Net XMR Margin PositionTotal XMR Longs minus XMR Shorts in order to give you the total outstanding XMR margin debt.
ie: If 50,000 XMR has been longed, and 40,000 XMR has been shorted, then 50,000 has been bought, and 40,000 sold, leaving us with 10,000 XMR (net) remaining to be sold to give us an overall neutral margin position.
That isn't to say that the net margin position must move towards zero, but it is a sensible reference point, and historical net values may provide useful insights into the current circumstances.
"ha溢价率" için komut dosyalarını ara
Net NEO Margin PositionTotal NEO Longs minus NEO Shorts in order to give you the total outstanding NEO margin debt.
ie: If 500,000 NEO has been longed, and 400,000 NEO has been shorted, then 500,000 has been bought, and 400,000 sold, leaving us with 100,000 NEO (net) remaining to be sold to give us an overall neutral margin position.
That isn't to say that the net margin position must move towards zero, but it is a sensible reference point, and historical net values may provide useful insights into the current circumstances.
(Anyone know what category this script should be in?)
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.
Strenght and MomentumThe scope of this script is to measure momentum and strenght of EURO and DOLLAR using their indexes.
Forza (line) above 0 means EURO is stonger than DOLLAR
Momento (histogram) above 0 means EURO has a positive momentum against DOLLAR
The added value to see MACD and RSI directly on EURUSD chart is that indexes consider also other pairs so their RSI and MACD has a larger view on forex markets.
Script has also an option for multi timeframes.
I think that could be used as filters for LONG or SHORT positions in lower time frames.
XPloRR MA-Trailing-Stop StrategyXPloRR MA-Trailing-Stop Strategy
Long term MA-Trailing-Stop strategy with Adjustable Signal Strength 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 fast buy EMA (blue) crossing over the slow buy SMA curve (orange) and the fast buy EMA has a certain up strength.
My sell strategy is triggered by either one of these conditions:
the EMA(6) of the close value is crossing under the trailing stop value (green) or
the fast sell EMA (navy) is crossing under the slow sell SMA curve (red) and the fast sell EMA has a certain down strength.
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 the high and low values.
The scripts shows a lot of graphical information:
The close value is shown in light-green. When the close value is lower then the buy value, the close value is shown in light-red. This way it is possible to evaluate the virtual losses during the trade.
the trailing stop value is shown in dark-green. When the sell value is lower then the buy value, the last color of the trade will be red (best viewed when zoomed)(in the example, there are 2 trades that end in gain and 2 in loss (red line at end))
the EMA and SMA values for both buy and sell signals are shown as a line
the buy and sell(close) signals are labeled in blue
How to use this strategy?
Every stock has it's own "DNA", so first thing to do is tune the right parameters to get the best strategy values voor EMA , SMA, Strength for both buy and sell and the Trailing Stop (#ATR).
Look in the strategy tester overview to optimize the values Percent Profitable and Net Profit (using the strategy settings icon, you can increase/decrease the parameters)
Then keep using these parameters for future buy/sell signals only for that particular stock.
Do the same for other stocks.
Important : optimizing these parameters is no guarantee for future winning trades!
Here are the parameters:
Fast EMA Buy: buy trigger when Fast EMA Buy crosses over the Slow SMA Buy value (use values between 10-20)
Slow SMA Buy: buy trigger when Fast EMA Buy crosses over the Slow SMA Buy value (use values between 30-100)
Minimum Buy Strength: minimum upward trend value of the Fast SMA Buy value (directional coefficient)(use values between 0-120)
Fast EMA Sell: sell trigger when Fast EMA Sell crosses under the Slow SMA Sell value (use values between 10-20)
Slow SMA Sell: sell trigger when Fast EMA Sell crosses under the Slow SMA Sell value (use values between 30-100)
Minimum Sell Strength: minimum downward trend value of the Fast SMA Sell value (directional coefficient)(use values between 0-120)
Trailing Stop (#ATR): the trailing stop value as a multiple of the ATR(15) value (use values between 2-20)
Example parameters for different stocks (Start capital: 1000, Order=100% of equity, Period 1/1/2005 to now) compared to the Buy&Hold Strategy(=do nothing):
BEKB(Bekaert): EMA-Buy=12, SMA-Buy=44, Strength-Buy=65, EMA-Sell=12, SMA-Sell=55, Strength-Sell=120, Stop#ATR=20
NetProfit: 996%, #Trades: 6, %Profitable: 83%, Buy&HoldProfit: 78%
BAR(Barco): EMA-Buy=16, SMA-Buy=80, Strength-Buy=44, EMA-Sell=12, SMA-Sell=45, Strength-Sell=82, Stop#ATR=9
NetProfit: 385%, #Trades: 7, %Profitable: 71%, Buy&HoldProfit: 55%
AAPL(Apple): EMA-Buy=12, SMA-Buy=45, Strength-Buy=40, EMA-Sell=19, SMA-Sell=45, Strength-Sell=106, Stop#ATR=8
NetProfit: 6900%, #Trades: 7, %Profitable: 71%, Buy&HoldProfit: 2938%
TNET(Telenet): EMA-Buy=12, SMA-Buy=45, Strength-Buy=27, EMA-Sell=19, SMA-Sell=45, Strength-Sell=70, Stop#ATR=14
NetProfit: 129%, #Trade
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
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.
DepthHouse - Moving Average ChannelsThe indicator Moving Average Channels was created for experimental purposes due to the parabolic moves BTC has made in the recent past.
How it works:
The basis, or center line, is a standard moving average that is set by the user.
The bands are then a customizable percentage of the basis.
Which based on the settings, could serve as possible support and resistance.
DepthHouse – Moving Average Channels has been published for you all to see and try for yourselves.
Maybe this indicator has uses elsewhere? If you find something feel free to post it in the comments below!
If you like this indicator, please drop a like or comment!
They are very much appreciated!
Be sure to go to my profile and check out my other indicators!
OHLC Volatility Estimators by @Xel_arjonaDISCLAIMER:
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 embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is by Creative-Commons as TradingView's regulations. Any use, copy or re-use of this code should mention it's origin as it's authorship.
WARNING NOTICE!
THE INCLUDED FUNCTION MUST BE CONSIDERED AS DEBUGING CODE The models included in the function have been taken from openly sources on the web so they could have some errors as in the calculation scheme and/or in it's programatic scheme. Debugging are welcome.
WHAT'S THIS?
Here's a full collection of candle based (compressed tick) Volatility Estimators given as a function, openly available for free, it can print IMPLIED VOLATILITY by an external symbol ticker like INDEX:VIX.
Models included in the volatility calculation function:
CLOSE TO CLOSE: This is the classic estimator by rule, sometimes referred as HISTORICAL VOLATILITY and is the must common, accepted and widely used out there. Is based on traditional Standard Deviation method derived from the logarithm return of current close from yesterday's.
ELASTIC WEIGHTED MOVING AVERAGE: This estimator has been used by RiskMetriks®. It's calculation is based on an ElasticWeightedMovingAverage Standard Deviation method derived from the logarithm return of current close from yesterday's. It can be viewed or named as an EXPONENTIAL HISTORICAL VOLATILITY model.
PARKINSON'S: The Parkinson 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.
ROGERS-SATCHELL: The Rogers-Satchell function is a volatility estimator that outperforms other estimators when the underlying follows a Geometric Brownian Motion (GBM) with a drift (historical data mean returns different from zero). As a result, it provides a better volatility estimation when the underlying is trending. However, this Rogers-Satchell estimator does not account for jumps in price (Gaps). It assumes no opening jump. The function uses the open, close, high, and low price series in its calculation and it has only one parameter, which is the period to use to estimate the volatility.
YANG-ZHANG: Yang and Zhang were the first to derive an historical volatility estimator that has a minimum estimation error, is independent of the drift, and independent of opening gaps. This estimator is maximally 14 times more efficient than the close-to-close estimator.
LOGARITHMIC GARMAN-KLASS: The former is a pinescript transcript of the model defined as in iVolatility . The metric used is a combination of the overnight, high/low and open/close range. Such a volatility metric is a more efficient measure of the degree of volatility during a given day. This metric is always positive.
Fractals and Levels by JustUncleLEven though there are a many other Fractal and Level indicators, this indicator has some unique features. The indicator will display Fractals, fractal levels and HH/LL points, they will only be drawn after they have completed. Also the indicator has options to :
Show Ideal Fractals Only.
Use Renko Style Fractals, where open/close values are used instead of high/low to find Fractals. This is used to show the correct Fractals when Renko Wicks are enabled.
Has an optional Filter to only display Fractals that are above/below a MA Ribbon.
References:
This code is based on Fractal Levels V8 by RicardoSantos
This is a Renko Chart with "Renko Style Fractals" enabled, notice that the wicks are ignored and only the true Bricks are used for Fractals:
Adaptive Donchian ChannelThis indicator adds a level of adaptivity to the simple Donchian Channel by adjusting the sensitivity (lookback periods) of the channel's upper and lower bounds based on the amount of time that has elapsed since the price has hit/expanded the channel boundaries. Comparing the results of this indicator to the standard Donchian Channel, the readier level of responsiveness may prove self-evident.
METHODOLOGY:
Specifically, the more recently the channel was expanded in one direction, the longer the lookback period grows in that direction. Conversely, if the channel has not been expanded in a given direction, the lookback period will contract so as to allow for a tighter channel.
For example, let the initial lookback period be 20 bars and let the factor argument be 0.1 (or 2 bars to start, as 20*0.1 = 2). Now say the current bar sets a new 20-period high. Then the lookback period for the upper bound is expanded by 2 bars to 22, and the lookback period for the lower bound is contracted by 2 bars to 18, thereby making it simultaneously harder to set new highs and easier to set new lows (and vice versa for hitting new lows). If neither a new high nor a new low is formed, both periods contract by the given factor.
Guth_3X_ConfirmThis indicator has three built in indicators based on the SMA of HIGH, SMA of LOW, and Stochastic. The baseline indicator is the retreats after departures from SMA of HIGH and LOW.
The first time a HIGH that is above the SMA HIGH has a lower HIGH but it still above the SMA HIGH, a (-) will appear at the bottom. This signals an aggressive entry point for potential coming downtrend. The second time the HIGH produces a lower high but is still above the SMA HIGH, a (S) will appear at the bottom which signals a more conservative entry point for potential coming downtrend. All of the opposite information is true of reversals beyond the SMA LOW.
When these reversals appear the same time the Stochastic is overbought or oversold, a red bar (overbought and potentially coming down) or a green bar (oversold and potentially coming up) will appear. NOTE: Aggressive symbols occur more often and will always occur when a conservative symbol appears. When a conservative indicator and respective overbought/oversold level occur at the same time, the bar is darker in color.
You can enter positions at any one of the indicators, however, the darker bars are what I look for. This has a high success rate but cannot guarantee results every time. I recommend adjusting the SMA, and Stoch parameters as well as time periods. I have had success with this indicator while day trading the 5, 10, 15, 30, 65 minute periods as well as daily and weekly periods. Every symbol traded can provide differing results based on the parameters used.
Please feel free to leave feedback and I know this can work well for you!
AlphaAlpha is a measure of the active return on an investment, the performance of that investment compared to the S&P500 index, where 0.01 = 1%
alpha < 0: the investment has earned too little for its risk (or, was too risky for the return)
alpha = 0: the investment has earned a return adequate for the risk taken
alpha > 0: the investment has a return in excess of the reward for the assumed risk
Mister Transistor 3.0This is a general purpose very flexible program to test the effectiveness of HA bars.
Please note that if you are charting at tradingview using Heikin-Ashi charting, your system will be trading fictitious prices even if you check the "use real prices" box. Thought you might like to know that before you lose all your money.
This program performs the HA calcs internally thus allowing you to use HA bars on a standard bar chart and obtaining real prices for your trades.
Courtesy of Boffin Hollow Lab
Author: Tarzan the Ape Man
Trailing Sharpe RatioThe Sharpe ratio allows you to see whether or not an investment has historically provided a return appropriate to its risk level. A Sharpe ratio above one is acceptable, above 2 is good, and above 3 is excellent. A Sharpe ratio less than one would indicate that an investment has not returned a high enough return to justify the risk of holding it. Interesting in this example, SPY's one year avg Sharpe ratio is above 3. This would mean on average SPY returns 3x better returns than the risk associated with holding it, implying there is some sort of underlying value to the investment.
When the sharpe ratio is above its signal, this implies the investment is currently outperforming compared to its typical return, below the signal means the investment is currently under performing. A negative Shape would mean that the investment has not provided a positive return, and may be a possible short candidate.
Zweig Market Breadth Thrust Indicator [LazyBear]The Breadth Thrust (BT) indicator is a market momentum indicator developed by Dr. Martin Zweig. According to Dr. Zweig a Breadth Thrust occurs when, during a 10-day period, the Breadth Thrust indicator rises from below 40 percent to above 61.5 percent.
A "Thrust" indicates that the stock market has rapidly changed from an oversold condition to one of strength, but has not yet become overbought. This is very rare and has happened only a few times. Dr. Zweig also points out that most bull markets begin with a Breadth Thrust.
All parameters are configurable. You can draw BT for NYSE, NASDAQ, AMEX or based on combined data (i.e., AMEX+NYSE+NASD). There is also a "CUSTOM" mode supported, so you can enter your own ADV/DEC symbols.
More info:
Definition: www.investopedia.com
A Breadth Thrust Signal: www.mcoscillator.com
A Rare "Zweig" Buy Signal: www.moneyshow.com
Zweig Breadth Thrust: recessionalert.com
List of my public indicators: bit.ly
List of my app-store indicators: blog.tradingview.com
KK_Traders Dynamic Index_Bar HighlightingHey guys,
this is one of my favorite scripts as it represents a whole trading system that has given me very good results!
I have only used it on Bitcoin so far but I am sure it will also work for other instruments.
The original code to this was created by LazyBear, so all props to him for this great script!
I have linked his original post down below.
You can find the full rules to the system in this PDF (which has also been taken from LBs post):
www.forexmt4.com
Here is a short summary of the rules:
Go long when (all conditions have to be met):
The green line is above 50
The green line is above the red line
The green line is above the orange line
The close is above the upper Band of the Price Action Channel
The candles close is above its open
(The green line is below 68)
Go short when (all conditions have to be met):
The green line is below 50
The green line is below the red line
The green line is below the orange line
The close is below the lower band of the Price Action Channel
The candles close is below its open
(The green line is above 32)
Close when:
Any of these conditions aren't true anymore.
I have marked two of the rules in brackets as they seem to cut out a lot of the profits this system generates. You can choose to still use these rules by checking the box that says "Use Original Ruleset" in the options.
The system also contains rules regarding the Heiken Ashi bars. However these aren't as specific as the other rules. This is where your personal judgement comes in and this part is hard to explain. Take a look at the PDF I have linked to get a better understanding.
So far, this is just the TDI trading system and LBs script, now what have I changed?
I have incorporated the Price Action Channel to the system and changed it so that it highlights the bars whenever the system is giving a signal. As long as the bars are green the system is giving a long signal, as long as they are red the system is giving a short signal. Keep in mind that this doesn't consider the bar size of the HA bars. I recommend coloring all bars grey via the chart settings in order to be able to see the bar highlighting properly.
I have also published the Price Action Channel seperately in case some of you wish to view the Channel.
I am fairly new to creating scripts so use it with caution and let me know what you think!
LBs original post:
The seperate Price Action Channel script:
CM Stochastic POP Method 1 - Jake Bernstein_V1A good friend ucsgears recently published a Stochastic Pop Indicator designed by Jake Bernstein with a modified version he found.
I spoke to Jake this morning and asked if he had any updates to his Stochastic POP Trading Method. Attached is a PDF Jake published a while back (Please read for basic rules, which also Includes a New Method). I will release the Additional Method Tomorrow.
Jake asked me to share that he has Updated this Method Recently. Now across all symbols he has found the Stochastic Values of 60 and 30 to be the most profitable. NOTE - This can be Significantly Optimized for certain Symbols/Markets.
Jake Bernstein will be a contributor on TradingView when Backtesting/Strategies are released. Jake is one of the Top Trading System Developers in the world with 45+ years experience and he is going to teach how to create Trading Systems and how to Optimize the correct way.
Below are a few Strategy Results....Soon You Will Be Able To Find Results Like This Yourself on TradingView.com
BackTesting Results Example: EUR-USD Daily Chart Since 01/01/2005
Strategy 1:
Go Long When Stochastic Crosses Above 60. Go Short When Stochastic Crosses Below 30. Exit Long/Short When Stochastic has a Reverse Cross of Entry Value.
Results:
Total Trades = 164
Profit = 50, 126 Pips
Win% = 38.4%
Profit Factor = 1.35
Avg Trade = 306 Pips Profit
***Most Consecutive Wins = 3 ... Most Consecutive Losses = 6
Strategy 2:
Rules - Proprietary Optimization Jake Will Teach. Only Added 1 Additional Exit Rule.
Results:
Total Trades = 164
Profit = 62, 876 Pips!!!
Win% = 38.4%
Profit Factor = 1.44
Avg Trade = 383 Pips Profit
***Most Consecutive Wins = 3 ... Most Consecutive Losses = 6
Strategy 3:
Rules - Proprietary Optimization Jake Will Teach. Only added 1 Additional Exit Rule.
Results:
Winning Percent Increases to 72.6%!!! , Same Amount of Trades.
***Most Consecutive Wins = 21 ...Most Consecutive Losses = 4
Indicator Includes:
-Ability to Color Candles (CheckBox In Inputs Tab)
Green = Long Trade
Blue = No Trade
Red = Short Trade
-Color Coded Stochastic Line based on being Above/Below or In Between Entry Lines.
Link To Jakes PDF with Rules
dl.dropboxusercontent.com
We Are Witnessing A Historical Event With A Clear Outcome!!!"Full Disclosure: I came across this information from www.SentimenTrader.com
I have no financial affiliation…They provide incredible statistical facts on
The General Market, Currencies, and Futures. They offer a two week free trial.
I Highly Recommend.
The S&P 500 has gone 43 trading days without a 1% daily move, up or down.
which is the equivalent of two months and one day in trading days.
During this stretch, the S&P has gained more than 4%,
and it has notched a 52-week high recently as well.
Since 1952, there were nine other precedents. All of
these went 42 trading days without a 1% move, all of
them saw the S&P gain at least 4% during their streaks,
and all of them saw the S&P close at a 52-week highs.
***There was consistent weakness a week later, with only three
gainers, and all below +0.5%.
***After that, stocks did better, often continuing an Extraordinary move higher.
Charts can sometimes give us a better nuance than
numbers from a table, and from the charts we can see a
general pattern -
***if stocks held up well in the following
weeks, then they tended to do extremely well in the
months ahead.
***If stocks started to stumble after this two-
month period of calm, however, then the following months
tended to show a lot more volatility.
We already know we're seeing an exceptional market
environment at the moment, going against a large number
of precedents that argued for weakness here, instead of
the rally we've seen. If we continue to head higher in
spite of everything, these precedents would suggest that
we're in the midst of something that could be TRULY EXTRAORDINARY.
Trading Strategy based on BB/KC squeeze**** [Edit: New version (v02) posted, see the comments section for the code *****
Simple strategy. You only consider taking a squeeze play when both the upper and lower Bollinger Bands go inside the Keltner Channel. When the Bollinger Bands (BOTH lines) start to come out of the Keltner Channel, the squeeze has been released and a move is about to take place.
I have added more support indicators -- I highlight the bullish / bearish KC breaches (using GREEN/RED crosses) and a SAR to see where price action is trending.
Appreciate any feedback. Enjoy!
Color codes for v02:
----------------------------
When both the upper and lower Bollinger Bands go inside the Keltner Channel, the squeeze is on and is highlighted in RED.
When the Bollinger Bands (BOTH lines) start to come out of the Keltner Channel, the squeeze has been released and is highlighted in GREEN.
When one of the Bollinger Bands is out of Keltner Channel, no highlighting is done (this means, the background color shows up, so don't get confused if you have RED/GREEN in your chart's bground :))
Color codes for v01:
----------------------------
When both the upper and lower Bollinger Bands go inside the Keltner Channel, the squeeze is on and is highlighted in YELLOW.
When the Bollinger Bands (BOTH lines) start to come out of the Keltner Channel, the squeeze has been released and is highlighted in BLUE.
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio
A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent.
Table of contents
What the script does at a glance
Momentum, what it is, how to know if it is present
Volatility targeting, why and how it is done here
Portfolio construction modes: Long Only, Short Only, Delta Neutral
Regime filter and when the strategy goes to cash
Transaction cost modelling in this script
Backtest metrics and definitions
Performance attribution chart
Monte Carlo simulation
Scatter plot analysis modes
Asset allocation pie chart
Inputs, presets, and deployment checklist
Suggested workflow
1) What the script does at a glance
Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility.
Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter.
Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite.
Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views.
2) Momentum: definition, detection, and validation
Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints.
Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization.
How to know momentum is present .
Leaders and laggards persist across adjacent windows rather than flipping every bar.
Spread between average momentum of leaders and laggards is materially positive in sample.
Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak.
Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries.
Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime.
3) Volatility targeting: purpose and implementation here
Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk.
Computation in this script (per asset, rolling):
Return series ret = log(price/price ).
Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays).
Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0).
This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero.
Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations.
Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure.
4) Portfolio construction modes
Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties.
Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult.
Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs.
Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3).
Notes .
The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage.
If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta.
5) Regime filter
A fast EMA(12) vs EMA(21) filter gates exposure.
Long Only active when EMA12 > EMA21. Otherwise the book is set to cash.
Short Only active when EMA12 < EMA21. Otherwise cash.
Delta Neutral is always active.
This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar.
6) Transaction cost modelling
There are two cost touchpoints in the script.
Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar.
Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots.
Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders.
Practical advice .
If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue.
For crypto perp microcaps, stress higher cost assumptions and add slippage buffers.
If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag.
7) Backtest metrics and definitions
The script computes a standard set of portfolio statistics once the start date is reached.
Net Profit percent over the full test.
Max Drawdown percent, tracked from running peaks.
Annualized Mean and Stdev using the chosen trading day count.
Variance is the square of annualized stdev.
Sharpe uses daily mean adjusted by risk-free rate and annualized.
Sortino uses downside stdev only.
Omega ratio of sum of gains to sum of losses.
Gain-to-Pain total gains divided by total losses absolute.
CAGR compounded annual growth from start date to now.
Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM.
Skewness of daily returns.
VaR 95 linear-interpolated 5th percentile of daily returns.
CVaR average of the worst 5 percent of daily returns.
Benchmark Buy-and-Hold equity path for comparison.
8) Performance attribution
Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels.
9) Monte Carlo simulation
The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed.
Normal uses Box-Muller with your seed. Good for quick, smooth envelopes.
Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption.
Bands show 10th, 25th, 75th, 90th percentiles and the path mean.
10) Scatter plot analysis
Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels.
Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite.
Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations.
Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta.
Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk.
11) Asset allocation pie chart
Builds a wheel of current allocations.
Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent.
Short Only, weights show the short book as positive slices that sum to 100 percent.
Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional.
Labels can show asset, percent, and current leverage.
12) Inputs and quick presets
Core
Portfolio Strategy . Long Only, Short Only, Delta Neutral.
Initial Capital . For equity scaling in the panel.
Trading Days/Year . 252 for stocks, 365 for crypto.
Target Volatility . Annualized, drives volMult.
Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above.
Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier.
Start Date . Ensure every symbol has data back to this date to avoid bias.
Benchmark . Used for alpha, beta, and B&H line.
Diagnostics
Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill.
Attribution panel. Toggle winners only to focus on what matters.
Monte Carlo mode with Normal or Bootstrap and confidence bands.
Scatter plot type and styling, labels, and portfolio star.
Pie chart and labels for current allocation.
Presets
Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands.
Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward.
Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta.
13) Suggested workflow
Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees.
Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe.
Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity.
Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear.
Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it.
Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence.
Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier.
Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs.
Important implementation details mapped to code
Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting.
Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult.
Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively.
Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate.
Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison.
Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures.
Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds.
Final notes
Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call.
Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it.
Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
cd_sfp_CxGeneral:
This indicator is designed to assist users who trade the Swing Failure Pattern ( SFP ).
In technical literature (various definitions exist), an SFP is a situation where the price violates a previous swing level but fails to close beyond that level.
• (Liquidity Sweep)
• (Buyer or seller dominance)
• (Stop hunt)
• (Turtle Soup)
The general strategy is built upon seeking trade opportunities after an SFP is formed and conviction is established that the market direction has changed.
Components used to gather confirmation:
• Determining Bias: Periodic SAR
• Obtaining Breakout/Reversal Confirmation: Change in State Delivery (CISD)
• Defining the Buyer/Seller Block (Supply/Demand Zones): Mitg Blocks (Mitigation Blocks), FVG (Fair Value Gaps), and Standard Deviation Projection
• Key Levels: Previous HTF (Higher Time Frame) levels
• Setting Targets: Standard Deviation Projection
• Trade Management: Anchored VWAP and opposing blocks
• Time-Based Context: Session Killzone times
• Notifications: An alarm/alert system will be utilized to stay informed.
________________________________________
Details:
Swing and Swing Failure Pattern:
Swing Sweep Types (Liquidity Sweep):
1. Single
2. Consecutive (The liquidity of the entity that swept the liquidity is being swept)
Bias Determination
We need to filter out the numerous SFPs that occur across all time frames. Our first strong filter will be the Bias. We will only look for trades aligned with our bias.
We will use Periodic SAR (Stop and Reverse) to determine the bias. We compare the price with the SAR value from a Higher Time Frame than the one we are trading on.
• Price > SAR => Bullish Bias
• Price < SAR => Bearish Bias
Depending on the pair, H1 SAR may be chosen for scalp trades, and Daily/Weekly SAR for intraday and swing trades.
Key Levels
Strategies looking for trades after a liquidity grab generally state that the sweep / stop hunt movement should occur at a significant price level.
The most fundamental Key Level levels are (User can customize):
• Previous Week High & Low
• Previous Day High & Low
• Previous H4 High & Low
• Previous H1 High & Low
• Asia Killzone High & Low
• London Killzone High & Low
• New York Killzone High & Low
• Monday Range High & Low values
We will prefer SFP formations that occur when these levels are swept. When Key Levels are violated, an information label appears on the screen.
Blocks / Zones
To strengthen our hand, we will use three types of blocks/zones, either with Key Levels or separately. When an SFP structure is formed in these areas (along with bias and breakout confirmation), our expectation is for the price to continue in our desired direction. These regions are:
1. Mitigation Blocks (Mtg)
o (Details can be found in the cd_VWAP_mtg_Cx indicator)
o In short: A second candle, following a bullish candle, crosses its high but fails to close above it. We call this a sweep / SFP. When the price, which was expected to go to the low, instead makes a new high/close, an Mtg block is formed. (Buyers are dominant)
2. FVGs (Fair Value Gaps)
o We use classic FVG structures.
3. Standard Deviation Projection Boxes
o When we get an SFP structure + breakout confirmation (CISD), we use the Standard Deviation Projection to determine our profit-taking and take-profit levels.
o Based on the idea that the price often respects the range between -2 and -2.5 of the projection values, we box this range and use it as our area of interest. (Our expectation is for the price to reverse after reaching this target).
o Let's mark it on the chart.
Confirmation
To summarize what has been explained so far: we look for the price to form an SFP structure in levels/zones we deem important, aligned with our bias, and for the breakout to be confirmed with a CISD.
No single component is strong on its own, but the success rate increases when they occur together.
We observe the following as additional confirmation along with the CISD: a new Mtg block forming in the direction of the breakout, high-volume movement (with FVG and a large body), and respect for VWAPs, the resistance/support line, and the defense block.
Additional Confirmations with Breakouts:
• Defence block, new mtg and VWAP
• Resistance / Support Line:
Indicator Signals
The indicator marks all formed sweeps, selected key levels, blocks, the projection, and CISD confirmations on the screen. The candle where the CISD confirmation occurs is indicated by an arrow.
• Arrows with double short lines signify a CISD that follows an SFP occurring at a Key Level.
• All other CISD candle indications are shown with single-line arrows.
Trade Management
When selecting profit targets in trades (preferably), the projection, opposing blocks, and structures that have formed are taken into account. Do not neglect to look at the structures that have formed against you when entering a trade.
Menu Settings:
• For Mtg blocks, the trading timeframe or a higher timeframe can be selected.
• FVGs formed in the current timeframe are displayed when the price creates an SFP (in "Fvg" option).
• Deviation boxes are displayed when the price creates an SFP (in box).
• The SAR HTF setting (H1) for scalp trades may vary depending on the pair. Users trying trades on higher timeframes should increase the HTF setting.
o Example: If you are looking for a trade with an SFP structure on H1, the SAR HTF setting should be H4 or higher.
• VWAP lines are refreshed starting from the candle that executed the sweep when the price forms an SFP. The only setting to adjust is the source selection setting (hlc3 is selected).
• Time frames and Killzone / Special Zone settings for Key Levels can be changed/should be checked.
Alarms / Alerts:
The conditions that will trigger an alert can be selected from the menu.
• To receive an alert aligned with the bias, the "Alignment with bias" checkbox must be selected.
• The alert should be set on the timeframe where you plan to enter the trade.
• The display options do not affect the alarm conditions. (Example: FVGs are monitored even when the menu selection is "off").
• If the necessary conditions are met, the alarm is triggered on the new candle that opens after the CISD confirmation.
• The alarm will not be triggered more than once at the same Key Level.
The user can preferably select alerts:
• Bias-aligned or Bias-independent
• Sweep (without waiting for CISD)
• Sweep + CISD (without looking for other conditions)
• Sweep + Key Level + CISD (the swept level is a Key Level)
• Sweep + Mtg / Fvg / Dev. + CISD (SFP formed in any of the blocks)
• Sweep + Mtg + CISD (SFP formed in the Mtg block)
• Sweep + Fvg + CISD (SFP formed inside the FVG)
• Sweep + Deviation Box + CISD (SFP formed inside the Dev. Box)
• Sweep + Key Level + Mtg / Fvg / Dev. + CISD (SFP formed simultaneously at a Key Level and any of the blocks)
Trade Example:
• Conditions: Bias-aligned + Sweep + Mtg/Fvg/Dev (at least one) + CISD
• Extra Confirmations: Respect for the Defense Block + Respect for VWAP
• Target (TP): Projection between -2 and -2.5
I welcome your thoughts and suggestions regarding my indicator, which I believe will be successful in the long run by adhering to uncompromising risk management and a strict trading plan.
Happy Trading!
Quasimodo Pattern Strategy Back Test [TradingFinder] QM Trading🔵 Introduction
The QM pattern, also known as the Quasimodo pattern, is one of the popular patterns in price action, and it is often used by technical analysts. The QM pattern is used to identify trend reversals and provides a very good risk-to-reward ratio. One of the advantages of the QM pattern is its high frequency and visibility in charts.
Additionally, due to its strength, it is highly profitable, and as mentioned, its risk-to-reward ratio is very good. The QM pattern is highly popular among traders in supply and demand, and traders also use this pattern.
The Price Action QM pattern, like other Price Action patterns, has two types: Bullish QM and Bearish QM patterns. To identify this pattern, you need to be familiar with its types to recognize it.
🔵 Identifying the QM Pattern
🟣 Bullish QM
In the bullish QM pattern, as you can see in the image below, an LL and HH are formed. As you can see, the neckline is marked as a dashed line. When the price reaches this range, it will start its upward movement.
🟣 Bearish QM
The Price Action QM pattern also has a bearish pattern. As you can see in the image below, initially, an HH and LL are formed. The neckline in this image is the dashed line, and when the LL is formed, the price reaches this neckline. However, it cannot pass it, and the downward trend resumes.
🔵 How to Use
The Quasimodo pattern is one of the clearest structures used to identify market reversals. It is built around the concept of a structural break followed by a pullback into an area of trapped liquidity. Instead of relying on lagging indicators, this pattern focuses purely on price action and how the market reacts after exhausting one side of liquidity. When understood correctly, it provides traders with precise entry points at the transition between trend phases.
🟣 Bullish Quasimodo
A bullish Quasimodo forms after a clear downtrend when sellers start losing control. The market continues to make lower lows until a sudden higher high appears, signaling that buyers are entering with strength. Price then pulls back to retest the previous low, creating what is known as the Quasimodo low.
This area often becomes the final trap for sellers before the market shifts upward. A visible rejection or displacement from this zone confirms bullish momentum. Traders usually place entries near this level, stops below the low, and targets at previous highs or the next resistance zone. Combining the setup with demand zones or Fair Value Gaps increases its accuracy.
🟣 Bearish Quasimodo
A bearish Quasimodo forms near the top of an uptrend when buyers begin to lose strength. The market continues to make higher highs until a sudden lower low breaks the bullish structure, showing that selling pressure is entering the market. Price then retraces upward to retest the previous high, forming the Quasimodo high, where breakout buyers are often trapped.
Once rejection appears at this level, it indicates a likely reversal. Traders can enter short near this area, with stop-losses placed above the high and targets near the next support or previous lows. The setup gains more reliability when aligned with supply zones, SMT divergence, or bearish Fair Value Gaps.
🔵 Setting
Pivot Period : You can use this parameter to use your desired period to identify the QM pattern. By default, this parameter is set to the number 5.
Take Profit Mode : You can choose your desired Take Profit in three ways. Based on the logic of the QM strategy, you can select two Take Profit levels, TP1 and TP2. You can also choose your take profit based on the Reward to Risk ratio. You must enter your desired R/R in the Reward to Risk Ratio parameter.
Stop Loss Refine : The loss limit of the QM strategy is based on its logic on the Head pattern. You can refine it using the ATR Refine option to prevent Stop Hunt. You can enter your desired coefficient in the Stop Loss ATR Adjustment Coefficient parameter.
Reward to Risk Ratio : If you set Take Profit Mode to R/R, you must enter your desired R/R here. For example, if your loss limit is 10 pips and you set R/R to 2, your take profit will be reached when the price is 20 pips away from your entry point.
Stop Loss ATR Adjustment Coefficient : If you set Stop Loss Refine to ATR Refine, you must adjust your loss limit coefficient here. For example, if your buy position's loss limit is at the price of 1000, and your ATR is 10, if you set Stop Loss ATR Adjustment Coefficient to 2, your loss limit will be at the price of 980.
Entry Level Validity : Determines how long the Entry level remains valid. The higher the level, the longer the entry level will remain valid. By default it is 2 and it can be set between 2 and 15.
🔵 Results
The following examples show the backtest results of the Quasimodo (QM) strategy in action. Each image is based on specific settings for the symbol, timeframe, and input parameters, illustrating how the QM logic can generate signals under different market conditions. The detailed configuration for each backtest is also displayed on the image.
⚠ Important Note : Even with identical settings and the same symbol, results may vary slightly across different brokers due to data feed variations and pricing differences.
Default Properties of Backtests :
OANDA:XAUUSD | TimeFrame: 5min | Duration: 1 Year :
BINANCE:BTCUSD | TimeFrame: 5min | Duration: 1 Year :
CAPITALCOM:US30 | TimeFrame: 5min | Duration: 1 Year :
NASDAQ:QQQ | TimeFrame: 5min | Duration: 5 Year :
OANDA:EURUSD | TimeFrame: 5min | Duration: 5 Year :
PEPPERSTONE:US500 | TimeFrame: 5min | Duration: 5 Year :
