The VMA (Variable Moving Average), also known as VIDYA (Volatility Index Dynamic Average) was developed by Tushar S. Chande and first presented in the March 1992 issue of Technical Analysis of Stocks & Commodities - Adapting Moving Averages To Market Volatility .
Chande's theory was that the performance of an exponential moving average could be improved by using...
VMA Bands are ATR bands with VMA as its centre. For a description of options, refer to my VMA post:
I have moved VMA calculation in to a separate function. Feel free to use calc_vma() in your scripts. For more MA calculation function (KAMA, VIDYA and others), refer to my complete list of indicators below.
Wish you all...
Variable Moving Average, often abbreviated as VMA, is an Exponential Moving Average developed by Tushar S. Chande. VMA automatically adjusts its smoothing constant on the basis of Market Volatility.
Use this like other Moving Averages. I have added the following options that can be enabled via options page:
- Trend Direction Indication: Green = Up trend, Blue =...
This is an experimental adaptive trend following study inspired by Giorgos Siligardos's Reverse Engineering RSI and Tushar S. Chande's Variable Moving Average.
In this study, reverse engineered RSI levels are calculated and used to generate a volatility index for VMA calculation.
First, price levels are calculated for when RSI will equal 70 and 30. The...
VOLUME PRESSURE COMPOSITE AVERAGE WITH BANDS
Ver. 1.0.beta.10.08.2015
By Ricardo M Arjona @XeL_Arjona
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The...
Using a simple 13 / 50 Day EMA crossover; 62% of the time the 13 Day EMA was above the 50 Day EMA.
Conclusion:
EMA(13) > EMA(50) = Bullish
EMA(13) < EMA(50) = Bearish
Plots four VMA's (Variable/Volatility Moving Average) in multiple static resolutions (1h, 2h, 4h, 8h), ideal for support/resistance/stops on predictably trending symbols like BTCUSD.
Example:
This study is an experiment in adaptive filtering. The process in this study was inspired by KAMA and ZLEMA filtering techniques.
First, data is given an optional modification for lag reduction.
Then, an adaptive filter of your choice is calculated. There are 6 different adaptive filters to choose from in this study:
-Commodity Channel Index Adaptive Moving...
This is simply three moving averages in the same indicator, with the possibility to change the source, length, offset and type on every moving average (Simple, Exponential, Weighted, Volume-weighted, Triple EMA or a moving average that uses RSI ).
If you want to disable any of the moving averages, then do that under the "Style"-tab by unchecking the box for that...
This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow...
![EURUSD: Variable Moving Average Bands [LazyBear]](https://s3.tradingview.com/h/HT99VkZb_mid.png "Variable Moving Average Bands [LazyBear]")
![AAPL: Variable Moving Average [LazyBear]](https://s3.tradingview.com/6/6Ix0E5Yr_mid.png "Variable Moving Average [LazyBear]")
![USDCHF: Relative Strength Volatility Variable Bands [DW]](https://s3.tradingview.com/1/1IyvlXWs_mid.png "Relative Strength Volatility Variable Bands [DW]")
![BTCUSD: Index Adaptive Keltner Channels [DW]](https://s3.tradingview.com/f/F6BVjYMH_mid.png "Index Adaptive Keltner Channels [DW]")
