tighten stops. Only time will tell if a reversal will occur. One or more divergences will often occur before reversals.
The nature of the divergence channel's design makes it particularly adept at identifying consolidation areas if its settings are kept on the conservative side. A gray divergence channel should usually be considered a no-trade zone. More adventurous traders can use the DV channel to orient their trade entries if they accept the risk of trading in a neutral divergence channel, which by definition will not have been breached by price.
If your charts are already busy with other stuff you want to hold on to, you could consider using only the chart bar coloring component of this indicator:
At its simplest, one way to use this indicator would be to look for overlaps of the strong bull/bear colors in both the DV channel and a divergence channel, as these identify points where price is breaching the divergence channel when buy/sell pressure is consistent with the direction of the breach. I have highlighted all those points in the chart below. Not all of them would have produced profitable trades, but nothing is perfect in the markets. Also, keep in mind that the circles identify the visual you would be looking for — not the trade's entry level.
█ LIMITATIONS
• The script will not work on symbols where no volume is available. An error will appear when that is the case.
• Because a maximum of 100K intrabars can be analyzed by a script, a compromise is necessary between the number of intrabars analyzed per chart bar
and chart coverage. The more intrabars you analyze per chart bar, the less coverage you will obtain.
The setting of the "Intrabar precision" field in the "DV channel" section of the script's inputs
is where you control how the lower timeframe is calculated from the chart's timeframe.
█ NOTES
Volume Quality
If you use volume, it's important to understand its nature and quality, as it varies with sectors and instruments. My Volume X-ray indicator is one way you can appraise the quality of an instrument's intraday volume.
For Pine Script™ Coders
• This script uses the new overload of the fill() function which now makes it possible to do vertical gradients in Pine. I use it for both channels displayed by this script.
• I use the new arguments for plot() 's `display` parameter to control where the script plots some of its values,
namely those I only want to appear in the script's status line and in the Data Window.
• I wrote my script using the revised recommendations in the Style Guide from the Pine v5 User Manual.
█ THANKS
To PineCoders . I have used their lower_tf library in this script, to manage the calculation of the LTF and intrabar stats, and their Time library to convert a timeframe in seconds to a printable form for its display in the Information box.
To TradingView's Pine Script™ team. Their innovations and improvements, big and small, constantly expand the boundaries of the language. What this script does would not have been possible just a few months back.
And finally, thanks to all the users of my scripts who take the time to comment on my publications and suggest improvements. I do not reply to all but I do read your comments and do my best to implement your suggestions with the limited time that I have.
Adaptive Oscillator constructor [lastguru]Adaptive Oscillators use the same principle as Adaptive Moving Averages. This is an experiment to separate length generation from oscillators, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. Note that all Oscillators here are normalized to -1..1 range. This indicator is based on my previously published public libraries and also serve as a usage demonstration for them. I will try to expand the collection (suggestions are welcome), however it is not meant as an encyclopaedic resource , so you are encouraged to experiment yourself: by looking on the source code of this indicator, I am sure you will see how trivial it is to use the provided libraries and expand them with your own ideas and combinations. I give no recommendation on what settings to use, but if you find some useful setting, combination or application ideas (or bugs in my code), I would be happy to read about them in the comments section.
The indicator works in three stages: Prefiltering, Length Adaptation and Oscillators.
Prefiltering is a fast smoothing to get rid of high-frequency (2, 3 or 4 bar) noise.
Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
Chande (Price) - based on Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length
Chande (Volume) - a variant of Chande's algorithm, where volume is used instead of price
VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
Deviation Scaling - based on DSSS by John F. Ehlers
Median Average - based on Median Average Adaptive Filter by John F. Ehlers
Fractal Adaptation - based on FRAMA by John F. Ehlers
MESA MAMA Alpha - based on MESA Adaptive Moving Average by John F. Ehlers
MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers , but unlike Alpha calculation, this adaptation estimates cycle period
Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers
Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).
The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers . I do not know, which combination works best, so you can experiment.
Chande's Adaptations also have 3 additional parameters: SD Length (lookback length of Standard deviation), Smooth (smoothing length of Standard deviation) and Power ( exponent of the length adaptation - lower is smaller variation). These are internal tweaks for the calculation.
Oscillators section offer you a choice of Oscillator algorithms:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
CMO - Chande Momentum Oscillator
RSI - Relative Strength Index
Volume-scaled RSI - my own version of RSI. It scales price movements by the proportion of RMS of volume
Momentum RSI - RSI of price momentum
Rocket RSI - inspired by RocketRSI by John F. Ehlers (not an exact implementation)
MFI - Money Flow Index
LRSI - Laguerre RSI by John F. Ehlers
LRSI with Fractal Energy - a combo oscillator that uses Fractal Energy to tune LRSI gamma
Fractal Energy - Fractal Energy or Choppiness Index by E. W. Dreiss
Efficiency ratio - based on Kaufman Adaptive Moving Average calculation
DMI - Directional Movement Index (only ADX is drawn)
Fast DMI - same as DMI, but without secondary smoothing
If no Adaptation is selected (None option), you can set Length directly. If an Adaptation is selected, then Cycle multiplier can be set.
Before an Oscillator, a High Pass filter may be executed to remove cyclic components longer than the provided Highpass Length (no High Pass filter, if Highpass Length = 0). Both before and after the Oscillator a Moving Average can be applied. The following Moving Averages are included: SMA, RMA, EMA, HMA , VWMA, 2-pole Super Smoother, 3-pole Super Smoother, Filt11, Triangle Window, Hamming Window, Hann Window, Lowpass, DSSS. For more details on these Moving Averages, you can check my other Adaptive Constructor indicator:
The Oscillator output may be renormalized and postprocessed with the following Normalization algorithms:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
Inverse Fisher Transform - Inverse Fisher Transform
Noise Elimination Technology - a simplified Kendall correlation algorithm "Noise Elimination Technology" by John F. Ehlers
Except for Inverse Fisher Transform, all Normalization algorithms can have Length parameter. If it is not specified (set to 0), then the calculated Oscillator length is used.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Runners & Laggers (scanner)Firstly, seems to me this may only work with crypto but I know nothing about the other sectors so i could be wrong. I was trying to think up a good way to find moving coins(other than by volume bc theres holes in the results when using it this way). Thought this was an interesting concept so decided to publish it as I've seen no others like it (though i did not extensively search for it. We need to start with a little Tradingview(TV) common knowledge. When there is no update of trades/volume in a candle TV does not print the candle. So when looking at (let's say) a 1 second chart, if the coin being observed by the user has no update from a trade in the time of that 1 sec candle it is skipped over. This means that a coin with a ton of volume might fill an entire 60 seconds with 60 candles and conversely with a low volume coin there could be as little as 0 1-second candles. BUT even for normally low volume coins, when a pump is beginning with the coin it could literally go from 0 1-second candles within a minute to 60 1-second candles within the next minute. ***NOTE: This DOES NOT show ANY information if the coin is going up or down but rather that a LOT more trading volume is occurring than normal.*** What this script does is scans (via request.security feature) up to 40 coins at a time and counts how many candles are printed within a user set timespan calculated in minute. 1 candle print per incremented timeframe that the chart is on. ie. if the chart is a 1 min chart it counts how many 1 min candles are printed. So, (as is in the captured image for the script) if you wanted to count how many 5 second candles are printed for each coin in 1 min then you would have to put the charts timeframe on 5sec and the setting titled 'Window of TIME(in minutes) to count bars' as 1.0 (which bc it's in minutes 1.0m = 60sec and bc 60s / 5s = 12 there would be 12 possible values that each coin can be at depending on how many bars are counted within that 1min/60sec. *** I will update to show an image of what I'm talking about here. Now, the exchange I'm scanning here is Kucoin's Margin Coins. There are 170 something coins total but I removed a few i didn't care for to make it a round 40 coins per set (there being 4 sets of 40 coins total=160 coins being scanned). To scan all 4 sets the indicator must be added 4 times to the chart and a different 'set' selected for each iteration of the script on the chart. Free users can only scan 3 at the most. All others can scan all 4 sets. In the script you can change the exchange and coins as necessary. If there done so and there are not 40 coins total just put '' '' in the extra coins spots that are not filled and the script will skip over these blankly filled spots. The suffix (traded pair) for the tickerID on all Kucoin's Margin Coin's is USDT so that's what i have inputted in the main function on line 46 (will need to be changed if that differs from the coins you want to scan. Next in the line of settings is 'Window of TIME(in minutes) to count bars' which has already been discussed. Following that is the setting "Table Shows" which the results are all in a table and the table will present the coins that have either "Passed" or "Failed" depending on which you choose. The next setting determines what passes or fails. If there are 12 possible rows for the coins to be in (as described above) then this setting is the "Pass/Fail Cutoff" level. So if you want to show all the coins that are in rows 11 and 12 (as in the image at top) then 11 should be selected here. At this point you will see all the coins that have a lot of volume in them. Finding coin names in the table that are usually not with a ton of volume will present your present movers. NOTE: coins like BTC and ETH will almost always be in these levels so it does not indicate anything different from the norm of these coins. Last setting is the ability to show the table on the main window or not. Hope you enjoy and find use in it. BTW this screener format is the same as the others I have published. If you like, check those out too. If you find difficulty using then refer to those as well as they have additional info in them on how to use the scanner and its format. Lastly, in the script is the ability to print the plots and labels but I commented them out bc its really just a jumbled mess. In the commented out sections there is a Random Color Function (provided by @hewhomustnotbenamed which was developed on the basis of Function-HSL-color by @RicardoSantos. All right, peace brothers....and sisters.
**** Also, I see how the "levels" could be confusing so I will put them into a % format soon (probably not today) so that the "Pass/Fail Cutoff" can be in % format so that if "passed" is chosen and 50% is chosen (in the new setting that will be changed) then it'll show you all the coins that have more than 50% of the bars printed within the time window chosen. Goodluck in all your trading adventures. ChasinAlts out.
JohnEhlersFourierTransformLibrary "JohnEhlersFourierTransform"
Fourier Transform for Traders By John Ehlers, slightly modified to allow to inspect other than the 8-50 frequency spectrum.
reference:
www.mesasoftware.com
high_pass_filter(source) Detrended version of the data by High Pass Filtering with a 40 Period cutoff
Parameters:
source : float, data source.
Returns: float.
transformed_dft(source, start_frequency, end_frequency) DFT by John Elhers.
Parameters:
source : float, data source.
start_frequency : int, lower bound of the frequency window, must be a positive number >= 0, window must be less than or 30.
end_frequency : int, upper bound of the frequency window, must be a positive number >= 0, window must be less than or 30.
Returns: tuple with float, float array.
db_to_rgb(db, transparency) converts the frequency decibels to rgb.
Parameters:
db : float, decibels value.
transparency : float, transparency value.
Returns: color.
BjCandlePatternsLibrary "BjCandlePatterns"
Patterns is a Japanese candlestick pattern recognition Library for developers. Functions here within detect viable setups in a variety of popular patterns. Please note some patterns are without filters such as comparisons to average candle sizing, or trend detection to allow the author more freedom.
doji(dojiSize, dojiWickSize) Detects "Doji" candle patterns
Parameters:
dojiSize : (float) The relationship of body to candle size (ie. body is 5% of total candle size). Default is 5.0 (5%)
dojiWickSize : (float) Maximum wick size comparative to the opposite wick. (eg. 2 = bottom wick must be less than or equal to 2x the top wick). Default is 2
Returns: (series bool) True when pattern detected
dLab(showLabel, labelColor, textColor) Produces "Doji" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
bullEngulf(maxRejectWick, mustEngulfWick) Detects "Bullish Engulfing" candle patterns
Parameters:
maxRejectWick : (float) Maximum rejection wick size.
The maximum wick size as a percentge of body size allowable for a top wick on the resolution candle of the pattern. 0.0 disables the filter.
eg. 50 allows a top wick half the size of the body. Default is 0% (Disables wick detection).
mustEngulfWick : (bool) input to only detect setups that close above the high prior effectively engulfing the candle in its entirety. Default is false
Returns: (series bool) True when pattern detected
bewLab(showLabel, labelColor, textColor) Produces "Bullish Engulfing" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
bearEngulf(maxRejectWick, mustEngulfWick) Detects "Bearish Engulfing" candle patterns
Parameters:
maxRejectWick : (float) Maximum rejection wick size.
The maximum wick size as a percentge of body size allowable for a bottom wick on the resolution candle of the pattern. 0.0 disables the filter.
eg. 50 allows a botom wick half the size of the body. Default is 0% (Disables wick detection).
mustEngulfWick : (bool) Input to only detect setups that close below the low prior effectively engulfing the candle in its entirety. Default is false
Returns: (series bool) True when pattern detected
bebLab(showLabel, labelColor, textColor) Produces "Bearish Engulfing" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
hammer(ratio, shadowPercent) Detects "Hammer" candle patterns
Parameters:
ratio : (float) The relationship of body to candle size (ie. body is 33% of total candle size). Default is 33%.
shadowPercent : (float) The maximum allowable top wick size as a percentage of body size. Default is 5%.
Returns: (series bool) True when pattern detected
hLab(showLabel, labelColor, textColor) Produces "Hammer" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
star(ratio, shadowPercent) Detects "Star" candle patterns
Parameters:
ratio : (float) The relationship of body to candle size (ie. body is 33% of total candle size). Default is 33%.
shadowPercent : (float) The maximum allowable bottom wick size as a percentage of body size. Default is 5%.
Returns: (series bool) True when pattern detected
ssLab(showLabel, labelColor, textColor) Produces "Star" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
dragonflyDoji() Detects "Dragonfly Doji" candle patterns
Returns: (series bool) True when pattern detected
ddLab(showLabel, labelColor) Produces "Dragonfly Doji" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
Returns: (series label) A label visible at the chart level intended for the title pattern
gravestoneDoji() Detects "Gravestone Doji" candle patterns
Returns: (series bool) True when pattern detected
gdLab(showLabel, labelColor, textColor) Produces "Gravestone Doji" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
tweezerBottom(closeUpperHalf) Detects "Tweezer Bottom" candle patterns
Parameters:
closeUpperHalf : (bool) input to only detect setups that close above the mid-point of the candle prior increasing its bullish tendancy. Default is false
Returns: (series bool) True when pattern detected
tbLab(showLabel, labelColor, textColor) Produces "Tweezer Bottom" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
tweezerTop(closeLowerHalf) Detects "TweezerTop" candle patterns
Parameters:
closeLowerHalf : (bool) input to only detect setups that close below the mid-point of the candle prior increasing its bearish tendancy. Default is false
Returns: (series bool) True when pattern detected
ttLab(showLabel, labelColor, textColor) Produces "TweezerTop" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
spinningTopBull(wickSize) Detects "Bullish Spinning Top" candle patterns
Parameters:
wickSize : (float) input to adjust detection of the size of the top wick/ bottom wick as a percent of total candle size. Default is 34%, which ensures the wicks are both larger than the body.
Returns: (series bool) True when pattern detected
stwLab(showLabel, labelColor, textColor) Produces "Bullish Spinning Top" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
spinningTopBear(wickSize) Detects "Bearish Spinning Top" candle patterns
Parameters:
wickSize : (float) input to adjust detection of the size of the top wick/ bottom wick as a percent of total candle size. Default is 34%, which ensures the wicks are both larger than the body.
Returns: (series bool) True when pattern detected
stbLab(showLabel, labelColor, textColor) Produces "Bearish Spinning Top" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
spinningTop(wickSize) Detects "Spinning Top" candle patterns
Parameters:
wickSize : (float) input to adjust detection of the size of the top wick/ bottom wick as a percent of total candle size. Default is 34%, which ensures the wicks are both larger than the body.
Returns: (series bool) True when pattern detected
stLab(showLabel, labelColor, textColor) Produces "Spinning Top" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
morningStar() Detects "Bullish Morning Star" candle patterns
Returns: (series bool) True when pattern detected
msLab(showLabel, labelColor, textColor) Produces "Bullish Morning Star" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
eveningStar() Detects "Bearish Evening Star" candle patterns
Returns: (series bool) True when pattern detected
esLab(showLabel, labelColor, textColor) Produces "Bearish Evening Star" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
haramiBull() Detects "Bullish Harami" candle patterns
Returns: (series bool) True when pattern detected
hwLab(showLabel, labelColor, textColor) Produces "Bullish Harami" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
haramiBear() Detects "Bearish Harami" candle patterns
Returns: (series bool) True when pattern detected
hbLab(showLabel, labelColor, textColor) Produces "Bearish Harami" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
haramiBullCross() Detects "Bullish Harami Cross" candle patterns
Returns: (series bool) True when pattern detected
hcwLab(showLabel, labelColor, textColor) Produces "Bullish Harami Cross" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
haramiBearCross() Detects "Bearish Harami Cross" candle patterns
Returns: (series bool) True when pattern detected
hcbLab(showLabel, labelColor) Produces "Bearish Harami Cross" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
Returns: (series label) A label visible at the chart level intended for the title pattern
marubullzu() Detects "Bullish Marubozu" candle patterns
Returns: (series bool) True when pattern detected
mwLab(showLabel, labelColor, textColor) Produces "Bullish Marubozu" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
marubearzu() Detects "Bearish Marubozu" candle patterns
Returns: (series bool) True when pattern detected
mbLab(showLabel, labelColor, textColor) Produces "Bearish Marubozu" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
abandonedBull() Detects "Bullish Abandoned Baby" candle patterns
Returns: (series bool) True when pattern detected
abwLab(showLabel, labelColor, textColor) Produces "Bullish Abandoned Baby" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
abandonedBear() Detects "Bearish Abandoned Baby" candle patterns
Returns: (series bool) True when pattern detected
abbLab(showLabel, labelColor, textColor) Produces "Bearish Abandoned Baby" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
piercing() Detects "Piercing" candle patterns
Returns: (series bool) True when pattern detected
pLab(showLabel, labelColor, textColor) Produces "Piercing" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
darkCloudCover() Detects "Dark Cloud Cover" candle patterns
Returns: (series bool) True when pattern detected
dccLab(showLabel, labelColor, textColor) Produces "Dark Cloud Cover" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
tasukiBull() Detects "Upside Tasuki Gap" candle patterns
Returns: (series bool) True when pattern detected
utgLab(showLabel, labelColor, textColor) Produces "Upside Tasuki Gap" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
tasukiBear() Detects "Downside Tasuki Gap" candle patterns
Returns: (series bool) True when pattern detected
dtgLab(showLabel, labelColor, textColor) Produces "Downside Tasuki Gap" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
risingThree() Detects "Rising Three Methods" candle patterns
Returns: (series bool) True when pattern detected
rtmLab(showLabel, labelColor, textColor) Produces "Rising Three Methods" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
fallingThree() Detects "Falling Three Methods" candle patterns
Returns: (series bool) True when pattern detected
ftmLab(showLabel, labelColor, textColor) Produces "Falling Three Methods" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
risingWindow() Detects "Rising Window" candle patterns
Returns: (series bool) True when pattern detected
rwLab(showLabel, labelColor, textColor) Produces "Rising Window" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
fallingWindow() Detects "Falling Window" candle patterns
Returns: (series bool) True when pattern detected
fwLab(showLabel, labelColor, textColor) Produces "Falling Window" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
kickingBull() Detects "Bullish Kicking" candle patterns
Returns: (series bool) True when pattern detected
kwLab(showLabel, labelColor, textColor) Produces "Bullish Kicking" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
kickingBear() Detects "Bearish Kicking" candle patterns
Returns: (series bool) True when pattern detected
kbLab(showLabel, labelColor, textColor) Produces "Bearish Kicking" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
lls(ratio) Detects "Long Lower Shadow" candle patterns
Parameters:
ratio : (float) A relationship of the lower wick to the overall candle size expressed as a percent. Default is 75%
Returns: (series bool) True when pattern detected
llsLab(showLabel, labelColor, textColor) Produces "Long Lower Shadow" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
lus(ratio) Detects "Long Upper Shadow" candle patterns
Parameters:
ratio : (float) A relationship of the upper wick to the overall candle size expressed as a percent. Default is 75%
Returns: (series bool) True when pattern detected
lusLab(showLabel, labelColor, textColor) Produces "Long Upper Shadow" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
bullNeck() Detects "Bullish On Neck" candle patterns
Returns: (series bool) True when pattern detected
nwLab(showLabel, labelColor, textColor) Produces "Bullish On Neck" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
bearNeck() Detects "Bearish On Neck" candle patterns
Returns: (series bool) True when pattern detected
nbLab(showLabel, labelColor, textColor) Produces "Bearish On Neck" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
soldiers(wickSize) Detects "Three White Soldiers" candle patterns
Parameters:
wickSize : (float) Maximum allowable top wick size throughout pattern expressed as a percent of total candle height. Default is 5%
Returns: (series bool) True when pattern detected
wsLab(showLabel, labelColor, textColor) Produces "Three White Soldiers" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
crows(wickSize) Detects "Three Black Crows" candle patterns
Parameters:
wickSize : (float) Maximum allowable bottom wick size throughout pattern expressed as a percent of total candle height. Default is 5%
Returns: (series bool) True when pattern detected
bcLab(showLabel, labelColor, textColor) Produces "Three Black Crows" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
triStarBull() Detects "Bullish Tri-Star" candle patterns
Returns: (series bool) True when pattern detected
tswLab(showLabel, labelColor, textColor) Produces "Bullish Tri-Star" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
triStarBear() Detects "Bearish Tri-Star" candle patterns
Returns: (series bool) True when pattern detected
tsbLab(showLabel, labelColor, textColor) Produces "Bearish Tri-Star" identifier label
Parameters:
showLabel : (bool) Shows label when input is true. Default is false
labelColor : (series color) Color of the label border and arrow
textColor : (series color) Text color
Returns: (series label) A label visible at the chart level intended for the title pattern
wrap(cond, barsBack, borderColor, bgcolor) Produces a box wrapping the highs and lows over the look back.
Parameters:
cond : (series bool) Condition under which to draw the box.
barsBack : (series int) the number of bars back to begin drawing the box.
borderColor : (series color) Color of the four borders. Optional. The default is color.gray.
bgcolor : (series color) Background color of the box. Optional. The default is color.gray.
Returns: (series box) A box who's top and bottom are above and below the highest and lowest points over the lookback
topWick() returns the top wick size of the current candle
Returns: (series float) A value equivelent to the distance from the top of the candle body to its high
bottomWick() returns the bottom wick size of the current candle
Returns: (series float) A value equivelent to the distance from the bottom of the candle body to its low
body() returns the body size of the current candle
Returns: (series float) A value equivelent to the distance between the top and the bottom of the candle body
highestBody() returns the highest body of the current candle
Returns: (series float) A value equivelent to the highest body, whether it is the open or the close
lowestBody() returns the lowest body of the current candle
Returns: (series float) A value equivelent to the highest body, whether it is the open or the close
barRange() returns the height of the current candle
Returns: (series float) A value equivelent to the distance between the high and the low of the candle
bodyPct() returns the body size as a percent
Returns: (series float) A value equivelent to the percentage of body size to the overall candle size
midBody() returns the price of the mid-point of the candle body
Returns: (series float) A value equivelent to the center point of the distance bewteen the body low and the body high
bodyupGap() returns true if there is a gap up between the real body of the current candle in relation to the candle prior
Returns: (series bool) true if there is a gap up and no overlap in the real bodies of the current candle and the preceding candle
bodydwnGap() returns true if there is a gap down between the real body of the current candle in relation to the candle prior
Returns: (series bool) true if there is a gap down and no overlap in the real bodies of the current candle and the preceding candle
gapUp() returns true if there is a gap down between the real body of the current candle in relation to the candle prior
Returns: (series bool) true if there is a gap down and no overlap in the real bodies of the current candle and the preceding candle
gapDwn() returns true if there is a gap down between the real body of the current candle in relation to the candle prior
Returns: (series bool) true if there is a gap down and no overlap in the real bodies of the current candle and the preceding candle
dojiBody() returns true if the candle body is a doji
Returns: (series bool) true if the candle body is a doji. Defined by a body that is 5% of total candle size
Summary ChecklistThis works on daily charts
Summarizes some data as shown.
Projects volume for eod.
Sometimes it opens on new window. You have to move it to the price window.
vol_rangesThis script shows three measures of volatility:
historical (hv): realized volatility of the recent past
median (mv): a long run average of realized volatility
implied (iv): a user-defined volatility
Historical and median volatility are based on the EWMA, rather than standard deviation, method of calculating volatility. Since Tradingview's built in ema function uses a window, the "window" parameter determines how much historical data is used to calculate these volatility measures. E.g. 30 on a daily chart means the previous 30 days.
The plots above and below historical candles show past projections based on these measures. The "periods to expiration" dictates how far the projection extends. At 30 periods to expiration (default), the plot will indicate the one standard deviation range from 30 periods ago. This is calculated by multiplying the volatility measure by the square root of time. For example, if the historical volatility (hv) was 20% and the window is 30, then the plot is drawn over: close * 1.2 * sqrt(30/252).
At the most recent candle, this same calculation is simply drawn as a line projecting into the future.
This script is intended to be used with a particular options contract in mind. For example, if the option expires in 15 days and has an implied volatility of 25%, choose 15 for the window and 25 for the implied volatility options. The ranges drawn will reflect the two standard deviation range both in the future (lines) and at any point in the past (plots) for HV (blue), MV (red), and IV (grey).
FOTSI - Open sourceI WOULD LIKE TO SPECIFY TWO THINGS:
- The indicator was absolutely not designed by me, I do not take any credit and much less I want them, I am just making this fantastic indicator open source and accessible to all
- The script code was not recycled from other indicators, but was created from 0 following the theory behind it to the letter, thus avoiding copyright infringement
- Advices and improvements are accepted, as having very little programming experience in Pine Script I consider this work still rough and slow
WHAT IS THE FOTSI?
The FOTSI is an oscillator that measures the relative strength of the individual currencies that make up the 28 major Forex exchanges.
By identifying the currencies that are in the overbought (+50) and oversold (-50) areas, it is possible to anticipate the correction of a currency pair following a strong trend.
THE THEORY BEHIND
1) At the base of everything is the 1-period momentum (close-open) of the single currency pairs that contain a certain currency. For example, the momentum of the USD currency is composed of all the exchange rates that contain the US dollar inside it: mom_usd = - mom_eurusd - mom_gbpusd + mom_usdchf + mom_usdjpy - mom_audusd + mom_usdcad - mom_nzdusd. Where the base currency is in second position, the momentum is subtracted instead of adding it.
2) The IST formula is applied to the momentum of the individual currencies obtained. In this way we get an oscillator that oscillates between 0 and its overbought and oversold areas. The area between +25 and -25 is an area in which we can consider the movements of individual currencies to be neutral.
3) The TSI is nothing more than a double smoothing on the momentum of individual currencies. This particularity makes the indicator very reactive, minimizing the delays of the trend reversal.
HOW TO USE
1) A currency is identified that is in the overbought (+50) or oversold (-50) area. Example GBP = 50
2) The second currency is identified as the one most opposite to the first. Example USD = -25
3) The currency pair consisting of the two currencies opens. So GBP / USD
4) Considering that GBP is oversold, we anticipate its future devaluation. So in this case we are short on GBP / SUD. Otherwise if GBP had been oversold (-50) we expect its future valuation and therefore we enter long.
5) It is used on the H1, H4 and D1 timeframes
6) Closing conditions: the position on the 50-period exponential moving average is split / the position at target on the 100-period exponential moving average is closed
7) Stoploss: it is recommended not to use it, if you want to use it it is equivalent to 5 times the ATR on the reference timeframe
8) Position sizing: go very slow! Being a counter-trend strategy, it is very risky to position yourself heavily. Use common sense in everything!
9) To insert the alerts that warn you of an overbought and oversold condition, it is necessary to enter the signals called "Overbought Signal" and "Oversold Signal" for each chart used, in the specific Trading View window. like me using multiple charts in the same window.
I hope you enjoy my work. For any questions write in the comments.
Thanks <3
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TENGO A PRECISARE DUE COSE:
- L'indicatore non è stato assolutamente ideato da me, non mi assumo nessun merito e tanto meno li voglio, io sto solo rendendo questo fantastico indicatore open source ed accessibile a tutti
- Il codice dello script non è stato riciclato da altri indicatori, ma è stato creato da 0 seguendo alla lettere la teoria che sta alla sua base, evitando così di violare il copyright
- Si accettano consigli e migliorie, visto che avendo pochissima esperienza di programmazione in Pine Script considero questo lavoro ancora grezzo e lento
COS'È IL FOTSI?
Il FOTSI è un oscillatore che misura la forza relativa delle singole valute che compongono i 28 cambi major del Forex.
Individuando le valute che si trovano nelle aree di ipercomprato (+50) ed ipervenduto (-50) , è possibile anticipare la correzione di una coppia valutaria al seguito di un forte trend.
LA TEORIA ALLA BASE
1) Alla base di tutto c'è il momentum ad 1 periodo (close-open) delle singole coppie valutarie che contengono una determinata valuta. Ad esempio il momentum della valuta USD è composto da tutti i cambi che contengono il dollaro americano al suo interno: mom_usd = - mom_eurusd - mom_gbpusd + mom_usdchf + mom_usdjpy - mom_audusd + mom_usdcad - mom_nzdusd . Ove la valuta base si trova in seconda posizione si sottrae il momentum al posto che sommarlo.
2) Si applica la formula del TSI ai momentum delle singole valute ottenute. In questo modo otteniamo un oscillatore che oscilla tra lo 0 e le sue aree di ipercomprato ed ipervenduto. L'area compresa tra +25 e -25 è un area in cui possiamo considerare neutri i movimenti delle singole valute.
3) Il TSI non è altro che un doppio smoothing sul momentum delle singole valute. Questa particolarità rende l'indicatore molto reattivo, minimizzando i ritardi dell'inversione del trend.
COME SI USA
1) Si individua una valuta che si trova nell'area di ipercomprato (+50) o ipervenduto (-50) . Esempio GBP = 50
2) Si individua come seconda valuta quella più opposta alla prima. Esempio USD = -25
3) Si apre la coppia di valuta composta dalle due valute. Quindi GBP/USD
4) Considerando che GBP è in fase di ipervenduto prevediamo una sua futura svalutazione. Quindi in questo caso entriamo short su GBP/SUD. Diversamente se GBP fosse stato in fase di ipervenduto (-50) ci aspettiamo una sua futura valutazione e quindi entriamo long.
5) Si usa sui timeframe H1, H4 e D1
6) Condizioni di chiusura: si smezza la posizione sulla media mobile esponenziale a 50 periodi / si chiude la posizione a target sulla media mobile esponenziale a 100 periodi
7) Stoploss: è consigliato non usarlo, nel caso lo si voglia utilizzare esso equivale a 5 volte l'ATR sul timeframe di riferimento
8) Position sizing: andateci molto piano! Essendo una strategia contro trend è molto rischioso posizionarsi in modo pesante. Usate il buonsenso in tutto!
9) Per inserire gli allert che ti avvertono di una condizione di ipercomprato ed ipervenduto, è necessario inserire dall'apposita finestra di Trading View i segnali denominati "Segnale di ipercomprato" ed "Segnale di ipervenduto" per ogni grafico che si usa, nel caso come me che si utilizzano più grafici nella stessa finestra.
Spero che possiate apprezzare il mio lavoro. Per qualsiasi domanda scrivete nei commenti.
Grazie<3
