Relational Quadratic Kernel Channel [Vin]The Relational Quadratic Kernel Channel (RQK-Channel-V) is designed to provide more valuable potential price extremes or continuation points in the price trend.
Example:
Usage:
Lookback Window: Adjust the "Lookback Window" parameter to control the number of previous bars considered when calculating the Rational Quadratic Estimate. Longer windows capture longer-term trends, while shorter windows respond more quickly to price changes.
Relative Weight: The "Relative Weight" parameter allows you to control the importance of each data point in the calculation. Higher values emphasize recent data, while lower values give more weight to historical data.
Source: Choose the data source (e.g., close price) that you want to use for the kernel estimate.
ATR Length: Set the length of the Average True Range (ATR) used for channel width calculation. A longer ATR length results in wider channels, while a shorter length leads to narrower channels.
Channel Multipliers: Adjust the "Channel Multiplier" parameters to control the width of the channels. Higher multipliers result in wider channels, while lower multipliers produce narrower channels. The indicator provides three sets of channels, each with its own multiplier for flexibility.
Details:
Rational Quadratic Kernel Function:
The Rational Quadratic Kernel Function is a type of smoothing function used to estimate a continuous curve or line from discrete data points. It is often used in time series analysis to reduce noise and emphasize trends or patterns in the data.
The formula for the Rational Quadratic Kernel Function is generally defined as:
K(x) = (1 + (x^2) / (2 * α * β))^(-α)
Where:
x represents the distance or difference between data points.
α and β are parameters that control the shape of the kernel. These parameters can be adjusted to control the smoothness or flexibility of the kernel function.
In the context of this indicator, the Rational Quadratic Kernel Function is applied to a specified source (e.g., close prices) over a defined lookback window. It calculates a smoothed estimate of the source data, which is then used to determine the central value of the channels. The kernel function allows the indicator to adapt to different market conditions and reduce noise in the data.
The specific parameters (length and relativeWeight) in your indicator allows to fine-tune how the Rational Quadratic Kernel Function is applied, providing flexibility in capturing both short-term and long-term trends in the data.
To know more about unsupervised ML implementations, I highly recommend to follow the users, @jdehorty and @LuxAlgo
Optimizing the parameters:
Lookback Window (length): The lookback window determines how many previous bars are considered when calculating the kernel estimate.
For shorter-term trading strategies, you may want to use a shorter lookback window (e.g., 5-10).
For longer-term trading or investing, consider a longer lookback window (e.g., 20-50).
Relative Weight (relativeWeight): This parameter controls the importance of each data point in the calculation.
A higher relative weight (e.g., 2 or 3) emphasizes recent data, which can be suitable for trend-following strategies.
A lower relative weight (e.g., 1) gives more equal importance to historical and recent data, which may be useful for strategies that aim to capture both short-term and long-term trends.
ATR Length (atrLength): The length of the Average True Range (ATR) affects the width of the channels.
Longer ATR lengths result in wider channels, which may be suitable for capturing broader price movements.
Shorter ATR lengths result in narrower channels, which can be helpful for identifying smaller price swings.
Channel Multipliers (channelMultiplier1, channelMultiplier2, channelMultiplier3): These parameters determine the width of the channels relative to the ATR.
Adjust these multipliers based on your risk tolerance and desired channel width.
Higher multipliers result in wider channels, which may lead to fewer signals but potentially larger price movements.
Lower multipliers create narrower channels, which can result in more frequent signals but potentially smaller price movements.
Reversion
Auto-Length Adaptive ChannelsIntroduction
The key innovation of the ALAC is the implementation of dynamic length identification, which allows the indicator to adjust to the "market beat" or dominant cycle in real-time.
The Auto-Length Adaptive Channels (ALAC) is a flexible technical analysis tool that combines the benefits of five different approaches to market band and price deviation calculations.
Traders often tend to overthink of what length their indicators should use, and this is the main idea behind this script. It automatically calculates length based on pivot points, averaging the distance that is in between of current market highs and lows.
This approach is very helpful to identify market deviations, because deviations are always calculated and compared to previous market behavior.
How it works
The indicator uses a Detrended Rhythm Oscillator (DRO) to identify the dominant cycle in the market. This length information is then used to calculate different market bands and price deviations. The ALAC combines five different methodologies to compute these bands:
1 - Bollinger Bands
2 - Keltner Channels
3 - Envelope
4 - Average True Range Channels
5 - Donchian Channels
By averaging these calculations, the ALAC produces an overall market band that generalizes the approaches of these five methods into a single, adaptive channel.
How to Use
When the price is at the upper band, this might suggest that the asset is overbought and may be due for a price correction. Conversely, when the price is at the lower band, the asset may be oversold and due for a price increase.
The space between the bands represents the market's volatility. Wider bands indicate higher volatility, while narrower bands suggest lower volatility.
Indicator Settings
The settings of the ALAC allow for customization to suit different trading strategies:
Use Autolength?: This allows the indicator to automatically adjust the length of the dominant cycle.
Usual Length: If "Use Autolength?" is disabled, this setting allows the user to manually specify the length of the cycle.
Moving Average Type: This selects the type of moving average to be used in the calculations. Options include SMA, EMA, ALMA, DEMA, JMA, KAMA, SMMA, TMA, TSF, VMA, VAMA, VWMA, WMA, and ZLEMA.
Channel Multiplier: This adjusts the distance between the bands.
Channel Multiplier Step: This changes the step size of the channel multiplier. Each next market band will be multiplied by a previous one. You can potentially use values below 1, which will plot bands inside the first, main channel.
Use DPO instead of source data?: This setting uses the DPO for calculations instead of the source data. Basically, this is how you can add or eliminate trend from calculation of an average leg-up / leg-down move.
Fast: This adjusts the fast length of the DPO.
Slow: This adjusts the slow length of the DPO.
Zig-zag Period: This adjusts the period of the zig-zag pattern used in the DPO.
(!) For more information about DPO visit official TradingView description here: link
Also, I want to say thanks to @StockMarketCycles for initial idea of Detrended Rhythm Oscillator (DRO) that I use in this script.
The Adaptive Average Channel is a powerful and versatile indicator that combines the strengths of multiple technical analysis methods.
In summary, with the ALAC, you can:
1 - Dynamically adapt to any asset and price action with automatic calculation of dominant cycle lengths.
2 - Identify potential overbought and oversold conditions with the adaptive market bands.
3 - Customize your analysis with various settings, including moving average type and channel multiplier.
4 - Enhance your trading strategy by using the indicator in conjunction with other forms of analysis.
Adaptive Mean Reversion IndicatorThe Adaptive Mean Reversion Indicator is a tool for identifying mean reversion trading opportunities in the market. The indicator employs a dynamic approach by adapting its parameters based on the detected market regime, ensuring optimal performance in different market conditions.
To determine the market regime, the indicator utilizes a volatility threshold. By comparing the average true range (ATR) over a 14-period to the specified threshold, it determines whether the market is trending or ranging. This information is crucial as it sets the foundation for parameter optimization.
The parameter optimization process is an essential step in the indicator's calculation. It dynamically adjusts the lookback period and threshold level based on the identified market regime. In trending markets, a longer lookback period and higher threshold level are chosen to capture extended trends. In ranging markets, a shorter lookback period and lower threshold level are used to identify mean reversion opportunities within a narrower price range.
The mean reversion calculation lies at the core of this indicator. It starts with computing the mean value using the simple moving average (SMA) over the selected lookback period. This represents the average price level. The deviation is then determined by calculating the standard deviation of the closing prices over the same lookback period. The upper and lower bands are derived by adding and subtracting the threshold level multiplied by the deviation from the mean, respectively. These bands serve as dynamic levels that define potential overbought and oversold areas.
In real-time, the indicator's adaptability shines through. If the market is trending, the adaptive mean is set to the calculated mean value. The adaptive upper and lower bands are adjusted by scaling the threshold level with a factor of 0.75. This adjustment allows the indicator to be less sensitive to minor price fluctuations during trending periods, providing more robust mean reversion signals. In ranging market conditions, the regular mean, upper band, and lower band are used as they are more suited to capture mean reversion within a confined price range.
The signal generation component of the indicator identifies potential trading opportunities based on the relationship between the current close price and the adaptive upper and lower bands. If the close price is above the adaptive upper band, it suggests a potential short entry opportunity (-1). Conversely, if the close price is below the adaptive lower band, it indicates a potential long entry opportunity (1). When the close price is within the range defined by the adaptive upper and lower bands, no clear trading signal is generated (0).
To further strengthen the quality of signals, the indicator introduces a confluence condition based on the RSI. When the RSI exceeds the threshold levels of 70 or falls below the threshold level of 30, it indicates a strong momentum condition. By incorporating this confluence condition, the indicator ensures that mean reversion signals align with the prevailing market momentum. It reduces the likelihood of false signals and provides traders with added confidence when entering trades.
The indicator offers alert conditions to notify traders of potential trading opportunities. Alert conditions are set to trigger when a potential long entry signal (1) or a potential short entry signal (-1) aligns with the confluence condition. These alerts allow traders to stay informed about favorable mean reversion setups, even when they are not actively monitoring the charts. By leveraging alerts, traders can efficiently manage their time and take advantage of market opportunities.
To enhance visual interpretation, the indicator incorporates background coloration that provides valuable insights into the prevailing market conditions. When the indicator generates a potential short entry signal (-1) that aligns with the confluence condition, the background color is set to lime. This color suggests a bullish trend that is potentially reaching an exhaustion point and about to revert downwards. Similarly, when the indicator generates a potential long entry signal (1) that aligns with the confluence condition, the background color is set to fuchsia. This color represents a bearish trend that is potentially reaching an exhaustion point and about to revert upwards. By employing background coloration, the indicator enables traders to quickly identify market conditions that may offer mean reversion opportunities with a directional bias.
The indicator further enhances visual clarity by incorporating bar coloring that aligns with the prevailing market conditions and signals. When the indicator generates a potential short entry signal (-1) that aligns with the confluence condition, the bar color is set to lime. This color signifies a bullish trend that is potentially reaching an exhaustion point, indicating a high probability of a downward reversion. Conversely, when the indicator generates a potential long entry signal (1) that aligns with the confluence condition, the bar color is set to fuchsia. This color represents a bearish trend that is potentially reaching an exhaustion point, indicating a high probability of an upward reversion. By using distinct bar colors, the indicator provides traders with a clear visual distinction between bullish and bearish trends, facilitating easier identification of mean reversion opportunities within the context of the broader trend.
While the "Adaptive Mean Reversion Indicator" offers a robust framework for identifying mean reversion opportunities, it's important to remember that no indicator is foolproof. Traders should exercise caution and employ risk management strategies. Additionally, it is recommended to use this indicator in conjunction with other technical analysis tools and fundamental factors to make well-informed trading decisions. Regular backtesting and refinement of the indicator's parameters are crucial to ensure its effectiveness in different market conditions.
Intraday Mean Reversion MainThe Intraday Mean Reversion Indicator works well on certain stocks. It should be used for day trading stocks but need to be applied on the Day to Day timeframe.
The logic behind the indicator is that stocks that opens substantially lower than yesterdays close, very often bounces back during the day and closes higher than the open price, thus the name Intraday Mean reversal. The stock so to speak, reverses to the mean.
The indicator has 7 levels to choose from:
0.5 * standard deviation
0.6 * standard deviation
0.7 * standard deviation
0.8 * standard deviation
0.9 * standard deviation
1.0 * standard deviation
1.1 * standard deviation
The script can easily be modified to test other levels as well, but according to my experience these levels work the best.
The info box shows the performance of one of these levels, chosen by the user.
Every Yellow bar in the graph shows a buy signal. That is: The stocks open is substantially lower (0.5 - 1.1 standard deviations) than yesterdays close. This means we have a buy signal.
The Multiplier shows which multiplier is chosen, the sum shows the profit following the strategy if ONE stock is bought on every buy signal. The Ratio shows the ratio between winning and losing trades if we followed the strategy historically.
We want to find stocks that have a high ratio and a positive sum. That is More Ups than downs. A ratio over 0.5 is good, but of course we want a margin of safety so, 0.75 is a better choice but harder to find.
If we find a stock that meets our criteria then the strategy will be to buy as early as possible on the open, and sell as close as possible on the close!
LNL Simple Hedging ToolLNL Simple Hedging Tool
Simple Hedging Tool was created specifically for swing traders who struggle with hedging. This tool helps to spot the ideal moments to put the hedges on (protection of the portfolio during "high risk" times). Simple Hedging Tool will not help you when day trading. It was designed for the daily charts. It is called simple because it is pretty much self-explanatory indicator. The candles are either blue or yellow. Meaning of the colors depend on the version you are using. This tool consist of two versions:
SPX Version:
This version was designed for indexes & overall market benchmarks. In contrast with the VIX version, the SPX version is little more sophisticated since it is based on key market internals. Blue arrows above the candles? More often than not this is signalizing that the key market internals are now approaching bearish signals which means it is the best time to hedge any bullish positions. On the contrary, the yellow arrows are the good reason to lighten up of the shorts & ease off the gas pedal on any bearish outlooks.
VIX Version:
Apart from the black swan events (big market crashes) Vix usually oscillates between the daily extremes. The VIX version is based on a simple bollinger band technique which is visualized with blue & yellow arrows. Whenever the yellow arrows & candles appear, it is good time to put the hedges on & perhaps lighten up on longs.
IMPORTANT DISCLAIMER:
The signals from this tool WILL NOT TELL YOU where to buy or sell! But rather when is a good time TO NOT buy or TO NOT sell. Once the signals appear it does not necessarily mean that the move is over & reversion willl happen immidiately. These signals can be flashing for days even weeks. They are not flashing for you to change the bias but rather tighten up your exposure in case your portfolio is mostly one sided.
Hope it helps.
Rekt Edge Reversion BandRekt Edge Reversion band is a technical indicator that utilizes a combination of moving averages and standard deviations to determine optimal entry and exit points in the market. By comparing the current price to its moving average, the indicator identifies potential trends and determines how you can position around them by plotting buy/sell signals and two channels based on user input parameters. The user can choose between Simple Moving Average ( SMA ) or Exponential Moving Average ( EMA ) and select the moving average period, the unit of separation, the multiples of the unit, and other important parameters. The indicator's inputs can be adjusted to suit different trading styles, and it can be used on any time frame. The indicator can be used to identify potential trend reversals or breakouts (or breakdowns) when the price moves outside of the channels. The indicators potential use cases include identifying overbought or oversold conditions. With its ability to provide a clear signal on when to enter and exit a trade, this indicator is a popular tool among traders looking to make more informed and profitable trading decisions. This indicator can also be used in conjunction with other technical analysis tools to confirm or invalidate trading signals.
range_statA basic statistic to describe "ranges". There are three inputs:
- short range
- long range
- moving average length
The output is a ratio of the short range to the long range. In the screenshot example, the short range is a single day (bar) and the long range is five days. A value near "1" would mean that every day entirely fills the five day range, and that a consolidation is likely present. A value near 0 would mean that each day fills only a small portion of the five day range, and price is probably "trending".
The moving average length is for smoothing the result (which also lags it of course).
The mean, and +- 2 standard deviations are plotted as fuchsia colored lines.
Keltner Channels Bands (RMA)Keltner Channel Bands
These normally consist of:
Keltner Channel Upper Band = EMA + Multiplier ∗ ATR
Keltner Channel Lower Band = EMA − Multiplier ∗ ATR
However instead of using ATR we are using RMA
This gives us a much smoother take of the KCB
We are also using 2 sets of bands built on 1 Moving average, this is a common set up for mean reversion strategies.
This can often be paired with RSI for lower timeframe divergences
Divergence
This is using the RSI to calculate when price sets new lows/highs whilst the RSI movement is in the opposite direction.
The way this is calculated is slightly different to traditional divergence scripts. instead of looking for pivot highs/lows in the RSI we are logging the RSI value when price makes it pivot highs/lows.
Gradient Bands
The Gradient Colouring on the bands is measuring how long price has been either side of the MA.
As Keltner bands are commonly used as a mean reversion strategy, I thought it would be useful to see how long price has been trending in a certain direction, the stronger the colours get,
the longer price has been trending that direction which could suggest we are looking for a retrace soon.
Alerts
Alerts included let you choose whether you want to receive an alert for the inside, outside or both band touches.
To set up these alerts, simply toggle them on in the settings, then click on the 3 dots next to the indicators name, from there you click 'Add Alert'.
From there you can customise the alert settings but make sure to leave the 2 top boxes which control the alert conditions. They will be default selected onto your correct settings, the rest you may want to change.
Once you create the alert, it will then trigger as soon as price touches your chosen inside/outside band.
Suggestions
Please feel free to offer any suggestions which you think could improve the script
Disclaimer
The default settings/parameters were shared by Jimtalbott, feel free to play about with the and use this code to make your own strategies.
Mean Reversion DotsMarkets tend to mean revert. This indicator plots a moving average from a higher time frame (type of MA and length selectable by the user). It then calculates standard deviations in two dimensions:
- Standard deviation of move of price away from this moving average
- Standard deviations of number of bars spent in this extended range
The indicator plots a table in the upper right corner with the % of distance of price from the moving average. It then plots 'mean reversion dots' once price has been 1 or more standard deviations away from the moving average for one or more standard deviations number of bars. The dots change color, becoming more intense, the longer the move persists. Optionally, the user can display the standard deviations in movement away from the moving average as channels, and the user can also select which levels of moves they want to see. Opting to see only more extreme moves will result in fewer signals, but signals that are more likely to imminently result in mean reversion back to the moving average.
In my opinion, this indicator is more likely to be useful for indices, futures, commodities, and select larger cap names.
Combinations I have found that work well for SPX are plotting the 30min 21ema on a 5min chart and the daily 21ema on an hourly chart.
In many cases, once mean reversion dots for an extreme enough move (level 1.3 or 2.2 and above) begin to appear, a trade may be initiated from a support/resistance level. A safer way to use these signals is to consider them as a 'heads up' that the move is overextended, and then look for a buy/sell signal from another indicator to initiate a position.
Note: I borrowed the code for the higher timeframe MA from the below indicator. I added the ability to select type of MA.
Daily/Weekly ExtremesBACKGROUND
This indicator calculates the daily and weekly +-1 standard deviation of the S&P 500 based on 2 methodologies:
1. VIX - Using the market's expectation of forward volatility, one can calculate the daily expectation by dividing the VIX by the square root of 252 (the number of trading days in a year) - also know as the "rule of 16." Similarly, dividing by the square root of 50 will give you the weekly expected range based on the VIX.
2. ATR - We also provide expected weekly and daily ranges based on 5 day/week ATR.
HOW TO USE
- This indicator only has 1 option in the settings: choosing the ATR (default) or the VIX to plot the +-1 standard deviation range.
- This indicator WILL ONLY display these ranges if you are looking at the SPX or ES futures. The ranges will not be displayed if you are looking at any other symbols
- The boundaries displayed on the chart should not be used on their own as bounce/reject levels. They are simply to provide a frame of reference as to where price is trading with respect to the market's implied expectations. It can be used as an indicator to look for signs of reversals on the tape.
- Daily and Weekly extremes are plotted on all time frames (even on lower time frames).
[Sidders]Std. Deviation from Mean/MA (Z-score)This indicator visualizes in a straight forward way the distance price is away from the mean in absolute standard deviations (Z-score) over a certain lookback period (can be configured). Additionally I've included a moving average of the distance, the MA type can be configured in the settings.
Personally using this indicator for some of my algo mean reversion strategies. Price reaching the extreme treshold (can be configured in settings, standard is 3) could be seen as a point where price will revert to the mean.
I've included alerts for when price crosses into extreme areas, as well as alerts for when crosses back into 'normal' territory again. Both are also plotted on the indicator through background coloring/shapes.
Since I've learned so much from other developers I've decided to open source the code. Let me know if you have any ideas on how to improve, I'll see if I can implement them.
Enjoy!
Multiple Indicator 50EMA Cross AlertsHere’s a screener including Symbol, Price, TSI, and 50 ema cross in a table output.
The 50 Exponential Moving Average is a trend indicator
You can find bullish momentum when the 50 ema crossed over or a bearish momentum when the 50 ema crossed under we are looking to take advantage by trading the reversion of these trends.
True strength index (TSI) is a trend momentum indicator
Readings are bullish when the True Strength Index shows positive values
Readings are bearish when the indicator displays negative values.
When a value is above 20, we look for selling overbought opportunity and when the value is under 20, we look for buying oversold opportunity.
You can select the pair of your choice in the settings.
Make sure to create an alert and choose any alerts then an alert will trigger when a price cross under or cross over the 50 ema for every pair separately.
This allow the user to verify if there is a trade set up or not.
Disclaimer
This post and the script don’t provide any financial advice.
Trend Day IndentificationVolatility is cyclical, after a large move up or down the market typically "ranges" during the next session. Directional order flow that enters the market during this subsequent session tends not to persist, this non-persistency of transactions leads to a non-trend day which is when I trade intraday reversionary strategies.
This script finds trend days in BTC with the purpose of:
1) counting trend day frequency
2) predicting range contraction for the next 1-2 days so I can run intraday reversion strategies
Trend down is defined as daily bar opening within X% of high and closing within X% of low
Trend up is defined as daily bar opening within X% of low and closing within X% of high
default parameters are:
1) open range extreme = 15% (open is within 15% of high or low)
2) close range extreme = 15% (close is within 15% of high or low)
There is also an atr filter that checks that the trend day has a larger range than the previous 4 bars this is to make sure we find true range expansion vs recent ranges.
Notes:
If a trend day occurs after a prolonged sideways contraction it can signal a breakout - this is less common but is an exception to the rule. These types of occurrences can lead to the persistency of order flow and result in extended directional daily runs.
If a trend day occurs close to 20 days high or low (stopping just short OR pushing slightly through) then wait an additional day before trading intraday reversion strategies.
Deviation BandsThis indicator plots the 1, 2 and 3 standard deviations from the mean as bands of color (hot and cold). Useful in identifying likely points of mean reversion.
Default mean is WMA 200 but can be SMA, EMA, VWMA, and VAWMA.
Calculating the standard deviation is done by first cleaning the data of outliers (configurable).
ETF 3-Day Reversion StrategyIntroduction: This strategy is a modification of the “3-day Mean Reversion Strategy” from the book "High Probability ETF Trading" by Larry Connors and Cesar Alvarez. In the book, the authors discuss a high-probability ETF mean reversion strategy for a 1-day time-frame with these simple rules:
The price must be above the 200 day SMA and below the 5 day SMA.
The low of today must be lower than the low of yesterday (must be true for 3 consecutive days)
The high of today must be lower than the high of yesterday (must be true for 3 consecutive days)
If the 3 rules above are true, then buy on the close of the current day.
Exit when the closing price crosses above the 5 day SMA.
In practice and in backtesting, I’ve found that the strategy consistently works better when using an EMA for the trend-line instead of an SMA. So, this script uses an EMA for the trend-line. I’ve also made the length of the exit EMA adjustable.
How it works:
The Strategy will buy when the buy conditions above are true. The strategy will sell when the closing price crosses over the Exit Moving Average
Plots:
Green line = Exit Moving Average (Default 5 Day EMA)
Blue line = 5 Day EMA (Used as Entry Criteria)
Disclaimer: Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
EMA MTF PlusI like trading the 1 minute and 3 minutes time-frames. I'm what is commonly called a "scalper". Long term investments yes, I have some, but for trading, I don't have neither the time,
nor the patience to wait hours or days for my trade to be complete.
This doesn't mean I discount the higher time-frames, no, I actually rely heavily on them. I found that EMAs do a decent job as support/resistance, sometimes to a tick level of precision. And this is important for a 1 minute trader.
As such, I made this script that tracks the higher time-frames EMAs and displays the last value as a line.
I do not need the whole EMA, I'm not interested in crossovers or crossunders, these are anyway late signals for me.
What's with the triangles? These are local tops/bottoms , candles that have a have decent size of the wick. These tops and bottoms are by no means "final", they are merely a rejection at certain levels of price. Due to markets complexities (and human erratic behaviors hehe) these levels could be breached at the very next candle. For a more "final" version (nothing is really final but..) I added Schaff Trend Cycle as filter, so a triangle will pop only when a trend is mature enough ( STC with a value near 0 or near 100).
Colored bars. When the body of the candle is big, it shows strength. Strong bars tend to have follow through, especially when breaking key levels. The script looks at the body of the candle and compares it with ATR (Average True Range), if it's at least 0.8 of ATR it changes the bar color to yellow (bull candles) or fuchsia(bear candles).
Range identifier. This code is copied from Lazy Bear (if there are any issues please let me know), it's very useful in conjunction with colored bars.
I look for breakout candles that go outside of the range as a signal for a trade.
There are many ways in which this script can be useful, like trading mean reversions or momentum trades (breakouts) or simply trend following trades.
I hope you guys find it useful, you can play with default values and change them as you like, these are what I found to be working best for me and my trading universe (mostly crypto).
Special thanks for the original work of:
LazyBear
everget
Jim8080
Keltner Channels BandsKeltner Channel Bands
Great indicator for mean reversion strategies.
Alerts you can set:
Crossover EMA
Crossunder EMA
Crossover upper band
Crossunder upper band
Crossover lower band
Crossunder lower band
Have fun!
conditional_returnsThis script attempts to contextualize the instrument's latest return. It asks, "when a return of the same or greater magnitude occurred in the past, in the same direction, what was the following period's return?"
By default, the latest return is used. For example, on a daily chart, that would mean "today's" return. However, you can select any return you want using the "override" input.
The output table shows:
- The latest/override return, as a percentage. This is in the top left, fuchsia cell.
The first three, blue columns, show:
- The count of up and down (or positive and negative) next period returns. This shows you the sample size.
- The percentage of up/down next period returns.
- The average next-period return return, up and down, as percentages.
The next three, green columns show these same statistics, but for all returns--every period in the active date range is used. This data serves as a basis for comparison.
Note that you can select a custom date range with the "start" and "end" inputs. The corresponding area on the chart is shaded light grey, to show which data is used in the computations.
[cache_that_pass] 1m 15m Function - Weighted Standard DeviationTradingview Community,
As I progress through my journey, I have come to the realization that it is time to give back. This script isn't a life changer, but it has the building blocks for a motivated individual to optimize the parameters and have a production script ready to go.
Credit for the indicator is due to @rumpypumpydumpy
I adapted this indicator to a strategy for crypto markets. 15 minute time frame has worked best for me.
It is a standard deviation script that has 3 important user configured parameters. These 3 things are what the end user should tweak for optimum returns. They are....
1) Lookback Length - I have had luck with it set to 20, but any value from 1-1000 it will accept.
2) stopPer - Stop Loss percentage of each trade
3) takePer - Take Profit percentage of each trade
2 and 3 above are where you will see significant changes in returns by altering them and trying different percentages. An experienced pinescript programmer can take this and build on it even more. If you do, I ask that you please share the script with the community in an open-source fashion.
It also already accounts for the commission percentage of 0.075% that Binance.US uses for people who pay fees with BNB.
How it works...
It calculates a weighted standard deviation of the price for the lookback period set (so 20 candles is default). It recalculates each time a new candle is printed. It trades when price lows crossunder the bottom of that deviation channel, and sells when price highs crossover the top of that deviation channel. It works best in mid to long term sideways channels / Wyckoff accumulation periods.
Augmented Dickey–Fuller (ADF) mean reversion testThe augmented Dickey-Fuller test (ADF) is a statistical test for the tendency of a price series sample to mean revert .
The current price of a mean-reverting series may tell us something about the next move (as opposed, for example, to a geometric Brownian motion). Thus, the ADF test allows us to spot market inefficiencies and potentially exploit this information in a trading strategy.
Mathematically, the mean reversion property means that the price change in the next time period is proportional to the difference between the average price and the current price. The purpose of the ADF test is to check if this proportionality constant is zero. Accordingly, the ADF test statistic is defined as the estimated proportionality constant divided by the corresponding standard error.
In this script, the ADF test is applied in a rolling window with a user-defined lookback length. The calculated values of the ADF test statistic are plotted as a time series. The more negative the test statistic, the stronger the rejection of the hypothesis that there is no mean reversion. If the calculated test statistic is less than the critical value calculated at a certain confidence level (90%, 95%, or 99%), then the hypothesis of a mean reversion is accepted (strictly speaking, the opposite hypothesis is rejected).
Input parameters:
Source - The source of the time series being tested.
Length - The number of points in the rolling lookback window. The larger sample length makes the ADF test results more reliable.
Maximum lag - The maximum lag included in the test, that defines the order of an autoregressive process being implied in the model. Generally, a non-zero lag allows taking into account the serial correlation of price changes. When dealing with price data, a good starting point is lag 0 or lag 1.
Confidence level - The probability level at which the critical value of the ADF test statistic is calculated. If the test statistic is below the critical value, it is concluded that the sample of the price series is mean-reverting. Confidence level is calculated based on MacKinnon (2010) .
Show Infobox - If True, the results calculated for the last price bar are displayed in a table on the left.
More formal background:
Formally, the ADF test is a test for a unit root in an autoregressive process. The model implemented in this script involves a non-zero constant and zero time trend. The zero lag corresponds to the simple case of the AR(1) process, while higher order autoregressive processes AR(p) can be approached by setting the maximum lag of p. The null hypothesis is that there is a unit root, with the alternative that there is no unit root. The presence of unit roots in an autoregressive time series is characteristic for a non-stationary process. Thus, if there is no unit root, the time series sample can be concluded to be stationary, i.e., manifesting the mean-reverting property.
A few more comments:
It should be noted that the ADF test tells us only about the properties of the price series now and in the past. It does not directly say whether the mean-reverting behavior will retain in the future.
The ADF test results don't directly reveal the direction of the next price move. It only tells wether or not a mean-reverting trading strategy can be potentially applicable at the given moment of time.
The ADF test is related to another statistical test, the Hurst exponent. The latter is available on TradingView as implemented by balipour , QuantNomad and DonovanWall .
The ADF test statistics is a negative number. However, it can take positive values, which usually corresponds to trending markets (even though there is no statistical test for this case).
Rigorously, the hypothesis about the mean reversion is accepted at a given confidence level when the value of the test statistic is below the critical value. However, for practical trading applications, the values which are low enough - but still a bit higher than the critical one - can be still used in making decisions.
Examples:
The VIX volatility index is known to exhibit mean reversion properties (volatility spikes tend to fade out quickly). Accordingly, the statistics of the ADF test tend to stay below the critical value of 90% for long time periods.
The opposite case is presented by BTCUSD. During the same time range, the bitcoin price showed strong momentum - the moves away from the mean did not follow by the counter-move immediately, even vice versa. This is reflected by the ADF test statistic that consistently stayed above the critical value (and even above 0). Thus, using a mean reversion strategy would likely lead to losses.
Roc Mean Reversion (ValueRay)This Indicator shows the Absolute Rate of Change in correlation to its Moving Average.
Values over 3 (gray dotted line) can savely be considered as a breakout; values over 4.5 got a high mean-reverting chance (red dotted line).
This Indicator can be used in all timeframes, however, i recommend to use it <30m, when you want search for meaningful Mean-Reverting Signals.
Please like, share and subscribe. With your love, im encouraged to write and publish more Indicators.
% Divergence of RSIA simple script that plots the difference between the %ROC of price vs the %ROC of RSI, AKA the % of divergence. A simple way to analyze how strong a potential divergence is. Top reversals are above 0, bottom reversals are below. A value of 0 means price and RSI are changing by the same % value. So, if oscillator is moving up as price moves up, it means divergence is increasing. If oscillator moves down as price moves up, it means divergence is decreasing.
Res/Sup With Concavity & Increasing / Decreasing Trend AnalysisPurple means the concavity is down blue means concavity is up which is good.
Yellow means increasing, Red means decreasing.
Sup = Green
Res = Red
