Turtle Trader StrategyTurtle Trader Strategy :
Introduction :
This strategy is based on the well known « Turtle Trader Strategy », that has proven itself over the years. It sends long and short signals with pyramid orders of up to 5, meaning that the strategy can trigger up to 5 orders in the same direction. Good risk and money management.
It's important to note that the strategy combines 2 systems working together (S1 and S2). Let’s describe the specific features of this strategy.
1/ Position size :
Position size is very important for turtle traders to manage risk properly. This position sizing strategy adapts to market volatility and to account (gains and losses). It’s based on ATR (Average True Range) which can also be called « N ». Its length is per default 20.
ATR(20) = (previous_atr(20)*19 + actual_true_range)/20
The number of units to buy is :
Unit = 1% * account/(ATR(20)*dollar_per_point)
where account is the actual account value and dollar_per_point is the variation in dollar of the asset with a 1 point move.
Depending on your risk aversion, you can increase the percentage of your account, but turtle traders default to 1%. If you trade contracts, units must be rounded down by default.
There is also an additional rule to reduce the risk if the value of the account falls below the initial capital : in this case and only in this case, account in the unit formula must be replace by :
account = actual_account*actual_account/initial capital
2/ Open a position :
2 systems are working together :
System 1 : Entering a new 20 day breakout
System 2 : Entering a new 55 day breakout
A breakout is a new high or new low. If it’s a new high, we open long position and vice versa if it’s a new low we enter in short position.
We add an additional rule :
System 1 : Breakout is ignored if last long/short position was a winner
System 2 : All signals are taken
This additional rule allows the trader to be in the major trends if the system 1 signal has been skipped. If a signal for system 1 has been skipped, and next candle is also a new 20 day breakout, S1 doesn’t give a signal. We have to wait S2 signal or wait for a candle that doesn’t make a new breakout to reactivate S1.
3/ Pyramid orders :
Turtle Strategy allows us to add extra units to the position if the price moves in our favor. I've configured the strategy to allow up to 5 orders to be added in the same direction. So if the price varies from 0.5*ATR(20) , we add units with the position size formula. Note that the value of account will be replaced by "remaining_account", i.e. the cash remaining in our account after subtracting the value of open positions.
4/ Stop Loss :
We set a stop loss at 1.5*ATR(20) below the entry price for longs and above the entry price for shorts. If pyramid units are added, the stop is increased/decreased by 0.5*ATR(20). Note that if SL is configured for a loss of more than 10%, we set the SL to 10% for the first entry order to avoid big losses. This configuration does not work for pyramid orders as SL moves by 0.5*ATR(20).
5/ Exit signals :
System 1 :
Exit long on a 10 day low
Exit short on a 10 day high
System 2 :
Exit long on a 20 day low
Exit short on a 20 day high
6/ What types of orders are placed ?
To enter in a position, stop orders are placed meaning that we place orders that will be automatically triggered by the signal at the exact breakout price. Stop loss and exit signals are also stop orders. Pyramid orders are market orders which will be triggered at the opening of the next candle to avoid repainting.
PARAMETERS :
Risk % of capital : Percentage used in the position size formula. Default is 1%
ATR period : ATR length used to calculate ATR. Default is 20
Stop ATR : Parameters used to fix stop loss. Default is 1.5 meaning that stop loss will be set at : buy_price - 1.5*ATR(20) for long and buy_price + 1.5*ATR(20) for short. Turtle traders default is 2 but 1.5 is better for cryptocurrency as there is a huge volatility.
S1 Long : System 1 breakout length for long. Default is 20
S2 Long : System 2 breakout length for long. Default is 55
S1 Long Exit : System 1 breakout length to exit long. Default is 10
S2 Long Exit : System 2 breakout length to exit long. Default is 20
S1 Short : System 1 breakout length for short. Default is 15
S2 Short : System 2 breakout length for short. Default is 55
S1 Short Exit : System 1 breakout length to exit short. Default is 7
S2 Short Exit : System 2 breakout length to exit short. Default is 20
Initial capital : $1000
Fees : Interactive Broker fees apply to this strategy. They are set at 0.18% of the trade value.
Slippage : 3 ticks or $0.03 per trade. Corresponds to the latency time between the moment the signal is received and the moment the order is executed by the broker.
Pyramiding : Number of orders that can be passed in the same direction. Default is 5.
Important : Turtle traders don't trade crypto. For this specific asset type, I modify some parameters such as SL and Short S1 in order to maximize return while limiting drawdown. This strategy is the most optimal on BINANCE:BTCUSD in 1D timeframe with the parameters set per default. If you want to use this strategy for a different crypto please adapt parameters.
NOTE :
It's important to note that the first entry order (long or short) will be the largest. Subsequent pyramid orders will have fewer units than the first order. We've set a maximum SL for the first order of 10%, meaning that you won't lose more than 10% of the value of your first order. However, it is possible to lose more on your pyramid orders, as the SL is increased/decreased by 0.5*ATR(20), which does not secure a loss of more than 10% on your pyramid orders. The risk remains well managed because the value of these orders is less than the value of the first order. Remain vigilant to this small detail and adjust your risk according to your risk aversion.
Enjoy the strategy and don’t forget to take the trade :)
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RSI & Backed-Weighted MA StrategyRSI & MA Strategy :
INTRODUCTION :
This strategy is based on two well-known indicators that work best together: the Relative Strength Index (RSI) and the Moving Average (MA). We're going to use the RSI as a trend-follower indicator, rather than a reversal indicator as most are used to. To the signals sent by the RSI, we'll add a condition on the chart's MA, filtering out irrelevant signals and considerably increasing our winning rate. This is a medium/long-term strategy. There's also a money management method enabling us to reinvest part of the profits or reduce the size of orders in the event of substantial losses.
RSI :
The RSI is one of the best-known and most widely used indicators in trading. Its purpose is to warn traders when an asset is overbought or oversold. It was designed to send reversal signals, but we're going to use it as a trend indicator by increasing its length to 20. The RSI formula is as follows :
RSI (n) = 100 - (100 / (1 + (H (n)/L (n))))
With n the length of the RSI, H(n) the average of days closing above the open and L(n) the average of days closing below the open.
MA :
The Moving Average is also widely used in technical analysis, to smooth out variations in an asset. The SMA formula is as follows :
SMA (n) = (P1 + P2 + ... + Pn) / n
where n is the length of the MA.
However, an SMA does not weight any of its terms, which means that the price 10 days ago has the same importance as the price 2 days ago or today's price... That's why in this strategy we use a RWMA, i.e. a back-weighted moving average. It weights old prices more heavily than new ones. This will enable us to limit the impact of short-term variations and focus on the trend that was dominating. The RWMA used weights :
The 4 most recent terms by : 100 / (4+(n-4)*1.30)
The other oldest terms by : weight_4_first_term*1.30
So the older terms are weighted 1.30 more than the more recent ones. The moving average thus traces a trend that accentuates past values and limits the noise of short-term variations.
PARAMETERS :
RSI Length : Lenght of RSI. Default is 20.
MA Type : Choice between a SMA or a RWMA which permits to minimize the impact of short term reversal. Default is RWMA.
MA Length : Length of the selected MA. Default is 19.
RSI Long Signal : Minimum value of RSI to send a LONG signal. Default is 60.
RSI Short signal : Maximum value of RSI to send a SHORT signal. Default is 40.
ROC MA Long Signal : Maximum value of Rate of Change MA to send a LONG signal. Default is 0.
ROC MA Short signal : Minimum value of Rate of Change MA to send a SHORT signal. Default is 0.
TP activation in multiple of ATR : Threshold value to trigger trailing stop Take Profit. This threshold is calculated as multiple of the ATR (Average True Range). Default value is 5 meaning that to trigger the trailing TP the price need to move 5*ATR in the right direction.
Trailing TP in percentage : Percentage value of trailing Take Profit. This Trailing TP follows the profit if it increases, remaining selected percentage below it, but stops if the profit decreases. Default is 3%.
Fixed Ratio : This is the amount of gain or loss at which the order quantity is changed. Default is 400, which means that for each $400 gain or loss, the order size is increased or decreased by a user-selected amount.
Increasing Order Amount : This is the amount to be added to or subtracted from orders when the fixed ratio is reached. The default is $200, which means that for every $400 gain, $200 is reinvested in the strategy. On the other hand, for every $400 loss, the order size is reduced by $200.
Initial capital : $1000
Fees : Interactive Broker fees apply to this strategy. They are set at 0.18% of the trade value.
Slippage : 3 ticks or $0.03 per trade. Corresponds to the latency time between the moment the signal is received and the moment the order is executed by the broker.
Important : A bot has been used to test the different parameters and determine which ones maximize return while limiting drawdown. This strategy is the most optimal on BITSTAMP:ETHUSD with a timeframe set to 6h. Parameters are set as follows :
MA type: RWMA
MA Length: 19
RSI Long Signal: >60
RSI Short Signal : <40
ROC MA Long Signal : <0
ROC MA Short Signal : >0
TP Activation in multiple ATR : 5
Trailing TP in percentage : 3
ENTER RULES :
The principle is very simple:
If the asset is overbought after a bear market, we are LONG.
If the asset is oversold after a bull market, we are SHORT.
We have defined a bear market as follows : Rate of Change (20) RWMA < 0
We have defined a bull market as follows : Rate of Change (20) RWMA > 0
The Rate of Change is calculated using this formula : (RWMA/RWMA(20) - 1)*100
Overbought is defined as follows : RSI > 60
Oversold is defined as follows : RSI < 40
LONG CONDITION :
RSI > 60 and (RWMA/RWMA(20) - 1)*100 < -1
SHORT CONDITION :
RSI < 40 and (RWMA/RWMA(20) - 1)*100 > 1
EXIT RULES FOR WINNING TRADE :
We have a trailing TP allowing us to exit once the price has reached the "TP Activation in multiple ATR" parameter, i.e. 5*ATR by default in the profit direction. TP trailing is triggered at this point, not limiting our gains, and securing our profits at 3% below this trigger threshold.
Remember that the True Range is : maximum(H-L, H-C(1), C-L(1))
with C : Close, H : High, L : Low
The Average True Range is therefore the average of these TRs over a length defined by default in the strategy, i.e. 20.
RISK MANAGEMENT :
This strategy may incur losses. The method for limiting losses is to set a Stop Loss equal to 3*ATR. This means that if the price moves against our position and reaches three times the ATR, we exit with a loss.
Sometimes the ATR can result in a SL set below 10% of the trade value, which is not acceptable. In this case, we set the SL at 10%, limiting losses to a maximum of 10%.
MONEY MANAGEMENT :
The fixed ratio method was used to manage our gains and losses. For each gain of an amount equal to the value of the fixed ratio, we increase the order size by a value defined by the user in the "Increasing order amount" parameter. Similarly, each time we lose an amount equal to the value of the fixed ratio, we decrease the order size by the same user-defined value. This strategy increases both performance and drawdown.
Enjoy the strategy and don't forget to take the trade :)
Machine Learning: MFI Heat Map [YinYangAlgorithms]Overview:
MFI Heat Maps are a visually appealing way to display the values of 29 different MFIs at the same time while being able to make sense of it. Each plot within the Indicator represents a different MFI value. The higher you get up, the longer the length that was used for this MFI. This Indicator also features the use of Machine Learning to help balance the MFI levels. It doesn’t solely rely upon Machine Learning but instead incorporates a growing length MFI averaged with the Machine Learning MFI at any given index.
For instance, say we are calculating the 10th plot from the bottom, the MFI would be an average of:
MFI(source, 11)
Machine Learning MFI at Index of 10
We do it this way as they both help smooth each other out without relying solely on just one calculation method.
Due to plot limitations, you are capped at 28 Plot Amounts within this indicator, but that is still quite a bit of information you can glean from a Heat Map.
The Machine Learning used in this indicator is of the K-Nearest Neighbor (KNN). It uses a Fast and Slow MFI calculation then sorts through them over Machine Learning Length and calculates the differences between them. It then slices off KNN length to create our Max/Min Distances allotted. It adds the average between Fast and Slow MFIs to a Viable Distances array if their distances are within the KNN Min/Max distance. It then averages all distances in the Viable Distances array and returns the result.
The result of the KNN Function is saved to another ML Data array whose length is that of Plot Amount (Heat Map Size). This way each Index of the ML Data array can be indexed according to the Heat Map Size.
The Average of the ML Data array is the MFI line (white) that you’ll see plotted on the Indicator. There is also the SMA of the MFI Average (orange) which is likewise plotted. These plots allow you to visualize where the ML MFI is sitting and can potentially be useful for seeing when the MFI Average and SMA cross over and under each other.
We’ve heard many people talk highly of RSI, but sadly not too many even refer to MFI. MFI oftentimes may be overlooked, especially with new traders who may not even know what it is. Essentially MFI is an RSI but it also incorporates Volume into its calculations, which in our opinion leads to a more accurate reading; afterall, what is price movement without Volume.
Tutorial:
You may be thinking, this Indicator looks appealing to the eye, but how do I benefit from it trading wise?
Before we get into our visual examples, let's talk briefly about what makes Heat Maps in general a useful tool for trading. Heat Maps give us the ability to visualize and understand lots of data while removing the clutter. We can understand the data of 29 different MFIs without having to look at and decipher 29 different MFI plots. When you overlay too many MFI lines on top of each other, they can be very difficult to read and oftentimes end up actually hindering your Technical Analysis. For this reason, we have a simple solution to this problem; Heat Maps. This MFI Heat Map allows you to easily know (in a relative %) what the MFI level is for varying lengths. For Instance, the First (bottom) plot indexes an MFI of (K(0) (loop of Plot Amount) + Smoothing Length (default 1)) = 1. Since this is indexing (usually) a very low length, it will change much quicker. Whereas the Last (top) plot indexes an MFI of (K(27) (loop of Plot Amount) + Smoothing Length (default 1)) = 28. This is indexing a much higher length of MFI which results in the MFI the higher you go up in the Heat Map to move much slower.
Heat Maps give us the ability to see changes happening over multiple MFIs at the same time, which can be very useful for seeing shifts in MFI / Momentum. Remember, MFI incorporates Volume, so even if the price goes up a lot, if there was low volume, the MFI won’t move as much as an RSI would. However, likewise, if there is high volume but low price movement, the MFI will move slightly more than the RSI.
Heat Maps change color based on their MFI level. If the MFI is >= 90 it is HOT (red), if the MFI <= 9 it is COLD (teal, think of ICE). Green represents an MFI of 50-59 and Dark Blue represents an MFI of 40-49. Green and Dark blue are the most common colors as all the others are more ‘Extreme’ MFI levels.
Okay, time to get to the Examples :
Since there is so much going on in Heat Maps, we’ve decided to focus this tutorial to this specific area and talk about individual locations before talking about it as a whole.
If you refer to the example above where there are 2 white circles; these white circles are highlighting a key location you’ll be wanting to identify within your Heat Maps, many things are happening here:
The MFI crossed over the SMA (bullish).
The Heat Map started changing from mid/dark Blue (30-50 MFI) to Green (50-59 MFI) around the midline (the 50% dashed like).
The Lower levels of the Heat Map are turning Yellow/Orange/Red (60-100 MFI).
The Upper Levels of the Heat Map are still Light Blue - Green (10-50 MFI).
The 4 Key points above, all point towards potential Bullish Momentum changes. You’re likely wondering, but why? Let's discuss about each one in more specific detail:
1. The MFI crossed over the SMA (bullish): What this tells us is that the current MFI Average is now greater than its average over the last (default) 16 bars. This means there's been a large amount of Money Flow (Price and Volume) recently (subjectively based on the last (default) 16 average). This is one of the leading Bullish / Bearish signals you will see within this Indicator. You can enable Signals within the Settings and/or even add Alerts for when these crossings occur.
2. The Heat Map started changing from mid/dark Blue (30-50 MFI) to Green (50-59 MFI) around the midline (the 50% dashed like): This shows us that the index’s in the mid (if using all 28 heat map plots it would be at 14) has already received some of this momentum change. If you look at the second white circle (right), you’ll also notice the higher MFI plot indexes are also green. This is because since their length is long they still have some momentum and strength from the first white circle (left). Just because the first white circle failed in its bullish push, doesn’t mean it didn’t achieve momentum that would later on help to push the price up.
3. The Lower levels of the Heat Map are turning Yellow/Orange/Red (60-100 MFI): It occurred somewhat in the left white circle, but mainly in the right white circle. This shows us the MFI is very high on the lower lengths, this may lead to the current, middle and higher length MFIs following suit soon. Remember it has to work its way up, the higher levels can’t go red unless the lower levels go red first and the higher levels can also lag quite a bit behind and take awhile to catch up, this is normal, expected and meant to happen. Vice versa is also true with getting higher levels to go cold (light teal (think of ICE)).
4. The Upper Levels of the Heat Map are still Light Blue - Green (10-50 MFI): You might think at first that this is a bad thing, but it's not! Remember you want to be Fearful when others are Greedy and Greedy when others are Fearful! You don’t want to buy when the higher levels have a high MFI, you want to buy when you see the momentum pushing up in the lower MFI levels (getting yellow/orange/red in the low levels) while it is still Cold in the higher levels (BLUE OR GREEN, nothing higher than green as it is already slightly too high). There will be many times that it is Yellow or possibly Orange in the high levels and the bullish push still happens, but this is much more risky! The key to trading is to minimize risks while maximizing potential.
Hopefully now you’re getting an idea of how to spot potential bullish momentum changes, but what about bearish momentum changes? Technically they are the exact opposite, so we don’t need to go into as much detail, but lets still take a look at a few examples:
In the example above we marked the 3 times where it was displaying overly bullish characteristics. We marked the bullish momentum occurring with arrows. If you look closely at the start of the arrow to where it finishes, you’ll notice how the heat (HOT)(RED) works its way up from the lower levels to the higher levels. We then see the MFI to SMA cross under. In all 3 of these examples the heat made it all the way to the top of the chart. These are all very bearish signals that represent a bearish momentum movement that may occur soon.
Also, please note, the level the MFI is at DOES matter! That line isn’t there simply for you to see when there are crosses over and under. The MFI is considered to be Overbought when it is greater than 70 (the upper white dashed line, it is just formatted to be on a different scale cause there are 28 plots, but it represents 70). The MFI is considered to be Oversold when it is less than 30 (the lower white dashed line).
If we look to the left a little here where a big drop in price occurred shortly after our MFI and SMA crossed, would we have been able to identify it using the Heat Maps? Likely, No. There was some color change in the lower levels a few bars prior that went yellow/orange/red but before this cross happened they all went back to Dark Blue. In the middle section when the cross happened it was only Green and Yellow and in the upper section we are Blue. This would be a very risky trade to go on as the only real Bearish Indication was the MFI to SMA cross under. Remember, you want to reduce risk, you don’t want to simply trade on everytime the MFI and SMA cross each other or you’ll be getting yourself into many risky trades based on false signals.
Based on what you’ve learned above, can you see the signs that are indicating where this white circle may have potential for a bullish momentum change?
Now that we are more zoomed in, you may also be noticing there are colors to the price bars. This can be disabled in the settings, but just so you know what they mean, let’s zoom in a little more and talk about it.
We’ve condensed the Indicator a bit so you can see the bars better here. The colors that are displayed on these bars are the Heat Map value for your MFI (the white line in the Indicator). This way you can better see when the Price is Hot and Cold. As you may see while looking, the colors generally go from cold to hot when bullish momentum is happening and hot to cold when bearish momentum is happening. We don’t recommend solely looking at the bars as indicators to MFI momentum change, as seeing the Heat Map will give you much more data; however it can be nice to see the Heat Map projected on the bars rather than trying to eyeball it yourself or hover over each bar specifically to see their levels.
We will conclude our Tutorial here. Hopefully this has given you some insight to how useful Heat Maps can be and why it works well with a Machine Learning (KNN) Model applied to the MFI.
PLEASE NOTE: You can adjust the line width for the Heat Map within the settings. If you condense the Indicator a lot or have a small screen, likely use a length of 1-2. If you have it stretched out or a large screen, a length of 2-3 will work nice. You just don’t want to have the lines overlapping or it defeats the purpose of a Heat Map. Also, the bigger the linewidth, generally you’ll want to increase the Transparency within the Settings also as it can get quite bright and hurt your eyes over time.
Settings:
MFI:
Show MFI and SMA Crossing Signals: MFI and SMA Crossing is one of the leading Bullish and Bearish Signals in this Indicator. You can also add alerts for these signals.
Plot Amount: How many plots are used in this Heat Map. (2 - 28).
Source: The Source to use in all MFI calculations.
Smooth Initial MFI Length: How much to smooth the Fast and Slow MFI calculation by. 1 = No smoothing.
MFI SMA Length: What length we smooth the MFI Average over to get our MFI SMA.
Machine Learning:
Average MFI data by adding a lookback to the Source: While populating our Heat Map with the MFI's, should use use the Source each MFI Length increase or should we also lookback a Source each MFI Length Increase.
KNN Distance Requirement: To be a valid KNN, it needs to abide by a Distance calculation. Generally only Max is used, but you can change it if it suits your trading style better.
Machine Learning Length: How much ML data should we store? The longer the length generally the smoother the result; which may not be as accurate for something like a Heat Map, so keeping this relatively low may lead to more accurate results.
KNN Length: How many KNN are used in the slice to calculate max/min distance allowed.
Fast Length: Fast MFI length used in KNN to calculate distances by comparing its distance with the Slow MFI Length.
Slow Length: Slow MFI length used in KNN to calculate distances by comparing its distance with the Fast MFI Length.
Smoothing Length: When populating our Heat Map, at what length do we start our MFI calculations with (A Higher value with result in a slower and more smoothed MFI / Heat Map).
Colors:
Change Bar Color: Change bar colors to MFI Avg Color.
Heat Map Transparency: If there isn't any transparency it can be a little hard on the eyes. The Greater the Line Width, generally the more transparency you'll want for your eyes.
Line Width: Set how wide the Heat Map lines are
MFI 90-100 Color: Color when the MFI is between these levels.
MFI 80-89 Color: Color when the MFI is between these levels.
MFI 70-79 Color: Color when the MFI is between these levels.
MFI 60-69 Color: Color when the MFI is between these levels.
MFI 50-59 Color: Color when the MFI is between these levels.
MFI 40-49 Color: Color when the MFI is between these levels.
MFI 30-39 Color: Color when the MFI is between these levels.
MFI 20-29 Color: Color when the MFI is between these levels.
MFI 10-19 Color: Color when the MFI is between these levels.
MFI 0-100 Color: Color when the MFI is between these levels.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Adaptive MACD [LuxAlgo]The Adaptive MACD indicator is an adaptive version of the popular Moving Average Convergence Divergence (MACD) oscillator, returning longer-term variations during trending markets and cyclic variations during ranging markets while filtering out noisy variations.
🔶 USAGE
The proposed oscillator contains all the elements within a regular MACD, such as a signal line and histogram. A MACD value above 0 would indicate up-trending variations, while a value under 0 would be indicating down-trending variations.
Just like most oscillators, our proposed Adaptive MACD is able to return divergences with the price.
As we can see in the image above ranging markets will make the Adaptive MACD more conservative toward more cyclical conservations, filtering out both noise and longer-term variations. However, when longer-term variations (such as in a trending market) are prominent the oscillator will conserve longer-term variations.
The R2 Period setting determines when trending/ranging markets are detected, with higher values returning indications for longer intervals.
The fast and slow settings will act similarly to the regular MACD, however, closer values will return more cyclical outputs.
The image above compares our proposed MACD (top) with a regular MACD (bottom), both using fast = 19 and slow = 20 .
🔶 DETAILS
It is common to be solely interested in the trend component when the market is trending, however, during a ranging market it is more common to observe a more prominent cyclical/noise component. We want to be able to preserve one of the components at the appropriate market conditions, however, the regular MACD lack the ability to preserve cyclical component with high accuracy.
The MACD is an IIR bandpass filter. In order to obtain a lower passband bandwidth and a more symmetrical magnitude response (which would allow to conserve more precise cyclical variations) we can directly change the system calculation:
y = (price - price ) × g + ((1 - a1) + (1 - a2)) × y - (1 - a1) × (1 - a2) × y
where:
a1 = 2/(fast + 1)
a2 = 2/(slow + 1)
g = a1 - a2
Using division instead of multiplication on the second feedback weight allows further weighting the 2 samples lagged output, returning a more desirable magnitude response with a higher degree of filtering on both ends of the spectrum as shown in the image below:
We are interested in conserving cycles during ranging markets, and longer-term variations during trending markets, we can do this by interpolating between our two filter coefficients:
α × + (1 - α) ×
where 1 > α > 0 . α is measuring if the market is trending or ranging, with values closer to 1 indicating a trending market. We see that for higher values of α the original coefficient of the MACD is used. The image below shows various magnitude responses given multiple values of α :
We use a rolling R-Squared as α , this measurement has the benefit of indicating if the market is trending or ranging, as well as being constrained within range (0, 1), and having a U-shaped distribution.
If you are interested to learn more about the MACD see:
🔶 SETTINGS
R2 Period: Calculation window of the R-Squared.
Fast: Fast period for the calculation of the Adaptive MACD, lower values will return more noisy results.
Slow: Slow period for the calculation of the Adaptive MACD, higher values will return result with longer-term conserved variations.
Signal: Period of the EMA applied to the Adaptive MACD.
Normalized Adaptive Trend Lines [MAMA and FAMA]These indicators was originally developed by John F. Ehlers (Stocks & Commodities V. 19:10: MESA Adaptive Moving Averages). Everget wrote the initial functions for these in pine script. I have simply normalized the indicators and chosen to use the Laplace transformation instead of the hilbert transformation
How the Indicator Works:
The indicator employs a series of complex calculations, but we'll break it down into key steps to understand its functionality:
LaplaceTransform: Calculates the Laplace distribution for the given src input. The Laplace distribution is a continuous probability distribution, also known as the double exponential distribution. I use this because of the assymetrical return profile
MESA Period: The indicator calculates a MESA period, which represents the dominant cycle length in the price data. This period is continuously adjusted to adapt to market changes.
InPhase and Quadrature Components: The InPhase and Quadrature components are derived from the Hilbert Transform output. These components represent different aspects of the price's cyclical behavior.
Homodyne Discriminator: The Homodyne Discriminator is a phase-sensitive technique used to determine the phase and amplitude of a signal. It helps in detecting trend changes.
Alpha Calculation: Alpha represents the adaptive factor that adjusts the sensitivity of the indicator. It is based on the MESA period and the phase of the InPhase component. Alpha helps in dynamically adjusting the indicator's responsiveness to changes in market conditions.
MAMA and FAMA Calculation: The MAMA and FAMA values are calculated using the adaptive factor (alpha) and the input price data. These values are essentially adaptive moving averages that aim to capture the current trend more effectively than traditional moving averages.
But Omar, why would anyone want to use this?
The MAMA and FAMA lines offer benefits:
The indicator offers a distinct advantage over conventional moving averages due to its adaptive nature, which allows it to adjust to changing market conditions. This adaptability ensures that investors can stay on the right side of the trend, as the indicator becomes more responsive during trending periods and less sensitive in choppy or sideways markets.
One of the key strengths of this indicator lies in its ability to identify trends effectively by combining the MESA and MAMA techniques. By doing so, it efficiently filters out market noise, making it highly valuable for trend-following strategies. Investors can rely on this feature to gain clearer insights into the prevailing trends and make well-informed trading decisions.
This indicator is primarily suppoest to be used on the big timeframes to see which trend is prevailing, however I am not against someone using it on a timeframe below the 1D, just be careful if you are using this for modern portfolio theory, this is not suppoest to be a mid-term component, but rather a long term component that works well with proper use of detrended fluctuation analysis.
Dont hesitate to ask me if you have any questions
Again, I want to give credit to Everget and ChartPrime!
ATR GOD Strategy by TradeSmart (PineConnector-compatible)This is a highly-customizable trading strategy made by TradeSmart, focusing mainly on ATR-based indicators and filters. The strategy is mainly intended for trading forex , and has been optimized using the Deep Backtest feature on the 2018.01.01 - 2023.06.01 interval on the EUR/USD (FXCM) 15M chart, with a Slippage value of 3, and a Commission set to 0.00004 USD per contract. The strategy is also made compatible with PineConnector , to provide an easy option to automate the strategy using a connection to MetaTrader. See tooltips for details on how to set up the bot, and check out our website for a detailed guide with images on how to automate the strategy.
The strategy was implemented using the following logic:
Entry strategy:
A total of 4 Supertrend values can be used to determine the entry logic. There is option to set up all 4 Supertrend parameters individually, as well as their potential to be used as an entry signal/or a trend filter. Long/Short entry signals will be determined based on the selected potential Supertrend entry signals, and filtered based on them being in an uptrend/downtrend (also available for setup). Please use the provided tooltips for each setup to see every detail.
Exit strategy:
4 different types of Stop Losses are available: ATR-based/Candle Low/High Based/Percentage Based/Pip Based. Additionally, Force exiting can also be applied, where there is option to set up 4 custom sessions, and exits will happen after the session has closed.
Parameters of every indicator used in the strategy can be tuned in the strategy settings as follows:
Plot settings:
Plot Signals: true by default, Show all Long and Short signals on the signal candle
Plot SL/TP lines: false by default, Checking this option will result in the TP and SL lines to be plotted on the chart.
Supertrend 1-4:
All the parameters of the Supertrends can be set up here, as well as their individual role in the entry logic.
Exit Strategy:
ATR Based Stop Loss: true by default
ATR Length (of the SL): 100 by default
ATR Smoothing (of the SL): RMA/SMMA by default
Candle Low/High Based Stop Loss: false by default, recent lowest or highest point (depending on long/short position) will be used to calculate stop loss value. Set 'Base Risk Multiplier' to 1 if you would like to use the calculated value as is. Setting it to a different value will count as an additional multiplier.
Candle Lookback (of the SL): 50 by default
Percentage Based Stop Loss: false by default, Set the stop loss to current price - % of current price (long) or price + % of current price (short).
Percentage (of the SL): 0.3 by default
Pip Based Stop Loss: Set the stop loss to current price - x pips (long) or price + x pips (short). Set 'Base Risk Multiplier' to 1 if you would like to use the calculated value as is. Setting it to a different value will count as an additional multiplier.
Pip (of the SL): 10 by default
Base Risk Multiplier: 4.5 by default, the stop loss will be placed at this risk level (meaning in case of ATR SL that the ATR value will be multiplied by this factor and the SL will be placed that value away from the entry level)
Risk to Reward Ratio: 1.5 by default, the take profit level will be placed such as this Risk/Reward ratio is met
Force Exiting:
4 total Force exit on custom session close options: none applied by default. If enabled, trades will close automatically after the set session is closed (on next candle's open).
Base Setups:
Allow Long Entries: true by default
Allow Short Entries: true by default
Order Size: 10 by default
Order Type: Capital Percentage by default, allows adjustment on how the position size is calculated: Cash: only the set cash amount will be used for each trade Contract(s): the adjusted number of contracts will be used for each trade Capital Percentage: a % of the current available capital will be used for each trade
ATR Limiter:
Use ATR Limiter: true by default, Only enter into any position (long/short) if ATR value is higher than the Low Boundary and lower than the High Boundary.
ATR Limiter Length: 50 by default
ATR Limiter Smoothing: RMA/SMMA by default
High Boundary: 1000 by default
Low Boundary: 0.0003 by default
MA based calculation: ATR value under MA by default, If not Unspecified, an MA is calculated with the ATR value as source. Only enter into position (long/short) if ATR value is higher/lower than the MA.
MA Type: RMA/SMMA by default
MA Length: 400 by default
Waddah Attar Filter:
Explosion/Deadzone relation: Not specified by default, Explosion over Deadzone: trades will only happen if the explosion line is over the deadzone line; Explosion under Deadzone: trades will only happen if the explosion line is under the deadzone line; Not specified: the opening of trades will not be based on the relation between the explosion and deadzone lines.
Limit trades based on trends: Not specified by default, Strong Trends: only enter long if the WA bar is colored green (there is an uptrend and the current bar is higher then the previous); only enter short if the WA bar is colored red (there is a downtrend and the current bar is higher then the previous); Soft Trends: only enter long if the WA bar is colored lime (there is an uptrend and the current bar is lower then the previous); only enter short if the WA bar is colored orange (there is a downtrend and the current bar is lower then the previous); All Trends: only enter long if the WA bar is colored green or lime (there is an uptrend); only enter short if the WA bar is colored red or orange (there is a downtrend); Not specified: the color of the WA bar (trend) is not relevant when considering entries.
WA bar value: Not specified by default, Over Explosion and Deadzone: only enter trades when the WA bar value is over the Explosion and Deadzone lines; Not specified: the relation between the explosion/deadzone lines to the value of the WA bar will not be used to filter opening trades.
Sensitivity: 150 by default
Fast MA Type: SMA by default
Fast MA Length: 10 by default
Slow MA Type: SMA
Slow MA Length: 20 by default
Channel MA Type: EMA by default
BB Channel Length: 20 by default
BB Stdev Multiplier: 2 by default
Trend Filter:
Use long trend filter 1: false by default, Only enter long if price is above Long MA.
Show long trend filter 1: false by default, Plot the selected MA on the chart.
TF1 - MA Type: EMA by default
TF1 - MA Length: 120 by default
TF1 - MA Source: close by default
Use short trend filter 1: false by default, Only enter long if price is above Long MA.
Show short trend filter 1: false by default, Plot the selected MA on the chart.
TF2 - MA Type: EMA by default
TF2 - MA Length: 120 by default
TF2 - MA Source: close by default
Volume Filter:
Only enter trades where volume is higher then the volume-based MA: true by default, a set type of MA will be calculated with the volume as source, and set length
MA Type: RMA/SMMA by default
MA Length: 200 by default
Date Range Limiter:
Limit Between Dates: false by default
Start Date: Jan 01 2023 00:00:00 by default
End Date: Jun 24 2023 00:00:00 by default
Session Limiter:
Show session plots: false by default, show market sessions on chart: Sidney (red), Tokyo (orange), London (yellow), New York (green)
Use session limiter: false by default, if enabled, trades will only happen in the ticked sessions below.
Sidney session: false by default, session between: 15:00 - 00:00 (EST)
Tokyo session: false by default, session between: 19:00 - 04:00 (EST)
London session: false by default, session between: 03:00 - 11:00 (EST)
New York session: false by default, session between: 08:00 - 17:00 (EST)
Trading Time:
Limit Trading Time: true by default, tick this together with the options below to enable limiting based on day and time
Valid Trading Days Global: 123567 by default, if the Limit Trading Time is on, trades will only happen on days that are present in this field. If any of the not global Valid Trading Days is used, this field will be neglected. Values represent days: Sunday (1), Monday (2), ..., Friday (6), Saturday(7) To trade on all days use: 123457
(1) Valid Trading Days: false, 123456 by default, values represent days: Sunday (1), Monday (2), ..., Friday (6), Saturday(7) The script will trade on days that are present in this field. Please make sure that this field and also (1) Valid Trading Hours Between is checked
(1) Valid Trading Hours Between: false, 1800-2000 by default, hours between which the trades can happen. The time is always in the exchange's timezone
All other options are also disabled by default
PineConnector Automation:
Use PineConnector Automation: false by default, In order for the connection to MetaTrader to work, you will need do perform prerequisite steps, you can follow our full guide at our website, or refer to the official PineConnector Documentation. To set up PineConnector Automation on the TradingView side, you will need to do the following:
1. Fill out the License ID field with your PineConnector License ID;
2. Fill out the Risk (trading volume) with the desired volume to be traded in each trade (the meaning of this value depends on the EA settings in Metatrader. Follow the detailed guide for additional information);
3. After filling out the fields, you need to enable the 'Use PineConnector Automation' option (check the box in the strategy settings);
4. Check if the chart has updated and you can see the appropriate order comments on your chart;
5. Create an alert with the strategy selected as Condition, and the Message as {{strategy.order.comment}} (should be there by default);
6. Enable the Webhook URL in the Notifications section, set it as the official PineConnector webhook address and enjoy your connection with MetaTrader.
License ID: 60123456789 by default
Risk (trading volume): 1 by default
NOTE! Fine-tuning/re-optimization is highly recommended when using other asset/timeframe combinations.
Statistics TableThis script display some useful Statistics data that can be useful in making trading decision.
Here the list of information this script is display in table format.
You can change each and every single ema and rs length as per your need from setting.
1) close difference from first ema
2) close difference from second ema
3) close difference from third ema
4) close difference from fourth ema
5) difference between first and second ema
6) difference between second and third ema
7) difference between first and third ema
8) volume up down ratio
9) ATR/ADR %
10) volume pocket pivot count
11) daily closing range
12) weekly closing range
13) close difference from 52week high
14) close difference from 52week low
15) close difference from All time high
16) close difference from All time low
17) rs line above or below first rs ema
18) rs line above or below second rs ema
19) rs line above or below third rs ema
20) rs line above or below fourth rs ema
21) first rs value
22) second rs value
23) third rs value
24) fourth rs value
25) difference between previous first rs length days change % and current first rs length days change %
26) difference between previous second rs length days change % and current second rs length days change %
27) difference between previous third rs length days change % and current third rs length days change %
Recessions & crises shading (custom dates & stats)Shades your chart background to flag events such as crises or recessions, in similar fashion to what you see on FRED charts. The advantage of this indicator over others is that you can quickly input custom event dates as text in the menu to analyse their impact for your specific symbol. The script automatically labels, calculates and displays the peak to through percentage corrections on your current chart.
By default the indicator is configured to show the last 6 US recessions. If you have custom events which will benefit others, just paste the input string in the comments below so one can simply copy/paste in their indicator.
Example event input (No spaces allowed except for the label name. Enter dates as YYYY-MM-DD.)
2020-02-01,2020-03-31,COVID-19
2007-12-01,2009-05-31,Subprime mortgages
2001-03-01,2001-10-30,Dot-com bubble
1990-07-01,1991-03-01,Oil shock
1981-07-01,1982-11-01,US unemployment
1980-01-01,1980-07-01,Volker
1973-11-01,1975-03-01,OPEC
Bollinger Bands - Breakout StrategyThe Bollinger Bands - Breakout Strategy is a trend-following optimized for short-term trading in the crypto market. This strategy employs the Bollinger Bands, a widely recognized technical indicator, as its primary instrument for pinpointing potential trades. It is capable of executing both long and short positions, depending on whether the market is in a spot or futures, and is particularly effective in trending markets.
The strategy boasts a high degree of configurability, allowing users to set the Bollinger Bands period and deviation, trend filter, volatility filter, trade direction filter, rate of change filter, and date filter. Furthermore, it offers options for Take Profit, Stop Loss, and Trailing Stop for both long and short positions, ensuring a comprehensive risk management approach. The inclusion of a maximum intraday loss feature adds another layer of protection, making this strategy a valuable tool for traders seeking a professional and adaptable trading system.
Name : Bollinger Bands - Breakout Strategy
Category : Trend Follower based on Bollinger Bands
Operating mode : Long and Short on Futures or Long on Spot
Trade duration : Intraday
Timeframe : 2H, 3H, 4H, 5H
Market : Crypto
Suggested usage : Trending Markets
Entry : When the price crosses above or below the Bollinger Bands
Exit : Opposite Cross or Profit target, Trailing stop or Stop loss
Configuration :
- Bollinger Bands period and deviation
- Trend Filter
- Volatility Filter
- Trade direction filter
- Rate of Change filter
- Date Filter (for backtesting purposes)
- Take Profit, Stop Loss and Trailing Stop for long and short positions
- Risk Management: Max Intraday Loss
Backtesting :
⁃ Exchange: BINANCE
⁃ Pair: BTCUSDT.P
⁃ Timeframe: 4H
⁃ Fee: 0.025%
⁃ Slippage: 1
- Initial Capital: 10000 USDT
- Position sizing: 10% of Equity
- Start : 2019-09-19 (Out Of Sample from 2022-12-23)
- Bar magnifier: on
Credits :
- LucF of Pine Coders for f_security function to avoid repainting using security.
- QuantNomad for Monthly Table.
Disclaimer : Risk Management is crucial, so adjust stop loss to your comfort level. A tight stop loss can help minimise potential losses. Use at your own risk.
How you or we can improve? Source code is open so share your ideas!
Leave a comment and smash the boost button!
Thanks for your attention, happy to support the TradingView community.
Lorentzian Classification Strategy Based in the model of Machine learning: Lorentzian Classification by @jdehorty, you will be able to get into trending moves and get interesting entries in the market with this strategy. I also put some new features for better backtesting results!
Backtesting context: 2022-07-19 to 2023-04-14 of US500 1H by PEPPERSTONE. Commissions: 0.03% for each entry, 0.03% for each exit. Risk per trade: 2.5% of the total account
For this strategy, 3 indicators are used:
Machine learning: Lorentzian Classification by @jdehorty
One Ema of 200 periods for identifying the trend
Supertrend indicator as a filter for some exits
Atr stop loss from Gatherio
Trade conditions:
For longs:
Close price is above 200 Ema
Lorentzian Classification indicates a buying signal
This gives us our long signal. Stop loss will be determined by atr stop loss (white point), break even(blue point) by a risk/reward ratio of 1:1 and take profit of 3:1 where half position will be closed. This will be showed as buy.
The other half will be closed when the model indicates a selling signal or Supertrend indicator gives a bearish signal. This will be showed as cl buy.
For shorts:
Close price is under 200 Ema
Lorentzian Classification indicates a selling signal
This gives us our short signal. Stop loss will be determined by atr stop loss (white point), break even(blue point) by a risk/reward ratio of 1:1 and take profit of 3:1 where half position will be closed. This will be showed as sell.
The other half will be closed when the model indicates a buying signal or Supertrend indicator gives a bullish signal. This will be showed as cl sell.
Risk management
To calculate the amount of the position you will use just a small percent of your initial capital for the strategy and you will use the atr stop loss or last swing for this.
Example: You have 1000 usd and you just want to risk 2,5% of your account, there is a buy signal at price of 4,000 usd. The stop loss price from atr stop loss or last swing is 3,900. You calculate the distance in percent between 4,000 and 3,900. In this case, that distance would be of 2.50%. Then, you calculate your position by this way: (initial or current capital * risk per trade of your account) / (stop loss distance).
Using these values on the formula: (1000*2,5%)/(2,5%) = 1000usd. It means, you have to use 1000 usd for risking 2.5% of your account.
We will use this risk management for applying compound interest.
> In settings, with position amount calculator, you can enter the amount in usd of your account and the amount in percentage for risking per trade of the account. You will see this value in green color in the upper left corner that shows the amount in usd to use for risking the specific percentage of your account.
> You can also choose a fixed amount, so you will have to activate fixed amount in risk management for trades and set the fixed amount for backtesting.
Script functions
Inside of settings, you will find some utilities for display atr stop loss, break evens, positions, signals, indicators, a table of some stats from backtesting, etc.
You will find the settings for risk management at the end of the script if you want to change something or trying new values for other assets for backtesting.
If you want to change the initial capital for backtest the strategy, go to properties, and also enter the commisions of your exchange and slippage for more realistic results.
In risk managment you can find an option called "Use leverage ?", activate this if you want to backtest using leverage, which means that in case of not having enough money for risking the % determined by you of your account using your initial capital, you will use leverage for using the enough amount for risking that % of your acount in a buy position. Otherwise, the amount will be limited by your initial/current capital
I also added a function for backtesting if you had added or withdrawn money frequently:
Adding money: You can choose how often you want to add money (Monthly, yearly, daily or weekly). Then a fixed amount of money and activate or deactivate this function
Withdraw money: You can choose if you want to withdraw a fixed amount or a percentage of earnings. Then you can choose a fixed amount of money, the period of time and activate or deactivate this function. Also, the percentage of earnings if you choosed this option.
Some other assets where strategy has worked
BTCUSD 4H, 1D
ETHUSD 4H, 1D
BNBUSD 4H
SPX 1D
BANKNIFTY 4H, 15 min
Some things to consider
USE UNDER YOUR OWN RISK. PAST RESULTS DO NOT REPRESENT THE FUTURE.
DEPENDING OF % ACCOUNT RISK PER TRADE, YOU COULD REQUIRE LEVERAGE FOR OPEN SOME POSITIONS, SO PLEASE, BE CAREFULL AND USE CORRECTLY THE RISK MANAGEMENT
Do not forget to change commissions and other parameters related with back testing results!. If you have problems loading the script reduce max bars back number in general settings
Strategies for trending markets use to have more looses than wins and it takes a long time to get profits, so do not forget to be patient and consistent !
Please, visit the post from @jdehorty called Machine Learning: Lorentzian Classification for a better understanding of his script!
Any support and boosts will be well received. If you have any question, do not doubt to ask!
Vigilant Asset Allocation G4 Backtesting EngineThis script was based off of an idea that @CubanEmissary had so the description and some of the code that @CubanEmissary built on TradingView was used.
Vigilant Asset Allocation G4 (VAA G4) is a dual-momentum based investment strategy that aggressively monitors the market and reallocates portfolio funds based on the relative momentums of user-defined risk assets and safety assets. It was created by Wouter Keller and JW Keuning, based on their paper "Breadth Momentum and Vigilant Asset Allocation." In contrast to traditional dual momentum strategies, VAA G4 monitors the market itself through the two asset types. When all risk assets have positive momentum, the portfolio is allocated entirely into the risk asset with the strongest momentum At any other time, the portfolio is allocated entirely into the safety asset with the strongest momentum. The combination of breadth momentum with a very defensive reallocation trigger results in a strategy which captures alpha consistently.
The Strategy Rules:
1. Calculate each asset's momentum score on each monthly close:
momentumScore = (12*(currentMonthlyClose/lastMonthlyClose))+(4*(currentMonthlyClose/thirdLastMonthlyClose))+(2*(currentMonthlyClose/sixthLastMonthlyClose))+(currentMonthlyClose/twelvethLastMonthlyClose)-19
2. If all risk asset momentums are positive, allocate entire portfolio to the risk asset with the strongest momentum.
3. If any risk asset's momentum is negative, allocate entire portfolio to the safety asset with the strongest momentum.
4. Reevaluate at the end of each month.
Caveats:
1. It seems like TradingView only has limited price data for these tickers that are listed in the strategy. So it is best to start the strategy when they all have ample data (~ June 2nd, 2008)
2. This backtesting engine is basic and doesn't account for slippage and trading fees. So I implemented a basic "trading fee" input that will subtract a trading fee whenever the strategy makes a trade at the end of the month.
3. It is assumed in this engine that the trades will be made the exact second a new monthly bar opens up.
4. MUST USE ON MONTHLY CHART. It is hard-coded to work on monthly chart, if you open it on a daily chart , the Sharpe, Sortino, & CAGR calculations might not be right as well as the momentum score
Global Unemployment RateThe Global Unemployment rate estimates what is the rate of unemployment of the whole world, taking same data from multiple countries and normalizing them.
It includes the Eurozone + 19 countries from all the continents, which are some of the richest countries as well as some of the most populous.
It sadly does not include India as its unemployment data on trading view as of today (Feb 2023) is only since year 2019, which is too short
Dominant Cycle Detection OscillatorThis is a Dominant Cycle Detection Oscillator that searches multiple ranges of wavelengths within a spectrum. Choose one of 4 different dominant cycle detection methods (MESA MAMA cycle, Pearson Autocorrelation, Discreet Fourier Transform, and Phase Accumulation) to determine the most dominant cycles and see the historical results. Straight lines can indicate a steady dominant cycle; while Wavy lines might indicate a varying dominant cycle length. The steadier the cycle, the easier it may be to predict future events in that cycle (keep the log scale in mind when considering steadiness). The presence of evenly divisible (or harmonic) cycle lengths may also indicate stronger cycles; for example, 19, 38, and 76 dominant lengths for the 2x, 4x, and 8x cycles. Practically, a trader can use these cycle outputs as the default settings for other Hurst/cycle indicators. For example, if you see dominant cycle oscillator outputs of 38 & 76 for the 4x and 8x cycle respectively, you might want to test/use defaults of 38 & 76 for the 4x & 8x lengths in the bandpass, diamond/semi-circle notation, moving average & envelope, and FLD instead of the defaults 40 & 80 for a more fine-tuned analysis.
Muting the oscillator's historical lines and overlaying the indicator on the chart can visually cue a trader to the cycle lengths without taking up extra panes. The DFT Cycle lengths with muted historical lines have been overlayed on the chart in the photo.
The y-axis scale for this indicator's pane (just the oscillator pane, not the chart) most likely needs to be changed to logarithmic to look normal, but it depends on the search ranges in your settings. There are instructions in the settings. In the photo, the MESA MAMA scale is set to regular (not logarithmic) which demonstrates how difficult it can be to read if not changed.
In the Spectral Analysis chapter of Hurst's book Profit Magic, he recommended doing a Fourier analysis across a spectrum of frequencies. Hurst acknowledged there were many ways to do this analysis but recommended the method described by Lanczos. Currently in this indicator, the closest thing to the method described by Lanczos is the DFT Discreet Fourier Transform method.
Shoutout to @lastguru for the dominant cycle library referenced in this code. He mentioned that he may add more methods in the future.
Market Crashes/Chart Timeframes HighlightThis extremely helpful indicator allows you to highlight 7 custom date-based timeframes on your charts.
The default dates selected are what I consider to be the most significant 7 most recent market declines, including and since the 87 flash crash.
Note: The default dates are approximate but good enough to highlight the key timeframes of these pullbacks/crashes/corrections.
It's simple to use and does exactly what it should.
I created this indicator to make it easier when looking at the overall story of a chart. I found it helpful to highlight these areas to see how a market or equity has responded during these significant market pullbacks.
The highlight alone I’ve found helpful, and it becomes more powerful if you combine it with your own trusted trade system.
Also, to get the most out of using the default dates it’s important to understand the narrative behind each pullback/crash. Here’s the list of what I consider significant pullbacks:
Black Monday - Oct 87
1990s Recession - Jul 90 to Mar 91
Dot Com Bubble - 2000 to 2002 or so
Real Estate 2008 Crisis - I choose 2007-2009 to cover full insider knowledge and aftermath
2016 - 2018 - This isn't seen as a pullback, but I have it as significant because in many markets and equities, this was an almost equal percentage pullback as 2008. See Notes below
2020 Crash - Covid-19 and related shenanigans pullback
April 2021 to August 2022 - I believe we are in a current SHORT cycle so I've highlighted April 2021 as the start of what might be the start of a major decline testing Dot Com or lower levels.
A few notes on the above.
You'll find on most of the pullbacks listed above most equities and related markets behave similarly or have similar patterns.
The 2016-18 pullback is the most difficult to track. For instance, GE in this timeframe had a -80% decline, whereas BA depending on how you want to measure it had a 50-110% gain.
TREND RBReduce Fake Signals, and identify the trend and use as double confirmation
How Does it work ?
- it calculates main trend direction by using the length that is user-defined. so you can change it as you wish
- then it calculates trend direction for each 9 lower lengths. if you set the length = 20 then the lengths are 19, 18,...11
- and it checks if the trend directions that came from lower lengths is same or not with main trend direction.
- it changes the trend color of the ribbon.
A_HMS_EMAsthis is an indicator that have many essentials in one indicator and prevent from adding many indicator in chart
Ichimoko clouds and 4 ema from number 19 , 33 , 66 , 199
and an spatial average ema with 2 color that help you to find trend
Histomentu is a great Indicator that combine RSI Composit by RSi line and show momentum of price movement by a histogram
for better use note that:
red line is composite line
green line is rsi line
when composite line run away from histogram momemtum increase rapidly
when composite and rsi line is in same way its time to get position .
some futures is hidden by default:
composite red and green signal line
rmi of price with to color with momentum 4
ema 13, 33 of rmi as signal line
finaly u can change any colors from setting
in background we determine some fills for better use of Indicator
for better use note that:
1_pivots show with tiny triangles bellow and above the candles
red triangles for hi pivots and green triangles for low povots
2_ema19 , 66 , 199 shown on chart by default and you can change its source or length from settings
3- ichimoko cloud help you to determine demand and supply zones that those points are price target to go.
Bitcoin Golden Pi CyclesTops are signaled by the fast top MA crossing above the slow top MA, and bottoms are signaled by the slow bottom MA crossing above the fast bottom MA. Alerts can be set on top and bottom prints. Does not repaint.
Similar to the work of Philip Swift regarding the Bitcoin Pi Cycle Top, I’ve recently come across a similar mathematically curious ratio that corresponds to Bitcoin cycle bottoms. This ratio was extracted from skirmantas’ Bitcoin Super Cycle indicator . Cycle bottoms are signaled when the 700D SMA crosses above the 137D SMA (because this indicator is closed source, these moving averages were reverse-engineered). Such crossings have historically coincided with the January 2015 and December 2018 bottoms. Also, although yet to be confirmed as a bottom, a cross occurred June 19, 2022 (two days prior to this article)
The original pi cycle uses the doubled 350D SMA and the 111D SMA . As pointed out this gives the original pi cycle top ratio:
350/111 = 3.1532 ≈ π
Also, as noted by Swift, 111 is the best integer for dividing 350 to approximate π. What is mathematically interesting about skirmanta’s ratio?
700/138 = 5.1095
After playing around with this for a while I realized that 5.11 is very close to the product of the two most numerologically significant geometrical constants, π and the golden ratio, ϕ:
πϕ = 5.0832
However, 138 turns out to be the best integer denominator to approximate πϕ:
700/138 = 5.0725 ≈ πϕ
This is what I’ve dubbed the Bitcoin Golden Pi Bottom Ratio.
In the spirit of numerology I must mention that 137 does have some things going for it: it’s a prime number and is very famously almost exactly the reciprocal of the fine structure constant (α is within 0.03% of 1/137).
Now why 350 and 700 and not say 360 and 720? After all, 360 is obviously much more numerologically significant than 350, which is proven by the fact that 360 has its own wikipedia page, and 350 does not! Using 360/115 and 720/142, which are also approximations of π and πϕ respectively, this also calls cycle tops and bottoms.
There are infinitely many such ratios that could work to approximate π and πϕ (although there are a finite number whose daily moving averages are defined). Further analysis is needed to find the range(s) of numerators (the numerator determines the denominator when maintaining the ratio) that correctly produce bottom and top signals.
Portfolio SummaryThe stocks in my portfolio has very poor performance since the FED hiked the rate.
since the covid-19, i came to the stock market and buy a stock that i ever heard the name which i don't even know its fundamental.
Some stock, i bought them at the hilltop (and sell at the bottom, hehe).
Everyone is a genius in a bull market ; and now i realized i am gonna loss money.
I have to do something, so i looked for an indicator that allow me to manage my portfolio.
unfortunately, i cannot find one that fit me.
so i make this indicator.
How you can get the benefit from this indicator
This indicator will summarize the gain/loss since you bought the stock.
it also include the dividend.
So that you can see how much you gain and loss, and make a decision as you wish.
How to use it
1. Input some neccessary data like ticker/stock (up to 20 tickers, yeah i know, it is too much), share, average price which you can input manually or depend on your entry date.
2. The indicator will calculate gain/loss, dividend, % of your portfolio.
3. You can select how to display based on "Index" or "Market Value".
4. You can toggle on/off both label and table.
Disclaimer
This indicator may display inaccurate and need a lot of improvement.
pls use carefully.
if you have any suggestion, pls dont hersitate to comment below.
Enjoy.
GMC Trend FilterAvoid Fake Signals, and identify the trend.
How it works ?
- it calculates main trend direction by using the length that is user-defined. so you can change it as you wish
- then it calculates trend direction for each 9 lower lengths. if you set the length = 20 then the lengths are 19, 18,...11
- and it checks if the trend directions that came from lower lengths is same or not with main trend direction.
- it changes the trend color of the ribbon.
Simple Daily Weekly Monthly YearlySimple price line of the previous daily, weekly, monthly, and yearly opens and closes.
By default your will get the open and close of the last period of the time frame. If you're in March, you will get the open and close of Febraury. If it is calendary day 20, you will get the open and close of day 19 (or previous market day).
Script can be modified to see the most recent opens and closes by switching the period on a desired time range to 0. This will make so that if you're in March, you will get the open of March and it's most recent close price even if the month isn't over.
Colors can be changed to your liking via the settings for the indicator.
To show the price labels on your y-axis, go to Main Chart Settings -> Scales -> "Last Value Indicator Label" and click the checkbox. Also, might be helpful to check the "Indicator Name Label" under the same settings.
The line plots need to be completely transparent, otherwise they will show up and crowd the price chart.
MathConstantsElectromagneticLibrary "MathConstantsElectromagnetic"
Mathematical Constants
ElementaryCharge() Elementary Electron Charge: e = 1.602176487e-19 (2007 CODATA)
MagneticFluxQuantum() Magnetic Flux Quantum: theta_0 = h/(2*e) (2007 CODATA)
ConductanceQuantum() Conductance Quantum: G_0 = 2*e^2/h (2007 CODATA)
JosephsonConstant() Josephson Constant: K_J = 2*e/h (2007 CODATA)
VonKlitzingConstant() Von Klitzing Constant: R_K = h/e^2 (2007 CODATA)
BohrMagneton() Bohr Magneton: mu_B = e*h_bar/2*m_e (2007 CODATA)
NuclearMagneton() Nuclear Magneton: mu_N = e*h_bar/2*m_p (2007 CODATA)
McClellan Oscillator Jack Corsellis [LazyBear Modified]Developed by Sherman and Marian McClellan, the McClellan Oscillator is a breadth indicator derived from Net Advances, the number of advancing issues less the number of declining issues.
Subtracting the 39-day exponential moving average of Net Advances from the 19-day exponential moving average of Net Advances forms the oscillator.
As the formula reveals, the McClellan Oscillator is a momentum indicator that works similar to MACD .
McClellan Oscillator signals can be generated with breadth thrusts, centerline crossovers, overall levels and divergences.
The original code was written by LazyBear ( code here: )
We have modified this code:
1. Added the ability to display and configure the overbought and undersold lines which are key to properly using this indicator. These are set to default +40 and -75 respectively.
2. This version uses USI:ADV(advances- issues) and USI:DECL (declines - issues) as the default.
Optimized Keltner Channels SL/TP Strategy for BTCThis strategy is optimized for Bitcoin with the Keltner Channel Strategy, which is TradingView's built-in strategy. In the original Keltner Channel Strategy, it was difficult to predict the timing of entry because the Buy and Sell signals floated in the middle of the candle in real time. This strategy is convenient because if the bitcoin price hits the top or bottom of the Keltner Channel and closes the closing price, you can enter Buy or Sell at the next candle start price. In addition, this strategy provides Stop Loss and Take Profit functions to maximize profit.
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Recommended settings are below.
- length: 9
- multiplier: 1
- source: close
- (v) Use EMA
- Bands Style: Average True Range
- ATR Length: 19
- Stop Loss (%): 20
- Take Profit (%) : 20
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- length: 9
- multiplier: 1
- source: close
- (v) Use EMA
- Bands Style: Average True Range
- ATR Length: 18
- Stop Loss (%): 20
- Take Profit (%) : 5
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▶ Usefulness and Originality
- Stop Loss and Take Profit functions are available
- Convenient Buy and Sell entry compared to the original Keltner Channel Strategy
- Optimized for BTCUSD market (maximizing profits)
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이 전략은 TradingView의 Built-in 전략인 Keltner Channel Strategy를 비트코인에 맞게 최적화되었습니다. 기존의 Keltner Channel Strategy는 Buy, Sell 신호가 캔들 중간에 실시간으로 떠서 진입 시점을 예측하기 어려운 불편함이 있었지만 이 전략은 비트코인 가격이 Keltner Channel 상단 혹은 하단을 찍고 종가를 마감하면 그 다음 캔들 시작가에서 Buy 혹은 Sell 진입이 가능하여 편리합니다. 또한, 이 전략은 Keltner Channel을 만나서 캔들을 마감한 가격 (bprice, sprice)을 시각적으로 plot을 제공하여 타점 및 차트를 보기에 편리하며 손절가 및 목표가를 지정한 백테스팅이 가능합니다.
