Open Interest StochasticStochastic Money Flow Index(MFI) using open interest instead of volume.
Open Interest data for Binance, Bitmex, and Kraken
Центральные осцилляторы
EMA and MACD with Trailing Stop Loss (by Coinrule)An exponential moving average ( EMA ) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average simple moving average ( SMA ), which applies an equal weight to all observations in the period.
Moving average convergence divergence ( MACD ) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average ( EMA ) from the 12-period EMA.
The result of that calculation is the MACD line. A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. Traders may buy the security when the MACD crosses above its signal line and sell—or short—the security when the MACD crosses below the signal line. Moving average convergence divergence ( MACD ) indicators can be interpreted in several ways, but the more common methods are crossovers, divergences, and rapid rises/falls.
The Strategy enters and closes the trade when the following conditions are met:
LONG
The MACD histogram turns bearish
EMA7 is greater than EMA14
EXIT
Price increases 3% trailing
Price decreases 1% trailing
This strategy is back-tested from 1 January 2022 to simulate how the strategy would work in a bear market and provides good returns.
Pairs that produce very strong results include XRPUSDT on the 1-minute timeframe. This short timeframe means that this strategy opens and closes trades regularly
In order to further improve the strategy, the EMA can be changed from 7 and 14 to, say, EMA20 and EMA50. Furthermore, the trailing stop loss can also be changed to ideally suit the user to match their needs.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
Percentile Rank of Moving Average Convergence DivergenceThis simple indicator provides you three useful information of the Moving Average Convergence Divergence (MACD) indicator:
The percentile rank of the current value of the MACD line, displayed by the bright blue line.
The percentile rank of the current value of the Signal line, displayed by the dark blue line.
The percentile rank of the current value of the Histogram line, displayed by the olive histogram.
This indicator can be useful to identify the strength of trend. This indicator makes the assumption that market tends to revert into the opposite direction. If the market has been trending a lot, it should consolidate for a while later. If the market has been consolidating for a long time, it would begin trending real soon.
When we see a low percentile rank, no matter which line it is, this tells that the market hasn't been moving much, or there is little momentum. If the percentile rank stays below the median or even below the first quartile for a long time, this could suggest that the market is ready for the next trend since it has stored quite some energy.
When we see a high percentile rank, no matter which line it is, this tells that the market has been trending a lot, or there is much momentum. If the percentile rank stays above the median or even above the third quartile for a long time, it is probable that the market has used up much of its energy and is going to take a rest (consolidate).
Divergence Cheat Sheet'Divergence Cheat Sheet' helps in understanding what to look for when identifying divergences between price and an indicator. The strength of a divergence can be strong, medium, or weak. Divergences are always most effective when references prior peaks and on higher time frames. The most common indicators to identify divergences with are the Relative Strength Index (RSI) and the Moving average convergence divergence (MACD).
Regular Bull Divergence: Indicates underlying strength. Bears are exhausted. Warning of a possible trend direction change from a downtrend to an uptrend.
Hidden Bull Divergence: Indicates underlying strength. Good entry or re-entry. This occurs during retracements in an uptrend. Nice to see during the price retest of previous lows. “Buy the dips."
Regular Bear Divergence: Indicates underlying weakness. The bulls are exhausted. Warning of a possible trend direction change from an uptrend to a downtrend.
Hidden Bear Divergence: Indicates underlying weakness. Found during retracements in a downtrend. Nice to see during price retests of previous highs. “Sell the rallies.”
Divergences can have different strengths.
Strong Bull Divergence
Price: Lower Low
Indicator: Higher Low
Medium Bull Divergence
Price: Equal Low
Indicator: Higher Low
Weak Bull Divergence
Price: Lower Low
Indicator: Equal Low
Hidden Bull Divergence
Price: Higher Low
Indicator: Higher Low
Strong Bear Divergence
Price: Higher High
Indicator: Lower High
Medium Bear Divergence
Price: Equal High
Indicator: Lower High
Weak Bear Divergence
Price: Higher High
Indicator: Equal High
Hidden Bull Divergence
Price: Lower High
Indicator: Higher High
Barndorff-Nielsen and Shephard Jump Statistic [Loxx]The following comments and descriptions are from from "Problems in the Application of Jump Detection Tests to Stock Price Data" by Michael William Schwert; Professor George Tauchen, Faculty Advisor.
This indicator applies several jump detection tests to intraday stock price data sampled at various frequencies. It finds that the choice of sampling frequency has an effect on both the amount of jumps detected by these tests, as well as the timing of those jumps. Furthermore, although these tests are designed to identify the same phenomenon, they find different amounts and timing of jumps when performed on the same data. These results suggest that these jump detection tests are probably identifying different types of jump behavior in stock price data, so they are not really substitutes for one another.
In recent years there has been a great deal of interest in studying jumps in asset price movements. Reasons why it is important to know when and how frequently jumps occur include risk management and the pricing and hedging of derivative contracts. Investors would benefit greatly from knowing the properties of jumps, since large instantaneous drops in asset prices result in large instantaneous losses. The effect of jumps on derivative pricing is equally significant, especially considering the important role derivatives play in modern financial markets. When asset price movements are continuous, investors can perfectly hedge derivative contracts such as options, but when jumps occur, they cause a change in the derivative price that is non-linear to the change in the price of the underlying asset. Thus, jumps introduce an unhedgeable risk to the holders of derivative contracts.
The ability to identify realized jumps in the financial markets could provide helpful information such as how frequently jumps occur, how large the jumps are, and whether they tend to occur in clusters. With this goal in mind, several authors have developed tests to determine whether or not an asset price movement is a statistically significant jump. These tests take advantage of the high-frequency intraday price data available today through electronic sources. Barndorff-Nielsen and Shephard (2004, 2006) use the difference between an estimate of variance and a jump-robust measure of variance to detect jumps over the course of a day. Approaching the problem differently, Jiang and Oomen (2007) exploit high order sample moments of returns to identify days that include jumps. Aїt-Sahalia and Jacod (2008) also exploit high order sample moments of returns to detect jumps by comparing price data sampled at two different frequencies. Lee and Mykland (2007) test for jumps at individual price observations by scaling returns by a local volatility measure. While these tests employ different strategies for detecting jumps, they are all designed to identify the same phenomenon.
For this indicator we are focused on the Barndorff-Nielsen and Shephard jump statistic.
Barndorff-Nielsen and Shephard (2004, 2006) developed a test that uses high-frequency price data to determine whether there is a jump over the course of a day. Their test compares two measures of variance: Realized Variance, which converges to the integrated variance plus a jump component as the time between observations approaches zero; and Bipower Variation, which converges to the integrated variance as the time between observations approaches zero, and is robust to jumps in the price path, an important fact for this application. The integrated variance of a price process is the integral of the square of the σ(t) term in (2.2.2), taken over the course of a day. Since prices cannot be observed continuously, one cannot calculate integrated variance exactly, and must estimate it instead.
For our purposes here, this is calculated as:
r = log(p /p )
This the geometric return from time ti-1 to time ti.
Then, Realized Variance and Bipower Variation are described by the following functions (see code for details)
realizedVariance(float src, int per)
and
bipowerVariance(float src, int per)
Huang and Tauchen (2005) also consider Relative Jump, a measure that approximates the percentage of total variance attributable to jumps:
RJ = (RV - BV) / RV
This statistic approximates the ratio of the sum of squared jumps to the total variance and is useful because it scales out long-term trends in volatility so one can compare the relative contribution of jumps to the variance of two price series with different volatilities.
To develop a statistical test to determine whether there is a significant difference between RV and BV, one needs an estimate of integrated quarticity. Andersen, Bollerslev, and Diebold (2004) recommend using a jump-robust realized Tri-Power Quarticity, I've included commentary in code to better explain how this indicator is collocated. See code for details.
How to use this indicator
When the bars turn gray, it's an indication that a jump has occurred in the market. It serves a warning that price jumped. I've included a percent point function (or inverse cumulative distribution function) to cutoff Z-score values depicted by histogram values. The top line at 3 is the empirical maximum Z-score value a serves merely as a point of reference. The Red line is the cutoff line calculated using PPF. When the histogram is green, no jumps have been detected. This indicator also includes alerts, signals, and bar coloring. I've also expanded the possible source types using my own Expanded Source Types library so you can test different log return methods as inputs. It is recommended to use window sizes of 7, 16, 78, 110, 156, and 270 returns for sampling intervals of 1 week, 1 day, 1 hour, 30 minutes, 15 minutes, and 5 minutes, respectively.
If you'ed like to better understand PPF, see here: Distributions in python
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
MACD strategy + Trailstop indicatorWelcome traveler !
Here is my first indicator I made after 3 days of hardlearning pine code (beginner in coding).
I hope it will please you, if you have any suggestion to enhance this indicator, do not hesitate to give me your thoughts in the comments section or by Private message on trading View !
How does it works ?
It's a simple MACD strategy as describe here :
Uses of EMA 200 as a trend confirmer,
For sells :
When above Zero line (MACD) and under EMA200, we go on sell (background color is red)
For buys:
When under Zero line (MACD) and above EMA 200, we go on Buy (back ground color is green)
FILTERS !
I haded one filter to reduce noise on the indicator :
Signals aren't taken if one of the 14 last candles closed on the other side of the EMA 14.
What are the green and red lines ?
The green line is equivalent of a potential stop loss as a buyer side, same for the red one on seller side !
To make the space with the price bigger, please use "ATR multiplier" in the input options of the indicator while on your chart !
Is it timeframe specific ?
Hell no it is not timeframe specific ! You can try to use it on every timeframe !
As usual, I like to remind you that the best way to test an indicator is to go backtest it or to paper trade before using it on real market conditions !
If you find an idea of filter for a specific timeframe, do not hesitate to contact me ! I'll try to do my best to enhance this indicator as the time goes !
Is there repainting ?
There is no repainting on confirmation !
There's only a movement that I don't know how to ignore on the current open candle for the trail stop indicator I built, it should not be a problem if you place alerts to automatise your trading on the close of the candle, and not the high or low !
If you know how to resolve this problem with my code, I would be glad to get your tips to enhance the script ! :)
Example of the indicator in market (backtest, as said, no repaint on confirmation) :
End-Pointed SSA of Normalized Price Corridor [Loxx]End-Pointed SSA of Normalized Price Corridor is an end-pointed SSA of normalized input price to output a smoothed normalized oscillator of price. Corridors are added in attempt to decipher larger trend direction of price. These corridor trend lines are based on highs and lows of price. Due to the SSA algorithm, this indicator takes some time load on the chat, so be patient. You can adjust the lag parameter downward to speed up the indicator load time but this will also degrade the signal. There are many different ways to use this indicator. It is also Renko chart friendly.
An example of emerging trends (these do not repaint)
What is Singular Spectrum Analysis ( SSA )?
Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Moving Average Convergence Divergence On Alter OBVOBV:
The OBV is perfect indicator to understand the strength of the particular stock. As the strength increase, the trend of the stock goes high along with price. But, the OBV is considered only with close of previous close which is to make sure the double confirmation on the price to accumulate the volume.
Altered OBV:
So, here is the altered OBV, which basically consider the close of previous close and also buying interested of the day when close is higher than open.
MACD:
I always admire the magic of MACD with pre-defined timeframe. Now, this MACD applied on top of altered OBV to signal us the moving of the ticker strength.
I hope the another MACDAltOBV would help on your swing trading strategy.
Happy Investing.
Adaptive Two-Pole Super Smoother Entropy MACD [Loxx]Adaptive Two-Pole Super Smoother Entropy (Math) MACD is an Ehlers Two-Pole Super Smoother that is transformed into an MACD oscillator using entropy mathematics. Signals are generated using Discontinued Signal Lines.
What is Ehlers; Two-Pole Super Smoother?
From "Cycle Analytics for Traders Advanced Technical Trading Concepts" by John F. Ehlers
A SuperSmoother filter is used anytime a moving average of any type would otherwise be used, with the result that the SuperSmoother filter output would have substantially less lag for an equivalent amount of smoothing produced by the moving average. For example, a five-bar SMA has a cutoff period of approximately 10 bars and has two bars of lag. A SuperSmoother filter with a cutoff period of 10 bars has a lag a half bar larger than the two-pole modified Butterworth filter.Therefore, such a SuperSmoother filter has a maximum lag of approximately 1.5 bars and even less lag into the attenuation band of the filter. The differential in lag between moving average and SuperSmoother filter outputs becomes even larger when the cutoff periods are larger.
Market data contain noise, and removal of noise is the reason for using smoothing filters. In fact, market data contain several kinds of noise. I’ll group one kind of noise as systemic, caused by the random events of trades being exercised. A second kind of noise is aliasing noise, caused by the use of sampled data. Aliasing noise is the dominant term in the data for shorter cycle periods.
It is easy to think of market data as being a continuous waveform, but it is not. Using the closing price as representative for that bar constitutes one sample point. It doesn’t matter if you are using an average of the high and low instead of the close, you are still getting one sample per bar. Since sampled data is being used, there are some dSP aspects that must be considered. For example, the shortest analysis period that is possible (without aliasing)2 is a two-bar cycle.This is called the Nyquist frequency, 0.5 cycles per sample.A perfect two-bar sine wave cycle sampled at the peaks becomes a square wave due to sampling. However, sampling at the cycle peaks can- not be guaranteed, and the interference between the sampling frequency and the data frequency creates the aliasing noise.The noise is reduced as the data period is longer. For example, a four-bar cycle means there are four samples per cycle. Because there are more samples, the sampled data are a better replica of the sine wave component. The replica is better yet for an eight-bar data component.The improved fidelity of the sampled data means the aliasing noise is reduced at longer and longer cycle periods.The rate of reduction is 6 dB per octave. My experience is that the systemic noise rarely is more than 10 dB below the level of cyclic information, so that we create two conditions for effective smoothing of aliasing noise:
1. It is difficult to use cycle periods shorter that two octaves below the Nyquist frequency.That is, an eight-bar cycle component has a quantization noise level 12 dB below the noise level at the Nyquist frequency. longer cycle components therefore have a systemic noise level that exceeds the aliasing noise level.
2. A smoothing filter should have sufficient selectivity to reduce aliasing noise below the systemic noise level. Since aliasing noise increases at the rate of 6 dB per octave above a selected filter cutoff frequency and since the SuperSmoother attenuation rate is 12 dB per octave, the Super- Smoother filter is an effective tool to virtually eliminate aliasing noise in the output signal.
What are DSL Discontinued Signal Line?
A lot of indicators are using signal lines in order to determine the trend (or some desired state of the indicator) easier. The idea of the signal line is easy : comparing the value to it's smoothed (slightly lagging) state, the idea of current momentum/state is made.
Discontinued signal line is inheriting that simple signal line idea and it is extending it : instead of having one signal line, more lines depending on the current value of the indicator.
"Signal" line is calculated the following way :
When a certain level is crossed into the desired direction, the EMA of that value is calculated for the desired signal line
When that level is crossed into the opposite direction, the previous "signal" line value is simply "inherited" and it becomes a kind of a level
This way it becomes a combination of signal lines and levels that are trying to combine both the good from both methods.
In simple terms, DSL uses the concept of a signal line and betters it by inheriting the previous signal line's value & makes it a level.
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
Leavitt Convolution Slope [CC]The Leavitt Convolution Slope indicator was created by Jay Leavitt (Stocks and Commodities Oct 2019, page 11), who is most well known for creating the Volume-Weighted Average Price indicator. This indicator is very similar to the Leavitt Convolution indicator but the big difference is that it is getting the slope instead of predicting the next Convolution value. I changed quite a few things from the original source code so let me know if you like these changes. I added a normalization function using code from a good friend @loxx that I recommend to leave on but feel free to experiment with it. Last but not least, the unsure levels are essentially acting as a buy or sell threshold. I personally recommend to buy or sell for zero crossovers but another option would be to buy or sell for crossovers using the unsure levels. I have color coded the lines to turn light green for a normal buy signal or dark green for a strong buy signal and light red for a normal sell signal, and dark red for a strong sell signal.
This is another indicator in a series that I'm publishing to fulfill a special request from @ashok1961 so let me know if you ever have any special requests for me.
Conversion Range Candles// Conversion Range Candles
// Compares price action range with that of the value currency (e.g. ETHBTC compared to BTCUSD).
// Public Domain
// by JollyWizard
MACDBB HistoXThis is a custom modified MACD where some parameters have been customized and Bollinger Band added to the MACD . When the MACD is running above its upper Bollinger Band , it will be depicted as lime, and vice versa red.
Then the second set of histograms is am idea of mine where the opposing parameters of MACD signals are deducted off each other to reveal the underlying "momentum" of the MACD .
Hope that these tweaks of a ol'trusty indicator it works for those who are interested! Enjoy!
FDI-Adaptive Fisher Transform [Loxx]FDI-Adaptive Fisher Transform is a Fractal Dimension Adaptive Fisher Transform indicator.
What is the Fractal Dimension Index?
The goal of the fractal dimension index is to determine whether the market is trending or in a trading range. It does not measure the direction of the trend. A value less than 1.5 indicates that the price series is persistent or that the market is trending. Lower values of the FDI indicate a stronger trend. A value greater than 1.5 indicates that the market is in a trading range and is acting in a more random fashion.
What is Fisher Transform?
The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution.
The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend.
Included:
Zero-line and signal cross options for bar coloring
Customizable overbought/oversold thresh-holds
Alerts
Signals
Normalized Velocity [Loxx]Velocity (which is often called a "smoother momentum" since it is much smoother than momentum without lagging at all) with an addition of ATR normalization
Since velocity is (even when normalization is applied) is not an indicator with fixed bounds, this indicator is uses floating levels for what is usually called overbought and oversold levels (+ a floating "zero" line is added). Something that would look like a "fixed levels" is easily achieved if you use long floating levels period in which case those levels are quite similar to fixed levels.
This indicator can be used like any momentum indicator (in that case recommended coloring mode is to use either slope coloring or "zero" middle level crossing coloring) or it can be used as a "trending" indicator in which case it is better to use coloring on outer level cross, and longer calculation periods are advised in that case.
Included:
Bar coloring
3 signal variations w/ alerts
Loxx's Expanded Source Types
Alerts
End-pointed SSA of Normalized Price Oscillator [Loxx]End-pointed SSA of Normalized Price Oscillator is an indicator that converts source price into a normalized oscillator and runs an SSA calculation to derived a smoother final output. This indicator also serves to introduce the concept of SSA to the Pine Coder community. The data returned from this algorithm is an array of modeled values on past X bars. We could use this data but it's not useful, so instead we use the end-pointed value which is the first value of the array at index 0.
What is Singular Spectrum Analysis (SSA)?
Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition (SVD) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA. This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA. The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA, one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA, the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
[blackcat] L3 RMI Trading StrategyLevel 3
Background
My view of correct usage of RSI and the relationship between RMI and RSI. A proposed RMI indicator with features is introduced
Descriptions
The Relative Strength Index (RSI) is a technical indicator that many people use. Its focus indicates the strength or weakness of a stock. In the traditional usage of this point, when the RSI is above 50, it is strong, otherwise it is weak. Above 80 is overbought, below 20 is oversold. This is what the textbook says. However, if you follow the principles in this textbook and enter the actual trading, you would lose a lot and win a little! What is the reason for this? When the RSI is greater than 50, that is, a stock enters the strong zone. At this time, the emotions of market may just be brewing, and as a result, you run away and watch others win profit. On the contrary, when RSI<20, that is, a stock enters the weak zone, you buy it. At this time, the effect of losing money is spreading. You just took over the chips that were dumped by the whales. Later, you thought that you had bought at the bottom, but found that you were in half mountainside. According to this cycle, there is a high probability that a phenomenon will occur: if you sell, price will rise, and if you buy, price will fall, who have similar experiences should quickly recall whether their RSI is used in this way. Technical indicators are weapons. It can be either a tool of bull or a sharp blade of bear. Don't learn from dogma and give it away. Trading is a game of people. There is an old saying called “people’s hearts are unpredictable”. Do you really think that there is a tool that can detect the true intentions of people’s hearts 100% of the time?
For the above problems, I suggest that improvements can be made in two aspects (in other words, once the strategy is widely spread, it is only a matter of time before it fails. The market is an adaptive and complex system, as long as it can be fully utilized under the conditions that can be used, it is not easy to use. throw or evolve):
1. RSI usage is the opposite. When a stock has undergone a deep adjustment from a high level, and the RSI has fallen from a high of more than 80 to below 50, it has turned from strong to weak, and cannot be bought in the short term. But when the RSI first moved from a low to a high of 80, it just proved that the stock was in a strong zone. There are funds in the activity, put into the stock pool.
Just wait for RSI to intervene in time when it shrinks and pulls back (before it rises when the main force washes the market). It is emphasized here that the use of RSI should be combined with trading volume, rising volume, and falling volume are all healthy performances. A callback that does not break an important moving average is a confirmed buying point or a second step back on an important moving average is a more certain buying point.
2. The RSI is changed to a more stable and adjustable RMI (Relative Momentum Indicator), which is characterized by an additional momentum parameter, which can not only be very close to the RSI performance, but also adjust the momentum parameter m when the market environment changes to ensure more A good fit for a changing market.
The Relative Momentum Index (RMI) was developed by Roger Altman and described its principles in his article in the February 1993 issue of the journal Technical Analysis of Stocks and Commodities. He developed RMI based on the RSI principle. For example, RSI is calculated from the close to yesterday's close in a period of time compared to the ups and downs, while the RMI is compared from the close to the close of m days ago. Therefore, in principle, when m=1, RSI should be equal to RMI. But it is precisely because of the addition of this m parameter that the RMI result may be smoother than the RSI.
Not much more to say, the below picture: when m=1, RMI and RSI overlap, and the result is the same.
The Shanghai 50 Index is from TradingView (m=1)
The Shanghai 50 Index is from TradingView (m=3)
The Shanghai 50 Index is from TradingView (m=5)
For this indicator function, I also make a brief introduction:
1. 50 is the strength line (white), do not operate offline, pay attention online. 80 is the warning line (yellow), indicating that the stock has entered a strong area; 90 is the lightening line (orange), once it is greater than 90 and a sell K-line pattern appears, the position will be lightened; the 95 clearing line (red) means that selling is at a climax. This is seen from the daily and weekly cycles, and small cycles may not be suitable.
2. The purple band indicates that the momentum is sufficient to hold a position, and the green band indicates that the momentum is insufficient and the position is short.
3. Divide the RMI into 7, 14, and 21 cycles. When the golden fork appears in the two resonances, a golden fork will appear to prompt you to buy, and when the two periods of resonance have a dead fork, a purple fork will appear to prompt you to sell.
4. Add top-bottom divergence judgment algorithm. Top_Div red label indicates top divergence; Bot_Div green label indicates bottom divergence. These signals are only for auxiliary judgment and are not 100% accurate.
5. This indicator needs to be combined with VOL energy, K-line shape and moving average for comprehensive judgment. It is still in its infancy, and open source is published in the TradingView community. A more complete advanced version is also considered for subsequent release (because the K-line pattern recognition algorithm is still being perfected).
Remarks
Feedbacks are appreciated.
eswaran ab//@version=5
strategy("MACD Strategy", overlay=true)
fastLength = input(12)
slowlength = input(26)
MACDLength = input(9)
MACD = ta.ema(close, fastLength) - ta.ema(close, slowlength)
aMACD = ta.ema(MACD, MACDLength)
delta = MACD - aMACD
if (ta.crossover(delta, 0))
strategy.entry("MacdLE", strategy.long, comment="MacdLE")
if (ta.crossunder(delta, 0))
strategy.entry("MacdSE", strategy.short, comment="MacdSE")
//plot(strategy.equity, title="equity", color=color.red, linewidth=2, style=plot.style_areabr)
Williams %R (v.4)This is an upgrade and an update of my Williams %R indicator modification.
As before this implementation is enhanced with CCI in the form of background colors. These colors can be used as a confirmation signal and indication of a current trend. Thee also can be employed in deciding when to enter/exit the market.
Besides, added is a scaling function and Lower/Upper Bound inputs.
DB CCI Breakout MTFDB CCI Breakout MTF
What does the indicator do?
The indicator will display crypto breakout and fallouts based on 4 timeframe CCI values. By default the current chart timeframe is used and the user may chose 3 other timeframes in the settings. Additionally, the symbol may be configured in the indicator settings. Default is Coinbase:ETHUSD.
The indicator will monitor the CCI levels on 4 timeframes and will alert to any CCI activity over 100 or under -100 which would indicate a breakout or fallout is present.
A green diamond is displayed when a breakout is detected on one or more of the timeframes for the selected symbol.
How should this indicator be used?
The indicator is a secondary alert system for the presence of breakouts or fallout conditions as under those scenarios position exit or entry strategies may be different.
Does the indicator include any alerts?
Not in this version. But I could add some if desired.
Use at your own risk and do your own diligence.
Enjoy!
Trend Surfers - Momentum + ADX + EMAThis script mixes the Lazybear Momentum indicator, ADX indicator, and EMA.
Histogram meaning:
Green = The momentum is growing and the ADX is growing or above your set value
Red = The momentum is growing on the downside and the ADX is growing or above your set value
Orange = The market doesn't have enough momentum or the ADX is not growing or above your value (no trend)
Background meaning:
Blue = The price is above the EMA
Purple = The price is under the EMA
Cross color on 0 line:
Dark = The market might be sideway still
Light = The market is in a bigger move
BB%Bx4This is just a script that combines 4 BB%B oscillators in one. It is useful for seeing multiple divergences on one graphic.
The default setting is the 1m time frame but, you can change it to 5m time frame and it will still work. You can see it on any CHART time frame and that was my goal when I made it. So, I don't have to switch back and forth.
I made this tool for my trading style and it may not work for you.
MACD x SuperTrend with trailing stoplossThis trading strategy is based on MACD crossover and crossunder. It uses the supertrend to identify the trend it is trading on and takes trades accordingly. You can use the built in risk to reward ratio parameter through the settings of the indicator for your desired R/R
My goal in creating this indicator was to learn about risk management. This indicator will put up a stop-loss and take profit target according to the entry point it shows.
This indicator showed me the best results on BTC at 5min price chart. I'm new to trading so, do your own due diligence
DCA After Downtrend v2 (by BHD_Trade_Bot)The purpose of the strategy is to identify the end of a short-term downtrend . So that you can easily to DCA certain amount of money for each month.
ENTRY
The buy orders are placed on a monthly basis for assets at the end of a short-term downtrend:
- Each month condition: In 1-hour time frame, each month has 24 * 30 candles
- The end of short-term downtrend condition: use MACD for less delay
CLOSE
The sell orders are placed when:
- Is last bar
The strategy use $1000 and trading fee is 1.1% for each order.
Pro tip: The 1-hour time frame has the best results on average:
- Total spent: $1000 x 33 = $33,000
- Total profit: $65,578
