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Hampel FilterHampel Filter script.
This indicator was originally developed by Frank Rudolf Hampel (Journal of the American Statistical Association, 69, 382–393, 1974: The influence curve and its role in robust estimation).
The Hampel filter is a simple but effective filter to find outliers and to remove them from data. It performs better than a median filter.
Moving Average Stop and Reverse (MASAR) [cI8DH]This indicator is an alternative to Parabolic Stop and Reverse indicator. It is primarily used to identify points of potential stops and reverses.
Instead of using a static parabolic curve, this indicator adjusts dynamically based on the changes in moving average of the price. Read here to learn more about the usage of this indicator .
I tested the strategy version of this indicator on Bitstamp:BTCUSD and compared the results to the Parabolic SAR. I changed the settings on both indicators to achieve the best results on each indicator. This indicator outperformed the Parabolic SAR by a large margin.
You need to calibrate the indicator depending on the asset and time frame. It works best in trending markets.
Inverse Fisher Transform on STOCHASTIC (modified graphics)Modified the graphic representation of the script from John Ehlers - From California, USA, he is a veteran trader. With 35 years trading experience he has seen it all. John has an engineering background that led to his technical approach to trading ignoring fundamental analysis (with one important exception). John strongly believes in cycles. He’d rather exit a trade when the cycle ends or a new one starts. He uses the MESA principle to make predictions about cycles in the market and trades one hundred percent automatically.
In the show John reveals:
• What is more appropriate than trading individual stocks
• The one thing he relies upon in his approach to the market
• The detail surrounding his unique trading style
• What important thing underpins the market and gives every trader an edge
About INVERSE FISHER TRANSFORM:
The purpose of technical indicators is to help with your timing decisions to buy or sell. Hopefully, the signals are clear and unequivocal. However, more often than not your decision to pull the trigger is accompanied by crossing your fingers. Even if you have placed only a few trades you know the drill. In this article I will show you a way to make your oscillator-type indicators make clear black-or-white indication of the time to buy or sell. I will do this by using the Inverse Fisher Transform to alter the Probability Distribution Function (PDF) of your indicators. In the past12 I have noted that the PDF of price and indicators do not have a Gaussian, or Normal, probability distribution. A Gaussian PDF is the familiar bell-shaped curve where the long “tails” mean that wide deviations from the mean occur with relatively low probability. The Fisher Transform can be applied to almost any normalized data set to make the resulting PDF nearly Gaussian, with the result that the turning points are sharply peaked and easy to identify. The Fisher Transform is defined by the equation
1)
Whereas the Fisher Transform is expansive, the Inverse Fisher Transform is compressive. The Inverse Fisher Transform is found by solving equation 1 for x in terms of y. The Inverse Fisher Transform is:
2)
The transfer response of the Inverse Fisher Transform is shown in Figure 1. If the input falls between –0.5 and +0.5, the output is nearly the same as the input. For larger absolute values (say, larger than 2), the output is compressed to be no larger than unity. The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
MA Study: Different Types and More [NeoButane]A study of moving averages that utilizes different tricks I've learned to optimize them. Included is Bollinger Bands, Guppy (GMMA) and Super Guppy.
The method used to make it MtF should be more precise and smoother than regular MtF methods that use the security function. For intraday timeframes, each number represents each hour, with 24 equal to 1 day. For daily, 3 is 3 day, for weekly, 4 is the 4 weekly, etc. If you're on a higher timeframe than the one selected, the length will not change.
Log-space is used to make calculations work on many cryptos. The rules for color changing Guppy is changed to make it not as choppy on MAs other than EMA. Note that length does not affect SWMA and VWAP and source does not affect VWAP.
A short summary of each moving average can be found here: medium.com
List of included MAs:
ALMA: Arnaud Legoux
Double EMA
EMA: Exponential
Hull MA
KAMA: Kaufman Adaptive
Linear Regression Curve
LSMA: Least Squares
SMA: Simple
SMMA/RMA: Smoothed/Running
SWMA: Symm. Weighted
TMA: Triangular
Triple EMA
VWMA: Volume Weighted
WMA: Weighted
ZLEMA: Zero Lag
VWAP: Vol Weighted Average
Welles Wilder MA
Logistic CorrelationLogistic Correlation is a correlation oscillator using a logistic function.
A Logistic Function is a Sigmoid Function who stabilize the variance of data.The logistic function have the same function as the inverse fisher transform but with an advantage over it, the k constant can control the steepness of the curve, lowers k's will preserve the original form of the data while highers one will transform it into a more square shaped form.
10 k
20 k
Simple profitable trading strategyThis strategy has three components.
Philakones EMAs are a sequence of five fibonacci EMAs. They range from 55 candles (green) to 8 candles (red) in length. A strong trend or breakout is marked by the emas appearing in sequence of their length from 8 to 55 or vice versa. These EMAs are also used to signal an exit. Only two EMAs are used for exit signals - when the 13 EMA crosses over/under the 55 EMA.
RSI gives a bullish signal when 40 > rsi > 70. Exit signals are oversold (30) or overbought (70)
Stochastics give a bullish signal when stoch < 80 and an exit signal when > 95.
Results include 3 ticks of slippage and taker fees of .002. Provides a pretty smooth equity curve with a 73% win rate and beats buy and hold by than 10x (returns about 60x overall) since start of 2017.
XPloRR MA-Buy ATR-Trailing-Stop Long Term Strategy Beating B&HXPloRR MA-Buy ATR-MA-Trailing-Stop Strategy
Long term MA Trailing Stop strategy to beat Buy&Hold strategy
None of the strategies that I tested can beat the long term Buy&Hold strategy. That's the reason why I wrote this strategy.
Purpose: beat Buy&Hold strategy with around 10 trades. 100% capitalize sold trade into new trade.
My buy strategy is triggered by the EMA(blue) crossing over the SMA curve(orange).
My sell strategy is triggered by another EMA(lime) of the close value crossing the trailing stop(green) value.
The trailing stop value(green) is set to a multiple of the ATR(15) value.
ATR(15) is the SMA(15) value of the difference between high and low values.
Every stock has it's own "DNA", so first thing to do is find the right parameters to get the best strategy values voor EMA, SMA and Trailing Stop.
Then keep using these parameter for future buy/sell signals only for that particular stock.
Do the same for other stocks.
Here are the parameters:
Exponential MA: buy trigger when crossing over the SMA value (use values between 11-50)
Simple MA: buy trigger when EMA crosses over the SMA value (use values between 20 and 200)
Stop EMA: sell trigger when Stop EMA of close value crosses under the trailing stop value (use values between 8 and 16)
Trailing Stop #ATR: defines the trailing stop value as a multiple of the ATR(15) value
Example parameters for different stocks (Start capital: 1000, Order=100% of equity, Period 1/1/2005 to now):
BAR(Barco): EMA=11, SMA=82, StopEMA=12, Stop#ATR=9
Buy&HoldProfit: 45.82%, NetProfit: 294.7%, #Trades:8, %Profit:62.5%, ProfitFactor: 12.539
AAPL(Apple): EMA=12, SMA=45, StopEMA=12, Stop#ATR=6
Buy&HoldProfit: 2925.86%, NetProfit: 4035.92%, #Trades:10, %Profit:60%, ProfitFactor: 6.36
BEKB(Bekaert): EMA=12, SMA=42, StopEMA=12, Stop#ATR=7
Buy&HoldProfit: 81.11%, NetProfit: 521.37%, #Trades:10, %Profit:60%, ProfitFactor: 2.617
SOLB(Solvay): EMA=12, SMA=63, StopEMA=11, Stop#ATR=8
Buy&HoldProfit: 43.61%, NetProfit: 151.4%, #Trades:8, %Profit:75%, ProfitFactor: 3.794
PHIA(Philips): EMA=11, SMA=80, StopEMA=8, Stop#ATR=10
Buy&HoldProfit: 56.79%, NetProfit: 198.46%, #Trades:6, %Profit:83.33%, ProfitFactor: 23.07
I am very curious to see the parameters for your stocks and please make suggestions to improve this strategy.
Inverse Fisher Transform COMBO STO+RSI+CCIv2 by KIVANÇ fr3762A combined 3in1 version of pre shared INVERSE FISHER TRANSFORM indicators on RSI , on STOCHASTIC and on CCIv2 to provide space for 2 more indicators for users...
About John EHLERS:
From California, USA, John is a veteran trader. With 35 years trading experience he has seen it all. John has an engineering background that led to his technical approach to trading ignoring fundamental analysis (with one important exception).
John strongly believes in cycles. He’d rather exit a trade when the cycle ends or a new one starts. He uses the MESA principle to make predictions about cycles in the market and trades one hundred percent automatically.
In the show John reveals:
• What is more appropriate than trading individual stocks
• The one thing he relies upon in his approach to the market
• The detail surrounding his unique trading style
• What important thing underpins the market and gives every trader an edge
About INVERSE FISHER TRANSFORM:
The purpose of technical indicators is to help with your timing decisions to buy or
sell. Hopefully, the signals are clear and unequivocal. However, more often than
not your decision to pull the trigger is accompanied by crossing your fingers.
Even if you have placed only a few trades you know the drill.
In this article I will show you a way to make your oscillator-type indicators make
clear black-or-white indication of the time to buy or sell. I will do this by using the
Inverse Fisher Transform to alter the Probability Distribution Function ( PDF ) of
your indicators. In the past12 I have noted that the PDF of price and indicators do
not have a Gaussian, or Normal, probability distribution. A Gaussian PDF is the
familiar bell-shaped curve where the long “tails” mean that wide deviations from
the mean occur with relatively low probability. The Fisher Transform can be
applied to almost any normalized data set to make the resulting PDF nearly
Gaussian, with the result that the turning points are sharply peaked and easy to
identify. The Fisher Transform is defined by the equation
1)
Whereas the Fisher Transform is expansive, the Inverse Fisher Transform is
compressive. The Inverse Fisher Transform is found by solving equation 1 for x
in terms of y. The Inverse Fisher Transform is:
2)
The transfer response of the Inverse Fisher Transform is shown in Figure 1. If
the input falls between –0.5 and +0.5, the output is nearly the same as the input.
For larger absolute values (say, larger than 2), the output is compressed to be no
larger than unity . The result of using the Inverse Fisher Transform is that the
output has a very high probability of being either +1 or –1. This bipolar
probability distribution makes the Inverse Fisher Transform ideal for generating
an indicator that provides clear buy and sell signals.
Creator: John EHLERS
Coppock Curve with its own Moving AverageIt shows Coppock with its own moving average. (Yes, in a way, 3 moving averages.)
Advised to use :
for long term, certainly not for day-trade;
on daily charts;
not as a standalone indicator, helps to read RSI, Klinger, TSI, CCI, etc.
as momentum-signaling: crossing 0, inflection points, crossover
as a quasi-centered, quasi oscillator, but not proportional always.
Weakness: mourning period certainly not the same for everyone.
Fractal Regression Bands [DW]This study is an experimental regression curve built around fractal and ATR calculations.
First, Williams Fractals are calculated, and used as anchoring points.
Next, high anchor points are connected to negative sloping lines, and low anchor points to positive sloping lines. The slope is a specified percentage of the current ATR over the sampling period.
The median between the positive and negative sloping lines is then calculated, then the best fit line (linear regression) of the median is calculated to generate the basis line.
Lastly, a Golden Mean ATR is taken of price over the sampling period and multiplied by 1/2, 1, 2, and 3. The results are added and subtracted from the basis line to generate the bands.
Williams Fractals are included in the plots. The color scheme indicated whether each fractal is engulfing or non-engulfing.
Custom bar color scheme is included.
Inverse Fisher Transform on SMI (Stochastic Momentum Index)Inverse Fisher Transform on SMI (Stochastic Momentum Index)
About John EHLERS:
From California, USA, John is a veteran trader. With 35 years trading experience he has seen it all. John has an engineering background that led to his technical approach to trading ignoring fundamental analysis (with one important exception).
John strongly believes in cycles. He’d rather exit a trade when the cycle ends or a new one starts. He uses the MESA principle to make predictions about cycles in the market and trades one hundred percent automatically.
In the show John reveals:
• What is more appropriate than trading individual stocks
• The one thing he relies upon in his approach to the market
• The detail surrounding his unique trading style
• What important thing underpins the market and gives every trader an edge
About INVERSE FISHER TRANSFORM:
The purpose of technical indicators is to help with your timing decisions to buy or
sell. Hopefully, the signals are clear and unequivocal. However, more often than
not your decision to pull the trigger is accompanied by crossing your fingers.
Even if you have placed only a few trades you know the drill.
In this article I will show you a way to make your oscillator-type indicators make
clear black-or-white indication of the time to buy or sell. I will do this by using the
Inverse Fisher Transform to alter the Probability Distribution Function (PDF) of
your indicators. In the past12 I have noted that the PDF of price and indicators do
not have a Gaussian, or Normal, probability distribution. A Gaussian PDF is the
familiar bell-shaped curve where the long “tails” mean that wide deviations from
the mean occur with relatively low probability. The Fisher Transform can be
applied to almost any normalized data set to make the resulting PDF nearly
Gaussian, with the result that the turning points are sharply peaked and easy to
identify. The Fisher Transform is defined by the equation
1)
Whereas the Fisher Transform is expansive, the Inverse Fisher Transform is
compressive. The Inverse Fisher Transform is found by solving equation 1 for x
in terms of y. The Inverse Fisher Transform is:
2)
The transfer response of the Inverse Fisher Transform is shown in Figure 1. If
the input falls between –0.5 and +0.5, the output is nearly the same as the input.
For larger absolute values (say, larger than 2), the output is compressed to be no
larger than unity. The result of using the Inverse Fisher Transform is that the
output has a very high probability of being either +1 or –1. This bipolar
probability distribution makes the Inverse Fisher Transform ideal for generating
an indicator that provides clear buy and sell signals.
3 Linear Regression CurveFast 3LRC - 15/30/60 standard settings - 15/30 give a lot of noise, but give you a some time to prepare for the 60 to flip
Zero Lag MACD Enhanced - Version 1.2ENHANCED ZERO LAG MACD
Version 1.2
Based on ZeroLag EMA - see Technical Analysis of Stocks and Commodities , April 0.21% 2000
Original version by user Glaz. Thanks !
Ideas and code from @yassotreyo version.
Tweaked by Albert Callisto ( AC )
New features:
On request by a user, added symbols to show the histogram crossing, can be either circles, cross symbols, a vertical bar. Colors can be chosen. You can also set the distance between the main axis and the symbols which are shown along the signal curve path.
Weekly Stock Trend Trading Tool// Created by TheBullTrader, 2017.
// Hi everyone, welcome to my Weekly Trend Trading Tool with the 50 day and 200 day moving averages
// This indicator scores each stock/ index individually and scores them on a simple scale -1.5 to +1.5
// This indicator has 2 zones: green zone = bullish, and red zone = bearish
// There are 3 plots: green = 50 day sma, red = 200 day sma, and trend signal= teal
// Buying Signal is when the green plot crosses teal plot or AGGRESSIVE Buy = green plot beginning to curve up from bearish zone.
// Sell Signal is when the green plot enters the RED ZONE
// By using this indicator as described, it will help you pick stock bottoms and COULD GET YOU OUT OF A STOCK CRASH!
// Recommendations is to scan this indicator against the top 100 US stocks with a long stock history greater than 10 years.
// I usually find 5-10 really good deals every few months. Slow and Easy way to build wealth. **Thanks for reading**
Another Millionaire toolBack with another Millionaire tool script, put like a solid 12 minutes here curve fitting the moving averages. THIS WILL MAKE YOU A MILLIONAIRE. It is so easy, it makes one of the hardest industries very very easy. Works on any market. I'VE DECIDED TO SHARE THE SCRIPT AND MAKE IT PUBLIC SO WE CAN ALL BE RICH TOGETHER, MILLIONAIRES
Trading Pub ScriptCrazy curve fitting, but I'm just trying to practice writing strategies. If only it were actually this easy!
Drexel StrategyThis is my variation of the famous Drexel Strategy. I've modified a few of the things, for instance using a JMA smoothing average as well as the RSX calculation from LazyBear. This strategy does not repaint I do not think, when I called the high and low I used or calculated things from open. I believe I avoided curve fitting because I get similar results across most pairs. This works best on the 1hr and below time frames. Hope you get lots of use out of it.
RSI featuring MACD on the Relative Divergence IndexHello Traders,
This Indicator uses RSI output to form a MACDish type of indicator.
Raw RSI output is smoothed with a linear regression curve to form the indicator line.
The signal line is a simple moving average of the same output, the histogram or momentum is the difference between the signal and indicator line, just as MACD
The outer level lines are switched off in MACD modus, because they will 'compress' this indicator, removing them also allows the zero line to 'float'
If you change the length of this indicator you also have to re-adjust the outer level lines, if used.
I recommend this indicator especially on higher lengths (55 or 89) in so you won't get whipped out by a early cross-over or 'false' divergence.
Cheers Indicat...
Fisher Transform Indicator by Ehlers Backtest v 2.0 Market prices do not have a Gaussian probability density function
as many traders think. Their probability curve is not bell-shaped.
But trader can create a nearly Gaussian PDF for prices by normalizing
them or creating a normalized indicator such as the relative strength
index and applying the Fisher transform. Such a transformed output
creates the peak swings as relatively rare events.
Fisher transform formula is: y = 0.5 * ln ((1+x)/(1-x))
The sharp turning points of these peak swings clearly and unambiguously
identify price reversals in a timely manner.
For signal used zero.
You can change long to short in the Input Settings
Please, use it only for learning or paper trading. Do not for real trading.
