Bollinger Bands with HMAThe built-in Bollinger Band script modified to use the Hull Moving Average as the basis.
Hull Moving Averages have much less lag than a regular moving average.
Do not assume that regular BB interpretation rules apply to this.
This is an experimental indicator at this time.
Полосы и каналы
ATR Trend FollowingThe script filters stocks on the basis of ATR. If the stock has moved above 7 times the ATR from the lows, the system generates buy signal and continues till the stock drops by 2 ATR. It is a good system in trending markets however in sideways consolidating markets, the system must be avoided. In trending markets it can generate good returns with significant Risk to Reward Ratio. Use it in confirmation with other trend depicting indicators is expected to generate better results.
supertrend advanceHELLO FRIENDS ...............THIS IS SUPERTREND ADVANCE WITH HENKIASHI CANDLE ...I got so many request on supertrend with henkiashi. This is for all of them ..I am making it open for all so you can change its coding according to your need
SOME IMPONTENT UPDATE IN THIS SUPERTRNED
1) You Can Use It for option trading ...you can do algo option trading using this strategy
2) YOU CAN USE CUSTOME SYNTAX TO ALGO TRADE IN STOCK,FOREX,COMMODITY AND CRYPTO
3) YO CAN USE IT IN INTRADAY TIME PERIOD ALSO U CAN SET ITS ENTRY AND EXIT TIME
4) YOU CAN USE HENKIASHI SUPERTREND ON NORMAL CANDLE STICK CHART
5) YOU CAN USE ITS QUNTITY FEATURE .BY THIS WAY U CAN DOUBLE YOUR QUNTITY SIZE ON LOSSING TRADE AND WHEN PROIFT TRADE OCCUR ITS QUNTITY AGAIN AUTOMATICALLY SHIFTED TO NORMAL QUNTITY ....THIS FEATURE ONLY HELP TRADER WITH MORE MARGIN....USE THIS FEATURE PROPERLY.THIS FEATURE BEST WORK ON ONE SIDE MEANS ONLY BUY SIDE TRADE OR SELL SIDE TRADE
6)YOU CAN SET YOUR TARGET AND STOPLOSS IN POINTS AND IN PERCENTAGE
7) YOU CAN CHOSE ONE SIDE TO TRADE ONLY BUY SIDE OR SELL SIDE
HOPE THIS FEARTURES HELPS EVERY ONE
ALL THE BEST FOR SUCESSFULL TRADING
Supertrend Filtered with MA ADX,Vol & RSI,RVSItest script that combines Supertrend Filtered with MA ADX,Vol & RSI,RVSI
Wavetrend Moving Average (WTMA) [Loxx]Wavetrend Moving Average (WTMA) is a Wavetrend indicator morphed into a moving average. Use this indicator as you would any other moving average.
What is the Wavetrend indicator?
Wavetrend's original name was TCI created by the The Trading Center. The TCI (Trading Channel Index) displays the ratio between the average daily price and the smoothed averaged value of the average daily price.
You can read about TCI here: tradingcenter.org
How do you morph Wavetrend into a moving average?
1. Calculate Wavetrend
2. Normalize Wavetrend from a range of -1 to 1 to a range of 0 to 1
3. Find high/low values of X bars back of Wavetrend,
4. Subtract high from low to find the simple range of Wavetrend
5. Calculate upper and lower boundaries
6. Calculate the HL2 of using these new boundaries
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Possible RSI [Loxx]Possible RSI is a normalized, variety second-pass normalized, Variety RSI with Dynamic Zones and optionl High-Pass IIR digital filtering of source price input. This indicator includes 7 types of RSI.
High-Pass Fitler (optional)
The Ehlers Highpass Filter is a technical analysis tool developed by John F. Ehlers. Based on aerospace analog filters, this filter aims at reducing noise from price data. Ehlers Highpass Filter eliminates wave components with periods longer than a certain value. This reduces lag and makes the oscialltor zero mean. This turns the RSI output into something more similar to Stochasitc RSI where it repsonds to price very quickly.
First Normalization Pass
RSI (Relative Strength Index) is already normalized. Hence, making a normalized RSI seems like a nonsense... if it was not for the "flattening" property of RSI. RSI tends to be flatter and flatter as we increase the calculating period--to the extent that it becomes unusable for levels trading if we increase calculating periods anywhere over the broadly recommended period 8 for RSI. In order to make that (calculating period) have less impact to significant levels usage of RSI trading style in this version a sort of a "raw stochastic" (min/max) normalization is applied.
Second-Pass Variety Normalization Pass
There are three options to choose from:
1. Gaussian (Fisher Transform), this is the default: The Fisher Transform is a function created by John F. Ehlers that converts prices into a Gaussian normal distribution. The normaliztion helps highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend.
2. Softmax: The softmax function, also known as softargmax: or normalized exponential function, converts a vector of K real numbers into a probability distribution of K possible outcomes. It is a generalization of the logistic function to multiple dimensions, and used in multinomial logistic regression. The softmax function is often used as the last activation function of a neural network to normalize the output of a network to a probability distribution over predicted output classes, based on Luce's choice axiom.
3. Regular Normalization (devaitions about the mean): Converts a vector of K real numbers into a probability distribution of K possible outcomes without using log sigmoidal transformation as is done with Softmax. This is basically Softmax without the last step.
Dynamic Zones
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
7 Types of RSI
See here to understand which RSI types are included:
Included:
Bar coloring
4 signal types
Alerts
Loxx's Expanded Source Types
Loxx's Variety RSI
Loxx's Dynamic Zones
Bollinger Pair TradeNYSE:MA-1.6*NYSE:V
Revision: 1
Author: @ozdemirtrading
Revision 2 Considerations :
- Simplify and clean up plotting
Disclaimer: This strategy is currently working on the 5M chart. Change the length input to accommodate your needs.
For the backtesting of more than 3 months, you may need to upgrade your membership.
Description:
The general idea of the strategy is very straightforward: it takes positions according to the lower and upper Bollinger bands.
But I am mainly using this strategy for pair trading stocks. Do not forget that you will get better results if you trade with cointegrated pairs.
Bollinger band: Moving average & standard deviation are calculated based on 20 bars on the 1H chart (approx 240 bars on a 5m chart). X-day moving averages (20 days as default) are also used in the background in some of the exit strategy choices.
You can define position entry levels as the multipliers of standard deviation (for exp: mult2 as 2 * standard deviation).
There are 4 choices for the exit strategy:
SMA: Exit when touches simple moving average (SMA)
SKP: Skip SMA and do not stop if moving towards 20D SMA, and exit if it touches the other side of the band
SKPXDSMA: Skip SMA if moving towards 20D SMA, and exit if it touches 20D SMA
NoExit: Exit if it touches the upper & lower band only.
Options:
- Strategy hard stop: if trade loss reaches a point defined as a percent of the initial capital. Stop taking new positions. (not recommended for pair trade)
- Loss per trade: close position if the loss is at a defined level but keeps watching for new positions.
- Enable expected profit for trade (expected profit is calculated as the distance to SMA) (recommended for pair trade)
- Enable VIX threshold for the following options: (recommended for volatile periods)
- Stop trading if VIX for the previous day closes above the threshold
- Reverse active trade direction if VIX for the previous day is above the threshold
- Take reverse positions (assuming the Bollinger band is going to expand) for all trades
Backtesting:
Close positions after a defined interval: mark this if you want the close the final trade for backtesting purposes. Unmark it to get live signals.
Use custom interval: Backtest specific time periods.
Other Options:
- Use EMA: use an exponential moving average for the calculations instead of simple moving average
- Not against XDSMA: do not take a position against 20D SMA (if X is selected as 20) (recommended for pairs with a clear trend)
- Not in XDSMA 1 DEV: do not take a position in 20D SMA 1*standart deviation band (recommended if you need to decrease # of trades and increase profit for trade)
- Not in XDSMA 2 DEV: do not take a position in 20D SMA 2*standart deviation band
Session management:
- Not in session: Session start and end times can be defined here. If you do not want to trade in certain time intervals, mark that session.(helps to reduce slippage and get more realistic backtest results)
HHLL Strategy This is simple Highest high and Lowest low strategy.
Buy when break HH+offset
Sell when break LL+offset
Offset = (HH-LL)/2
Moving Average ChannelThe Moving Average Channel (MAC) is a concept developed by Jake Bernstein, Speaker at Wealth365®, where the 10 period SMA of the High and the 8 period SMA of the Low are plotted to create a channel. As the channel begins expanding, the current trend is getting stronger. However, when the expansion is too large, the trend may make a pullback to the channel (upper or lower), which act as support and resistance lines. This concept is the foundation for the Expansion Contraction Indicator (XC) developed by Brian Latta, Author of “The Book on Trading”.
Roger & Satchell Estimator Historical Volatility Bands [Loxx]Roger & Satchell Estimator Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using theRoger & Satchell Estimator Historical Volatility Bands for bands calculation.
What is Roger & Satchell Estimator Historical Volatility?
The Rogers–Satchell estimator does not handle opening jumps; therefore, it underestimates the volatility. It accurately explains the volatility portion that can be attributed entirely to a trend in the price evolution. Rogers and Satchell try to embody the frequency of price observations in the model in order to overcome the drawback. They claim that the corrected estimator outperforms the uncorrected one in a study based on simulated data.
RSEHV = sqrt((Z/n) * sum((log(high/close)*log(high/open)) + (log(low/close)*log(low/open))))
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Garman-Klass-Yang-Zhang Historical Volatility Bands [Loxx]Garman-Klass-Yang-Zhang Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Garman-Klass-Yang-Zhang Historical Volatility Bands for bands calculation.
What is Garman-Klass-Yang-Zhang Historical Volatility?
Yang and Zhang derived an extension to the Garman Klass historical volatility estimator that allows for opening jumps. It assumes Brownian motion with zero drift. This is currently the preferred version of open-high-low-close volatility estimator for zero drift and has an efficiency of 8 times the classic close-to-close estimator. Note that when the drift is nonzero, but instead relative large to the volatility, this estimator will tend to overestimate the volatility. The Garman-Klass-Yang-Zhang Historical Volatility calculation is as follows:
GKYZHV = sqrt((Z/n) * sum((log(open(k)/close(k-1)))^2 + (0.5*(log(high(k)/low(k)))^2) - (2*log(2) - 1)*(log(close(k)/open(2:end)))^2))
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Related Indicators
Garman & Klass Estimator Historical Volatility Bands
Garman & Klass Estimator Historical Volatility Bands [Loxx]Garman & Klass Estimator Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Garman & Klass Estimator Historical Volatility (instead of "regular" Historical Volatility ) for bands calculation.
What is Garman & Klaus Historical Volatility?
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. The Garman and Klass estimator for estimating historical volatility assumes Brownian motion with zero drift and no opening jumps (i.e. the opening = close of the previous period). This estimator is 7.4 times more efficient than the close-to-close estimator. Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate. Garman and Klass also assumed that the process of price change is a process of continuous diffusion (geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements. Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
The Garman & Klass Estimator is as follows:
GKE = sqrt((Z/n)* sum((0.5*(log(high./low)).^2) - (2*log(2) - 1).*(log(close./open)).^2))
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Related indicators:
Parkinson's Historical Volatility Bands
High/Low Historical Volatility Bands [Loxx]High/Low Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Historical Volatility high/low (instead of "regular" Historical Volatility) for bands calculation.
What is Historical Volatility?
Historical Volatility (HV) is a statistical measure of the dispersion of returns for a given security or market index over a given period of time. Generally, this measure is calculated by determining the average deviation from the average price of a financial instrument in the given time period. Using standard deviation is the most common, but not the only, way to calculate Historical Volatility .
The higher the Historical Volatility value, the riskier the security. However, that is not necessarily a bad result as risk works both ways - bullish and bearish , i.e: Historical Volatility is not a directional indicator and should not be used as other directional indicators are used. Use to to determine the rising and falling price change volatility .
SH is stock's High price in t day.
SL is stock's Low price in t day.
High/Low Return (xt^HL) is calculated as the natural logarithm of the ratio of a stock's High price to stock's Low price.
Return:
And Parkinson's number: 1 / (4 * math.log(2)) * 252 / n * Σ (n, t =1) {math.log(Ht/Lt)^2}
An important use of the Parkinson's number is the assessment of the distribution prices during the day as well as a better understanding of the market dynamics. Comparing the Parkinson's number and periodically sampled volatility helps traders understand the tendency towards mean reversion in the market as well as the distribution of stop-losses.
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Related indicators:
Parkinson's Historical Volatility Bands
Historical Volatility Bands
Parkinson's Historical Volatility Bands [Loxx]Parkinson's Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Parkinson's historical volatility (instead of "regular" Historical Volatility) for bands calculation.
What is Parkinson's Historical Volatility?
The Parkinson's number, or High Low Range Volatility developed by the physicist, Michael Parkinson in 1980, aims to estimate the Volatility of returns for a random walk using the High and Low in any particular period. IVolatility.com calculates daily Parkinson values. Prices are observed on a fixed time interval: n = 10, 20, 30, 60, 90, 120, 150, 180 days.
SH is stock's High price in t day.
SL is stock's Low price in t day.
High/Low Return (xt^HL) is calculated as the natural logarithm of the ratio of a stock's High price to stock's Low price.
Return:
And Parkinson's number: 1 / (4 * math.log(2)) * 252 / n * Σ (n, t =1) {math.log(Ht/Lt)^2}
An important use of the Parkinson's number is the assessment of the distribution prices during the day as well as a better understanding of the market dynamics. Comparing the Parkinson's number and periodically sampled volatility helps traders understand the tendency towards mean reversion in the market as well as the distribution of stop-losses.
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Historical Volatility Bands [Loxx]Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Historical Volatility for bands calculation.
What is Historical Volatility?
Historical Volatility (HV) is a statistical measure of the dispersion of returns for a given security or market index over a given period of time. Generally, this measure is calculated by determining the average deviation from the average price of a financial instrument in the given time period. Using standard deviation is the most common, but not the only, way to calculate Historical Volatility.
The higher the Historical Volatility value, the riskier the security. However, that is not necessarily a bad result as risk works both ways - bullish and bearish, i.e: Historical Volatility is not a directional indicator and should not be used as other directional indicators are used. Use to to determine the rising and falling price change volatility.
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
FDI-Adaptive Supertrend w/ Floating Levels [Loxx]FDI-Adaptive Supertrend w/ Floating Levels is a Fractal Dimension Index adaptive Supertrend indicator. This allows Supertrend to better adaptive to volatility of the market. This also includes floating levels that act as support and resistance, stop loss or take profit, or indication of market reversal. Additional signal types will be added in the future based on these floating levels.
What is the Fractal Dimension Index?
The goal of the fractal dimension index is to determine whether the market is trending or in a trading range. It does not measure the direction of the trend. A value less than 1.5 indicates that the price series is persistent or that the market is trending. Lower values of the FDI indicate a stronger trend. A value greater than 1.5 indicates that the market is in a trading range and is acting in a more random fashion.
What is the Supertrend?
Supertrend indicator was created by Olivier Seban to work on different time frames. It works for futures , forex, and equities. It is used in 15 minutes, hourly, weekly, and daily charts . Based on the parameters of multiplier and period, the indicator normally uses 3 for multiplier and 7 for the ATR period as default values. Average True Range is represented by the number of days while the multiplier is the value by which the range is multiplied.
Included:
Bar coloring
Alerts
Signals
Opening Range Breakout with Price TargetsJust publishing a version of the script amitgandhinz already created, which is amazing.
Added fib levels that amitgandhinz already started but commented out
Added mid point that is often found effective as a starting point, SL, etc
Price ProfileThe indicator shows number of candles present in the horizontal box areas for the given time window. You can set up:
1) Start time
2) Stop time
3) Number of horizontal bars
