MTF Williams Vix Market Bottoms [CryptoSea]MTF Williams Vix Fix Indicator is a dynamic tool tailored for traders looking to capture market extremes with high precision. This multi-timeframe indicator leverages the concept of the Williams Vix Fix to spot potential reversals before they occur.
Key Features
Multi-Timeframe Analysis: Provides simultaneous visibility across multiple timeframes, enabling traders to assess market conditions comprehensively from a single chart.
Advanced Volatility Detection: Utilizes a modified Vix Fix formula to highlight extreme price deviations, which often precede significant market reversals.
Customizable Settings: Offers extensive input options to tweak the lookback periods, percentile thresholds, and visibility settings, aligning with various trading strategies.
Visual Band Indicators: Features upper bands and range highs that signal potential overbought and oversold conditions, enhancing trading decision-making.
Below, you can see how the indicator performs across different timeframes, providing valuable insights into market behavior.
How it Works
Vix Fix Calculation: Determines the worst-case 'panic' sell-offs in price as a percentage of the high, capturing the emotional extremes of the market.
Statistical Bands: Employs Bollinger bands over the Vix Fix values to define normal and extreme volatility conditions.
Color-Coded Indicators: Uses color differentiation to instantly highlight when readings surpass critical upper band or range high thresholds, signaling key trading opportunities.
For instance, in the analysis provided below, notice how the indicator flags significant market moves, allowing traders to anticipate potential entry or exit points.
Application
Risk Management: Aids in identifying extreme market conditions where prices may revert, helping in effective position sizing and risk management.
Strategic Planning: Enhances strategic trading plans by identifying not only when but also where market extremes may occur, considering multiple timeframes.
Customization: Adapts seamlessly to different market environments with adjustable settings for volatility thresholds and visual display preferences.
The MTF Williams Vix Fix Indicator by is an essential tool for traders aiming to leverage market volatility for optimal entry and exit, ensuring they are well-equipped to handle market extremes with confidence.
Cerca negli script per "signal"
Normalised T3 Oscillator [BackQuant]Normalised T3 Oscillator
The Normalised T3 Oscillator is an technical indicator designed to provide traders with a refined measure of market momentum by normalizing the T3 Moving Average. This tool was developed to enhance trading decisions by smoothing price data and reducing market noise, allowing for clearer trend recognition and potential signal generation. Below is a detailed breakdown of the Normalised T3 Oscillator, its methodology, and its application in trading scenarios.
1. Conceptual Foundation and Definition of T3
The T3 Moving Average, originally proposed by Tim Tillson, is renowned for its smoothness and responsiveness, achieved through a combination of multiple Exponential Moving Averages and a volume factor. The Normalised T3 Oscillator extends this concept by normalizing these values to oscillate around a central zero line, which aids in highlighting overbought and oversold conditions.
2. Normalization Process
Normalization in this context refers to the adjustment of the T3 values to ensure that the oscillator provides a standard range of output. This is accomplished by calculating the lowest and highest values of the T3 over a user-defined period and scaling the output between -0.5 to +0.5. This process not only aids in standardizing the indicator across different securities and time frames but also enhances comparative analysis.
3. Integration of the Oscillator and Moving Average
A unique feature of the Normalised T3 Oscillator is the inclusion of a secondary smoothing mechanism via a moving average of the oscillator itself, selectable from various types such as SMA, EMA, and more. This moving average acts as a signal line, providing potential buy or sell triggers when the oscillator crosses this line, thus offering dual layers of analysis—momentum and trend confirmation.
4. Visualization and User Interaction
The indicator is designed with user interaction in mind, featuring customizable parameters such as the length of the T3, normalization period, and type of moving average used for signals. Additionally, the oscillator is plotted with a color-coded scheme that visually represents different strength levels of the market conditions, enhancing readability and quick decision-making.
5. Practical Applications and Strategy Integration
Traders can leverage the Normalised T3 Oscillator in various trading strategies, including trend following, counter-trend plays, and as a component of a broader trading system. It is particularly useful in identifying turning points in the market or confirming ongoing trends. The clear visualization and customizable nature of the oscillator facilitate its adaptation to different trading styles and market environments.
6. Advanced Features and Customization
Further enhancing its utility, the indicator includes options such as painting candles according to the trend, showing static levels for quick reference, and alerts for crossover and crossunder events, which can be integrated into automated trading systems. These features allow for a high degree of personalization, enabling traders to mold the tool according to their specific trading preferences and risk management requirements.
7. Theoretical Justification and Empirical Usage
The use of the T3 smoothing mechanism combined with normalization is theoretically sound, aiming to reduce lag and false signals often associated with traditional moving averages. The practical effectiveness of the Normalised T3 Oscillator should be validated through rigorous backtesting and adjustment of parameters to match historical market conditions and volatility.
8. Conclusion and Utility in Market Analysis
Overall, the Normalised T3 Oscillator by BackQuant stands as a sophisticated tool for market analysis, providing traders with a dynamic and adaptable approach to gauging market momentum. Its development is rooted in the understanding of technical nuances and the demand for a more stable, responsive, and customizable trading indicator.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Uptrick: RSI MA Buying/Selling signalsIndicator Purpose:
This indicator, titled "Uptrick: RSI MA Buying/Selling signals" or "UpRSIMA," aims to provide buying and selling signals based on the Moving Average (MA) of the Relative Strength Index (RSI).
It plots the RSI MA line and highlights whether the RSI MA value is above or below 50, indicating potential bullish or bearish signals, respectively.
RSI Calculation:
The script calculates the RSI using a user-defined length parameter (default is 14) and a specified source (typically the closing price).
It then computes the MA of the RSI using the Recursive Moving Average (RMA) function applied to the RSI values.
Color Representation:
The color of the RSI MA line is determined based on whether it's above or below the neutral level of 50.
If the RSI MA is above 50, indicating potential bullish signals, the color is set to green; otherwise, it's set to red for potential bearish signals.
Plotting:
The RSI MA line is plotted on the chart with the specified color based on its value relative to 50.
Additionally, a horizontal line is drawn at y = 50 to visually represent the neutral level.
Histogram bars are also added to visually represent the difference between the RSI MA and the neutral level, with green bars indicating bullish signals and red bars indicating bearish signals.
User Interface:
The indicator is designed to be used as an overlay on price charts, allowing traders to easily visualize potential buying and selling signals based on RSI MA crossovers and levels relative to 50.
Overall, the "Uptrick: RSI MA Buying/Selling signals" indicator offers traders insights into potential trend reversals or continuations based on the moving average of the Relative Strength Index, aiding them in making informed trading decisions.
Cumulative Delta Volume WaveIntroducing an Enhanced Version of the CDV by LonesomeTheBlue
For the original version and description check this link:
What Makes This Version Different than the original?
This enhanced version of the CDV indicator incorporates advanced signal processing techniques to bring new depth to market analysis.
Standard Deviation Bands and EMAs: These additions to the CDV offer a visual representation of significant market movements—highlighting major pumps and dumps, as well as identifying potential support and resistance levels.
Color-Coded Insights: The standard deviation bands utilize color coding based on signal processing principles. This feature becomes increasingly useful the more you zoom out, making it easier to observe and interpret market waves.
Market Maker Activity: By examining fluctuations within the standard deviation bands, traders can gauge when Market Makers are actively maneuvering to establish their long and short positions, often at the expense of retail traders.
EMA Support and Resistance: The embedded Exponential Moving Averages (EMAs) serve as dynamic support and resistance levels. Analyzing these can help traders determine the continuing strength of a market move, whether bullish or bearish.
Visual Guide to the Basics
For a clearer understanding of what this enhanced indicator can show, please refer to the image below:
And in addition to all the above one can detect relevant W and M structures way easier with this indicator ;)
On Balance Volume WaveIntroducing an Enhanced Version of the Classic OBV Indicator
The On-Balance Volume (OBV) indicator is a well-known tool among traders, celebrated for its ability to track momentum by using volume flow to predict changes in stock price. For an overview of the original OBV indicator, please visit: www.tradingview.com .
What Makes This Version Different?
This enhanced version of the OBV indicator incorporates advanced signal processing techniques to bring new depth to market analysis. Here's what sets it apart:
Standard Deviation Bands and EMAs: These additions to the OBV offer a visual representation of significant market movements—highlighting major pumps and dumps, as well as identifying potential support and resistance levels.
Color-Coded Insights: The standard deviation bands utilize color coding based on signal processing principles. This feature becomes increasingly useful the more you zoom out, making it easier to observe and interpret market waves.
Market Maker Activity: By examining fluctuations within the standard deviation bands, traders can gauge when Market Makers are actively maneuvering to establish their long and short positions, often at the expense of retail traders.
EMA Support and Resistance: The embedded Exponential Moving Averages (EMAs) serve as dynamic support and resistance levels. Analyzing these can help traders determine the continuing strength of a market move, whether bullish or bearish.
Visual Guide to the Basics
For a clearer understanding of what this enhanced indicator can show, please refer to the image below:
And in addition to all the above one can detect relevant W and M structures way easier with this indicator ;)
Advanced Buy and Sell SignalsThis script for TradingView is designed for technical traders seeking a more comprehensive and discerning market analysis. The script combines buy and sell signals from multiple popular technical indicators, providing a holistic view that can be useful for short to medium-term trading strategies. It incorporates the following features:
EMA Trend Cloud:
Two Exponential Moving Averages (EMAs) are calculated: a fast EMA and a slow EMA.
A "cloud" is formed on the chart, changing color as the EMAs cross, indicating potential trend shifts.
Additional Indicators:
RSI (Relative Strength Index): Used to identify overbought or oversold conditions.
Stochastic Oscillator: Helps determine the strength or weakness of the price.
OBV (On-Balance Volume) with EMA: Combines volume and price to show how volume might be influencing price direction.
Combined Buy and Sell Signals:
Buy and sell signals are generated based on a combination of the following criteria:
Crossings of the EMAs (indicative of trend changes).
Conditions of the RSI (identifying potential market extremes).
Crossings of the Stochastic Oscillator (indicating momentum).
Crossings of the OBV with its EMA (assessing the influence of volume on price movement).
Buy signals are indicated by green triangles below the price bars, while sell signals are indicated by red triangles above the price bars.
Alerts:
The script also includes alert conditions to notify the user when potential buy or sell signals are detected.
Application:
This script is suitable for traders who utilize technical analysis and seek to confirm their trading decisions with multiple sources of information. It is particularly useful in volatile markets, where the combination of different indicators can provide more reliable insights.
Note:
It is important to remember that no script or indicator can guarantee success in trading, and one should always consider risk and conduct thorough analysis before making trading decisions.
This script is most effective when used in conjunction with fundamental analysis and a solid understanding of the market.
OKX Signal BOT - Strategy Scanner & Orderer
Hello traders,
With the OKX Signal BOT - Strategy Scanner & Orderer, you can now design your own strategy, scan over 20 cryptocurrencies, and send orders for futures trades on the OKX exchange.
How to Use:
🌐 First, log into your account on the OKX exchange and create a signal bot.
📝 While creating the signal bot, note down the webhook URL and signal token variables given to you; they'll be needed later.
🔍 Select the trading pairs that the bot will work on.
📈 Add this indicator to your chart.
⚙️ Adjust the values of the indicators you will use in your strategy.
📊 Set your entry conditions and indicator setups according to your preference.
🚀 Decide which condition will generate a LONG signal and which will generate a SHORT signal.
🔗 Then, link these conditions with either an AND or OR connector.
🛠️ This also serves as a strategy designer.
🆔 Paste the signal token value you got from OKX into the OKX Signal ID section in the indicator.
➕ Add the cryptocurrency pairs you added to the bot on OKX to this design tool as well.
💾 Save and exit.
🚨 Set an alarm and paste the webhook URL link you got from OKX.
Congratulations, you can now send signals from Tradingview to the OKX exchange without needing any other platform.
Warnings:
⚠️ Works only for futures trades.
📈 Make your leverage settings through the exchange.
🛑 It is recommended to set take profit and stop loss through the exchange.
🚫 If too many alarms are triggered, Tradingview may stop your alarms.
💡 Ensure that the coins you add in the symbol section are from the OKX exchange.
🔍 For futures trades, make sure the symbols end with ".P".
🎉 Enjoy using it!
Z Score CANDLE and Exciting candle signal [DJ D]This script paints candles when their zscore reaches above 2 standard deviations in price from the mean. The blue candle represents up candle above 2. Magenta candle below -2. The candles can signal the beginning of a move and also importantly exhaustion.
The script also signals when a candle has volatility above 6. The higher the sensitivity the less frequent it will paint. These are real time paints and signals. You can adjust for higher time frames by adjusting the length of the z score and adjust the sensitivity of the volatility candles.
The yellow candle is a mean candle and can signify consolidation and/or indecision. Drawing a Darvis type box around around mean candles can give you a zone to watch.
These settings are for 1 minute scalping. The volatility sensitivity range between 1- 2 is good for 15, 30, (ie 1.0 or 1.2) and your discretion....
signal_datagramThe purpose of this library is to split and merge an integer into useful pieces of information that can easily handled and plotted.
The basic piece of information is one word. Depending on the underlying numerical system a word can be a bit, octal, digit, nibble, or byte.
The user can define channels. Channels are named groups of words. Multiple words can be combined to increase the value range of a channel.
A datagram is a description of the user-defined channels in an also user-defined numeric system that also contains all runtime information that is necessary to split and merge the integer.
This library simplifies the communication between two scripts by allowing the user to define the same datagram in both scripts.
On the sender's side, the channel values can be merged into one single integer value called signal. This signal can be 'emitted' using the plot function. The other script can use the 'input.source' function to receive that signal.
On the receiver's end based on the same datagram, the signal can be split into several channels. Each channel has the piece of information that the sender script put.
In the example of this library, we use two channels and we have split the integer in half. However, the user can add new channels, change them, and give meaning to them according to the functionality he wants to implement and the type of information he wants to communicate.
Nowadays many 'input.source' calls are allowed to pass information between the scripts, When that is not a price or a floating value, this library is very useful.
The reason is that most of the time, the convention that is used is not clear enough and it is easy to do things the wrong way or break them later on.
With this library validation checks are done during the initialization minimizing the possibility of error due to some misconceptions.
Library "signal_datagram"
Conversion of a datagram type to a signal that can be "send" as a single value from an indicator to a strategy script
method init(this, positions, maxWords)
init - Initialize if the word positons array with an empty array
Namespace types: WordPosArray
Parameters:
this (WordPosArray) : - The word positions array object
positions (int ) : - The array that contains all the positions of the worlds that shape the channel
maxWords (int) : - The maximum words allowed based on the span
Returns: The initialized object
method init(this)
init - Initialize if the channels word positons map with an empty map
Namespace types: ChannelDesc
Parameters:
this (ChannelDesc) : - The channels' descriptor object
Returns: The initialized object
method init(this, numericSystem, channelDesc)
init - Initialize if the datagram
Namespace types: Datagram
Parameters:
this (Datagram) : - The datagram object
numericSystem (simple string) : - The numeric system of the words to be used
channelDesc (ChannelDesc) : - The channels descriptor that contains the positions of the words that each channel consists of
Returns: The initialized object
method add_channel(this, name, positions)
add_channel - Add a new channel descriptopn with its name and its corresponding word positons to the map
Namespace types: ChannelDesc
Parameters:
this (ChannelDesc) : - The channels' descriptor object to update
name (simple string)
positions (int )
Returns: The initialized object
method set_signal(this, value)
set_signal - Set the signal value
Namespace types: Datagram
Parameters:
this (Datagram) : - The datagram object to update
value (int) : - The signal value to set
method get_signal(this)
get_signal - Get the signal value
Namespace types: Datagram
Parameters:
this (Datagram) : - The datagram object to query
Returns: The value of the signal in digits
method set_signal_sign(this, sign)
set_signal_sign - Set the signal sign
Namespace types: Datagram
Parameters:
this (Datagram) : - The datagram object to update
sign (int) : - The negative -1 or positive 1 sign of the underlying value
method get_signal_sign(this)
get_signal_sign - Get the signal sign
Namespace types: Datagram
Parameters:
this (Datagram) : - The datagram object to query
Returns: The sign of the signal value -1 if it is negative and 1 if it is possitive
method get_channel_names(this)
get_channel_names - Get an array of all channel names
Namespace types: Datagram
Parameters:
this (Datagram)
Returns: An array that has all the channel names that are used by the datagram
method set_channel_value(this, channelName, value)
set_channel_value - Set the value of the channel
Namespace types: Datagram
Parameters:
this (Datagram) : - The datagram object to update
channelName (simple string) : - The name of the channel to set the value to. Then name should be as described int the schemas channel descriptor
value (int) : - The channel value to set
method set_all_channels_value(this, value)
set_all_channels_value - Set the value of all the channels
Namespace types: Datagram
Parameters:
this (Datagram) : - The datagram object to update
value (int) : - The channel value to set
method set_all_channels_max_value(this)
set_all_channels_value - Set the value of all the channels
Namespace types: Datagram
Parameters:
this (Datagram) : - The datagram object to update
method get_channel_value(this, channelName)
get_channel_value - Get the value of the channel
Namespace types: Datagram
Parameters:
this (Datagram) : - The datagram object to query
channelName (simple string)
Returns: Digit group of words (bits/octals/digits/nibbles/hexes/bytes) found at the channel accodring to the schema
WordDesc
Fields:
numericSystem (series__string)
span (series__integer)
WordPosArray
Fields:
positions (array__integer)
ChannelDesc
Fields:
map (map__series__string:|WordPosArray|#OBJ)
Schema
Fields:
wordDesc (|WordDesc|#OBJ)
channelDesc (|ChannelDesc|#OBJ)
Signal
Fields:
value (series__integer)
isNegative (series__bool)
words (array__integer)
Datagram
Fields:
schema (|Schema|#OBJ)
signal (|Signal|#OBJ)
3-Signal Directional Trend Strategy for E-MinisThis is a conceptual strategy intended for E-mini S&P 500 futures with hourly bars.
It uses three signals, going long or short when two or more change in the same direction.
First is MACD. A positive oscillator is considered a bullish signal and a falling oscillator is interpreted bearishly.
Next, stochastics are used as an overbought/oversold indicator. Overbought conditions are considered bearish and oversold readings are viewed as bullish.
Third is a custom indicator based on our Moving Average Speed script. It takes the rate of change of the 50-hour simple moving average (SMA), and then smooths it using a 10-period average. This provides a directional signal.
Traders may want to experiment with different settings for moving average speed.
Note: This is intended for use with stock index futures, which have round-the clock price data to populate the data in the indicators. It may not yield good results with stocks or ETFs.
TradeStation has, for decades, advanced the trading industry, providing access to stocks, options, futures and cryptocurrencies. See our Overview for more.
Important Information
TradeStation Securities, Inc., TradeStation Crypto, Inc., and TradeStation Technologies, Inc. are each wholly owned subsidiaries of TradeStation Group, Inc., all operating, and providing products and services, under the TradeStation brand and trademark. TradeStation Crypto, Inc. offers to self-directed investors and traders cryptocurrency brokerage services. It is neither licensed with the SEC or the CFTC nor is it a Member of NFA. When applying for, or purchasing, accounts, subscriptions, products, and services, it is important that you know which company you will be dealing with. Please click here for further important information explaining what this means.
This content is for informational and educational purposes only. This is not a recommendation regarding any investment or investment strategy. Any opinions expressed herein are those of the author and do not represent the views or opinions of TradeStation or any of its affiliates.
Investing involves risks. Past performance, whether actual or indicated by historical tests of strategies, is no guarantee of future performance or success. There is a possibility that you may sustain a loss equal to or greater than your entire investment regardless of which asset class you trade (equities, options, futures, or digital assets); therefore, you should not invest or risk money that you cannot afford to lose. Before trading any asset class, first read the relevant risk disclosure statements on the Important Documents page, found here: www.tradestation.com .
Volume Pressure Based Buy and Sell SignalsThis script uses a volume pressure indicator to generate buy and sell signals. The volume pressure indicator is calculated by taking the sum of the product of volume and price change over a specified period of time, and then dividing that sum by the total volume over the same period. This gives a measure of the amount of buying pressure or selling pressure in the market.
The script then compares the volume pressure indicator to a moving average of the volume pressure indicator. When the volume pressure indicator crosses above the moving average, a buy signal is generated. When the volume pressure indicator crosses below the moving average, a sell signal is generated.
This script is beneficial to traders because it can help them to identify potential trend reversals. When the volume pressure indicator crosses above the moving average, it indicates that there is a growing amount of buying pressure in the market. This could be a sign that the trend is about to reverse from a downtrend to an uptrend. Conversely, when the volume pressure indicator crosses below the moving average, it indicates that there is a growing amount of selling pressure in the market. This could be a sign that the trend is about to reverse from an uptrend to a downtrend.
Price Action (ValueRay)With this indicator, you gain access to up to 5 moving averages from a selection of 15 different types. This flexibility allows you to customize your trading strategy based on your preferences and market conditions. Whether you're a fan of simple moving averages, exponential moving averages, or weighted moving averages, our indicator has got you covered! Additionally, all the MAs are Multi-Time-Frame!
The indicator also provides trading signals. By analyzing market trends and price movements, it generates accurate buy and sell signals, providing you with clear entry and exit points. You can choose between Fast, Mid, and Slow signal speeds.
Trendlines are another crucial aspect of effective trading, and our indicator seamlessly integrates them, helping you visualize the market's direction.
Furthermore, the indicator empowers you with recent highs and lows. By highlighting these key levels, it becomes easier than ever to spot support and resistance areas, aiding you in making well-informed trading choices.
Additionally, you can switch the ADR% (Average Daily Range as a Percentage) on and off. This number instantly provides you with information on how much the stock usually moves per day as a percentage.
Key Features:
Up to 5 Moving Averages, each with its own timeframe.
SMA, EMA, WMA, RMA, Triangular, Volume Weighted, Elastic Volume Weighted, Least Squares, ZLEMA, Hull, Double EMA, Triple EMA, T3, ALMA, KAMA (more to come in future versions).
Recent High and Low Pivot Points acting as support/resistance.
Trendline indicating the current trend.
Buy/Sell Signals (recommended for use as exit points, stop loss, or take profit levels).
Signals can have three different speeds: Fast, Mid, and Slow. You can switch them anytime depending on how quickly or slowly you want to exit a trade.
The predefined colors are best suited for a dark background, and the predefined settings provide a solid starting point that many traders use in their daily work.
Unlock the full potential of your trading strategy with our comprehensive indicator and start making informed trading decisions today!
EMA + ATR Support and Resistance + Take Profit SignalThe 'EMA+ ATR Support Resistance Take Profit signal' indicator is a technical analysis tool designed to help traders identify potential support and resistance levels, using the Exponential Moving Average (EMA) and the Average True Range (ATR) indicators. This indicator not only tracks the EMA and ATR but also plots these levels as support and resistance lines, providing useful insights into potential buy and sell points.
The indicator allows you to set the lengths for both the EMA and ATR, with default values set to 20 and 14, respectively. Moreover, you can specify the multiplier for the ATR in the Support/Resistance (S/R) length setting, which defaults to 2. The line width for the plotted lines can also be adjusted according to your preference.
The EMA line in center is invisible by default but you can change that by going to the setting of the indicator. The support and resistance lines are plotted in green and red, respectively. When the price hits the support or resistance levels, the indicator provides a visual signal with a cross shape below or above the respective bars, in lime and red, respectively. If you do not need the take profit signals you can disable them in the setting.
How to Use:
1. Define the EMA and ATR lengths according to your trading strategy. Higher lengths will provide smoother lines but may also lag the current price action.
2. Set the S/R length to determine the distance of the support and resistance lines from the EMA line. Higher values will place these lines further away from the EMA.
3. Monitor the chart for instances when the price hits the support or resistance levels. This is indicated by a cross shape below (for support hit) or above (for resistance hit) the price bar. These points may be considered as potential take profit points or entry/exit points, depending on your strategy.
4. Use the indicator in conjunction with other tools and indicators to confirm signals and reduce the risk of false signals. So the assumption is you enter a trade using your other indicators but you can rely on this indicator to remind you to take profit if you are long by a red cross of the resistance line and if you are short reminds you by a green cross on the support line.
Disclaimer: This indicator should not be used as the sole determinant for any investment decision. Always conduct thorough research and consider multiple factors before trading.
Typical Price Difference - TPD © with reversal zones and signalsv1.0 NOTE: The maths have been tested only for BTC and weekly time frame.
This is a concept that I came through after long long hours of VWAP trading and scalping.
The idea is pretty simple:
1) Typical Price is calculated by (h+l+c) / 3. If we take this price and adjust it to volume we get the VWAP value. The difference between this value and the close value, i call it " Typical Price Difference - TPD ".
2) We get the Historical Volatility as calculated by TradingView script and we add it up to TPD and divide it by two (average). This is what I call " The Source - TS ".
3) We apply the CCI formula to TS .
4) We calculate the Rate of Change (roc) of the CCI formula.
5) We apply the VIX FIX of Larry Williams (script used is from ChrisMoody - CM_Williams_Vix_Fix Finds Market Bottoms) *brilliant script!!!
How to use it:
a) When the (3) is over the TPD we have a bullish bias (green area). When it's under we have a bearish bias (red area).
b) If the (1) value goes over or under a certain value (CAUTION!!! it varies in different assets or timeframes) we get a Reversal Zone (RZ). Red/Green background.
c) If we are in a RZ and the VIX FIX gives a strong value (look for green bars in histogram) and roc (4) goes in the opposite direction, we get a reversal signal that works for the next week(s).
I applied this to BTC on a weekly time frame and after some corrections, it gives pretty good reversal zones and signals. Especially bottoms. Also look for divergences in the zones/signals.
As I said I have tested and confirmed it only on BTC/weekly. I need more time with the maths and pine to automatically adjust it to other time frames. You can play with it in different assets or time frames to find best settings by hand.
Feel free to share your thoughts or ideas on this.
P.S. I realy realy realy try to remember when or how or why I came up with the idea to combine typical price with historical volatility and CCI. I can't! It doesn't make any sense LOL
Swing BoxesHey, folks!
Sorry for not posting anything for such a long time. Don't have enough ideas and resources to get inspiration, so trying to brainstorm good stuff in my free time from university studies.
But despite my absence more I now have 300+ people subscribed to me! Thanks, guys, for keeping interest for my work, as I still do value each boost on my script, for real :)
So here is new script , enjoy!
Swing Boxes is pretty simple indicator, which plots signals with "boxes", that help you determine price targets.
What is the idea behind?
I wanted to make indicator, that could help me make swing trades with nice accuracy (as all we want, lol), and for signal criteria I decided to use highs and lows of the price . Then I started coding some ideas to see which of them could be worthy. And, actually, Swing Boxes appeared to be good. But the thing is, that I didn't intend to build them, they appeared as an anomaly from my code :)
I started to explore this anomaly (it looked super cool, but was repainting hard) to fix it and I succeeded, now Swng Boxes don't repaint.
The main idea is that when price goes above it's highest value of p-bars back or below it's lowest value p-bars back, then there is a some god probability, that price will continue to follow current direction.
And the things about Swing Boxes is that when there is a good trend movement, the boxes become super small to track price movement and when price breaks out in the counter-trend direction, then you will be able to almost perfectly catch a top or a bottom! But most of the signals won't be so high-quality, so don't think that is this some holy grail to trade swing-trading, because it is not.
Signal logic
Quick hint:
- epsilon(variable e ) = ATR * ATR_Factor . It is used to determine box's sensitivity to price changes.
If previous close is higher than variable, which contains previous HIGHEST value (variable h in the code), then update the this variable by taking up-to-date highest value and add epsilon( e ) to it;
If previous close is lower than variable, which contains previous LOWEST value (variable l in the code), then update the this variable by taking up-to-date lowest value and substract epsilon( e ) from it.
Variables decribed above ( h and l ) are box's top and bottom respectively, so if price cross them, it is logical to update it is value.
Settings and what is what
Swing Box Period - numbers fo bars in the past to find highest and lowest price from. The bigger the input, the bigger the boxes will be;
ATR Period;
ATR Factor - multiplier for ATR, determines sensitivity for price changes. The bigger this input, the more accurate signals will be, but less the probability that the signal will be on the top or a bottom.
Show Boxes? - when chosen, plots box's top and bottom. Used to determine price targets.
Show Baseline? - when chosen, plot's baseline, which midline between box's top and bottom.
How to use?
This indicator plots green and red triangles by default.
- Green triangle --> Buy ;
- Red triangle --> Sell ;
As I've said before, many signals from indicator will probably be garbage, so you need to tune settings for youself, so it could satisfy you .
You can enable showing boxes to see box's top and bottom. Box's bottom --> your entry, top --> your profit target.
If you find a way to sort bad signals, you will be able to trade with super cool RR, because the signal from Swing Boxes appear to be a good one, there is almost 95% probability, that price will not even come close to your stop loss, so you can trade with super small stop-losses! Smaller stop-loss --> smaller risk --> smaller loss --> bigger profit, it is that easy.
Also you can enable baseline to use at as your 1st TP, and box's top/bottom as 2nd TP, closing 25% on TP1 and the rest on TP2 (but that is just mine recommendation, you can use different RM (risk-management), if you want).
Also you can use baseline as your S/R (Support/Resistance) line, test it out on your charts.
And please, hear me out: as all other indicators out here on the TradingView, Swing Boxes ARE NOT meant to be traded in solo! Many bad signal can go in a row, so PLEASE find your way to filter out bad signals with other indicators.
You can see here the example of a garabge-class signal in a row, so be don't be deluded!
I do hope that somebody will suggest and idea to improve this thing, as I personally don't have enough time to think about it because of my university studies, but I will probably try it make this thing better throughout the time.
And that's it for now, folks! If you have any ideas for scripts, strategies or anything else, feel free to DM me or leave a comment, I will check it.
Hope you will find this script useful.
Take your profits!
- Tarasenko Fyodor
Bollinger Bands SignalsDescription:
This indicator works well in trendy markets on long runs and in mean-reverting markets, at almost any timeframe.
That said, higher timeframes are much preferred for their intrinsic ability to cut out noise. The example chart is in 3H TF.
Be mindful, the script shows somewhat erratic jigsaw-like behaviour during consolidation periods when the price
jumps up and down in indecision which way to go. Fortunately, there are scripts out there that detect such periods.
You can choose between 4 Moving Averages, Vidya being the default. Period, Deviation and Bands Width parameters
all of them affect the signal generation.
For the Pine Script coder this script is pretty obvious.
It uses a standard technical analysis indicator - Bollinger Bands - and appends it with a 'width' parameter and
a signal generation procedure.
The signal generation procedure is the heart of this script that keeps the script pumping signals.
The BB width is used as a filter.
You can use this procedure in your own scripts and it will continue generate signals according to your rules.
SignalBuilderSignalBuilder
Utility for building a collection of signal values. Provides a default view for displaying signals.
Simplified API for aggregating signal values.
Flexible for use with indicators and strategies.
See the demo section for an example.
[-_-] Volatility Calibrated ATRDescription:
An indicator based on ATR adjusted for volatility of the market. It uses Heikin Ashi data to find short and long opportunities and displays a dynamic stop loss level. Additionally, it has alerts for when the trend changes (which is an entry signal).
How it works:
It works by dynamically calculating the Period for ATR which depends on current volatility level that is calculated by a function that uses Standard Deviation of price. ATR is then smoothed by Weighted Moving Average and multiplied by ATR Factor, resulting in a plot that changes its colour to red when we're in a downtrend and green when in an uptrend. This plot should be used as a dynamic Stop Loss level. Trend change is determined by price crossing the dynamic Stop Loss level. The squared red and green labels appear when the trend changes, and should be used as Entry signals.
Parameters:
- Source -> data used for calculations
- ATR Factor -> higher values produce less noise and longer trends, lower values give more signals
Digital Kahler Stochastic [Loxx]Digital Kahler Stochastic is a Digital Kahler filtered Stochastic. This modification significantly reduces noise.
What is Digital Kahler?
From Philipp Kahler's article for www.traders-mag.com, August 2008. "A Classic Indicator in a New Suit: Digital Stochastic"
Digital Indicators
Whenever you study the development of trading systems in particular, you will be struck in an extremely unpleasant way by the seemingly unmotivated indentations and changes in direction of each indicator. An experienced trader can recognise many false signals of the indicator on the basis of his solid background; a stupid trading system usually falls into any trap offered by the unclear indicator course. This is what motivated me to improve even further this and other indicators with the help of a relatively simple procedure. The goal of this development is to be able to use this indicator in a trading system with as few additional conditions as possible. Discretionary traders will likewise be happy about this clear course, which is not nerve-racking and makes concentrating on the essential elements of trading possible.
How Is It Done?
The digital stochastic is a child of the original indicator. We owe a debt of gratitude to George Lane for his idea to design an indicator which describes the position of the current price within the high-low range of the historical price movement. My contribution to this indicator is the changed pattern which improves the quality of the signal without generating too long delays in giving signals. The trick used to generate this “digital” behavior of the indicator. It can be used with most oscillators like RSI or CCI .
First of all, the original is looked at. The indicator always moves between 0 and 100. The precise position of the indicator or its course relative to the trigger line are of no interest to me, I would just like to know whether the indicator is quoted below or above the value 50. This is tantamount to the question of whether the market is just trading above or below the middle of the high-low range of the past few days. If the market trades in the upper half of its high-low range, then the digital stochastic is given the value 1; if the original stochastic is below 50, then the value –1 is given. This leads to a sequence of 1/-1 values – the digital core of the new indicator. These values are subsequently smoothed by means of a short exponential moving average . This way minor false signals are eliminated and the indicator is given its typical form.
Calculation
The calculation is simple
Step1: create the CCI
Step 2: Use CCI as Fast MA and smoothed CCI as Slow MA
Step 3: Multiple the Slow and Fast MAs by their respective input ratios, and then divide by their sum. if the result is greater than 0, then the result is 1, if it's less than 0 then the result is -1, then chart the data
if ((slowr * slow_k + fastr * fast_k) / (fastr + slowr) > 50.0)
temp := 1
if ((slowr * slow_k + fastr * fast_k) / (fastr + slowr) < 50.0)
temp := -1
Step 4: Profit
Other implementations of Digital Kahler
This is to better understand the process the DK process and it's result, and furthermore, I'm linking these because for many in the Forex community, they see DK filtered indicators as the best implementations of standard indicators.
Digital Kahler MACD
VHF-Adaptive, Digital Kahler Variety RSI w/ Dynamic Zones
Digital Kahler CCI
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
Digital Kahler CCI [Loxx]Digital Kahler CCI is a Digital Kahler filtered CCI. This modification significantly reduces noise.
What is Digital Kahler?
From Philipp Kahler's article for www.traders-mag.com, August 2008. "A Classic Indicator in a New Suit: Digital Stochastic"
Digital Indicators
Whenever you study the development of trading systems in particular, you will be struck in an extremely unpleasant way by the seemingly unmotivated indentations and changes in direction of each indicator. An experienced trader can recognise many false signals of the indicator on the basis of his solid background; a stupid trading system usually falls into any trap offered by the unclear indicator course. This is what motivated me to improve even further this and other indicators with the help of a relatively simple procedure. The goal of this development is to be able to use this indicator in a trading system with as few additional conditions as possible. Discretionary traders will likewise be happy about this clear course, which is not nerve-racking and makes concentrating on the essential elements of trading possible.
How Is It Done?
The digital stochastic is a child of the original indicator. We owe a debt of gratitude to George Lane for his idea to design an indicator which describes the position of the current price within the high-low range of the historical price movement. My contribution to this indicator is the changed pattern which improves the quality of the signal without generating too long delays in giving signals. The trick used to generate this “digital” behavior of the indicator. It can be used with most oscillators like RSI or CCI .
First of all, the original is looked at. The indicator always moves between 0 and 100. The precise position of the indicator or its course relative to the trigger line are of no interest to me, I would just like to know whether the indicator is quoted below or above the value 50. This is tantamount to the question of whether the market is just trading above or below the middle of the high-low range of the past few days. If the market trades in the upper half of its high-low range, then the digital stochastic is given the value 1; if the original stochastic is below 50, then the value –1 is given. This leads to a sequence of 1/-1 values – the digital core of the new indicator. These values are subsequently smoothed by means of a short exponential moving average . This way minor false signals are eliminated and the indicator is given its typical form.
Calculation
The calculation is simple
Step1 : create the CCI
Step 2 : Use CCI as Fast MA and smoothed CCI as Slow MA
Step 3 : Multiple the Slow and Fast MAs by their respective input ratios, and then divide by their sum. if the result is greater than 0, then the result is 1, if it's less than 0 then the result is -1, then chart the data
if ((slowr * slow_k + fastr * fast_k) / (fastr + slowr) > 50.0)
temp := 1
if ((slowr * slow_k + fastr * fast_k) / (fastr + slowr) < 50.0)
temp := -1
Step 4 : Profit
Other implementations of Digital Kahler
This is to better understand the process the DK process and it's result, and furthermore, I'm linking these because for many in the Forex community, they see DK filtered indicators as the best implementations of standard indicators.
MACD
VHF-Adaptive, Digital Kahler Variety RSI w/ Dynamic Zones
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
Adaptive-Lookback CCI w/ Double Juirk Smoothing [Loxx]Adaptive-Lookback CCI w/ Double Juirk Smoothing is a CCI indicator with Adaptive period inputs. The adaptive calculation in this case is the count of pivots in historical bars. This indicator is also double smoothing using Jurik smoothing to reduce noise and refine the signal.
What is CCI?
The Commodity Channel Index ( CCI ) measures the current price level relative to an average price level over a given period of time. CCI is relatively high when prices are far above their average. CCI is relatively low when prices are far below their average. Using this method, CCI can be used to identify overbought and oversold levels.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Included:
Bar coloring
3 signal variations w/ alerts
R-squared Adaptive T3 [Loxx]R-squared Adaptive T3 is an R-squared adaptive version of Tilson's T3 moving average. This adaptivity was originally proposed by mladen on various forex forums. This is considered experimental but shows how to use r-squared adapting methods to moving averages. In theory, the T3 is a six-pole non-linear Kalman filter.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis. Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD, Momentum, Relative Strength Index) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA (simple moving average) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA(n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA.
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE/2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE/2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE/2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA, popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE/2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA(3) has lag 1, and EMA(11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA(3) through itself 5 times than if I just take EMA(11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA(3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA(7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA(n) = EMA(n) + EMA(time series - EMA(n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA. The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA(n) + EMA(time series - EMA(n))*.7;
This is algebraically the same as:
EMA(n)*1.7-EMA(EMA(n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD(n,v) = EMA(n)*(1+v)-EMA(EMA(n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA, and when v=1, GD is DEMA. In between, GD is a cooler DEMA. By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD(GD(GD(n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA(n)) to correct themselves. In Technical Analysis, these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Nyquist Moving Average (NMA) MACD [Loxx]Nyquist Moving Average (NMA) MACD is a MACD indicator using Nyquist Moving Average for its calculation.
What is the Nyquist Moving Average?
A moving average outlined originally developed by Dr . Manfred G. Dürschner in his paper "Gleitende Durchschnitte 3.0".
In signal processing theory, the application of a MA to itself can be seen as a Sampling procedure. The sampled signal is the MA (referred to as MA.) and the sampling signal is the MA as well (referred to as MA). If additional periodic cycles which are not included in the price series are to be avoided sampling must obey the Nyquist Criterion.
It can be concluded that the Moving Averages 3.0 on the basis of the Nyquist Criterion bring about a significant improvement compared with the Moving Averages 2.0 and 1.0. Additionally, the efficiency of the Moving Averages 3.0 can be proven in the result of a trading system with NWMA as basis.
What is the MACD?
Moving average convergence divergence (MACD) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA.
The result of that calculation is the MACD line. A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. Traders may buy the security when the MACD crosses above its signal line and sell—or short—the security when the MACD crosses below the signal line. Moving average convergence divergence (MACD) indicators can be interpreted in several ways, but the more common methods are crossovers, divergences, and rapid rises/falls.
Included
Bar coloring
2 types of signal output options
Alerts
Loxx's Expanded Source Types
