Simple SuperTrend Strategy for BTCUSD 4HHello guys!, If you are a swing trader and you are looking for a simple trend strategy, you should check this one. Based in the supertrend indicator, this strategy will help you to catch big movements in BTCUSD 4H and avoid losses as much as possible in consolidated situations of the market
This strategy was designed for BTCUSD in 4H timeframe
Backtesting context: 2020-01-02 to 2023-01-05 (The strategy has also worked in previous years)
Trade conditions:
Rules are actually simple, the most important thing is the risk and position management of this strategy
For long:
Once Supertrend changes from a downtrend to a uptrend, you enter into a long position. The stop loss will be defined by the atr stop loss
The first profit will be of 0.75 risk/reward ratio where half position will be closed. When this happens, you move the stop loss to break even.
Now, just will be there two situations:
Once Supertrend changes from a uptrend to a downtrend, you close the other half of the initial long position.
If price goes againts the position, the position will be closed due to breakeven.
For short:
Once Supertrend changes from a uptrend to a downtrend, you enter into a short position. The stop loss will be defined by the atr stop loss
The first profit will be of 0.75 risk/reward ratio where half position will be closed. When this happens, you move the stop loss to break even.
Like in the long position, just will be there two situations:
Once Supertrend changes from a downtrend to a uptrend, you close the other half of the initial short position.
If price goes againts the position, the position will be closed due to breakeven.
Risk management
For 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 for this.
Example: You have 1000 usd and you just want to risk 2,5% of your account, there is a long signal at price of 20,000 usd. The stop loss price from atr stop loss is 19,000. You calculate the distance in percent between 20,000 and 19,000. In this case, that distance would be of 5,0%. 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%)/(5,0%) = 500usd. It means, you have to use 500 usd for risking 2.5% of your account.
We will use this risk management for apply compound interest.
Script functions
Inside of settings, you will find some utilities for display atr stop loss, supertrend or positions.
You will find the settings for risk management at the end of the script if you want to change something. But rebember, do not change values from indicators, the idea is to not over optimize the strategy.
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.
Signals meanings:
L for long position. CL for close long position.
S for short position. CS for close short position.
Tp for take profit (it also appears when the position is closed due to stop loss, this due to the script uses two kind of positions)
Exit due to break even or due to stop loss
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
The amount of trades closed in the backtest are not exactly the real ones. If you want to know the real ones, go to settings and change % of trade for first take profit to 100 for getting the real ones. In the backtest, the real amount of opened trades was of 194.
Indicators used:
Supertrend
Atr stop loss by garethyeo
This is the fist strategy that I publish in tradingview, I will be glad with you for any suggestion, support or advice for future scripts. Do not doubt in make any question you have and if you liked this content, leave a boost. I plan to bring more strategies and useful content for you!
Cari dalam skrip untuk "2020年+国债收益率"
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.
Selected Dates Filter by @zeusbottradingWe are presenting you feature for strategies in Pine Script.
This function/pine script is about NOT opening trades on selected days. Real usage is for bank holidays or volatile days (PPI, CPI, Interest Rates etc.) in United States and United Kingdom from 2020 to 2030 (10 years of dates of bank holidays in mentioned countries above). Strategy is simple - SMA crossover of two lengts 14 and 28 with close source.
In pine script you can see we picked US and GB bank holidays. If you add this into your strategy, your bot will not open trades on those days. You must make it a rule or a condition. We use it as a rule in opening long/short trades.
You can also add some of your prefered dates, here is just example of our idea. If you want to add your preffered days you can find them on any site like forexfactory, myfxbook and so on. But don’t forget to add function “time_tradingday ! = YourChoosedDate” as it is writen lower in the pine script.
Sometimes the date is substituted for a different day, because the day of the holiday is on Saturday or Sunday.
Made with ❤️ for this community.
If you have any questions or suggestions, let us know.
The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold zeusbottrading TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script.
Physics CandlesPhysics Candles embed volume and motion physics directly onto price candles or market internals according to the cyclic pattern of financial securities. The indicator works on both real-time “ticks” and historical data using statistical modeling to highlight when these values, like volume or momentum, is unusual or relatively high for some periodic window in time. Each candle is made out of one or more sub-candles that each contain their own information of motion, which converts to the color and transparency, or brightness, of that particular candle segment. The segments extend throughout the entire candle, both body and wicks, and Thick Wicks can be implemented to see the color coding better. This candle segmentation allows you to see if all the volume or energy is evenly distributed throughout the candle or highly contained in one small portion of it, and how intense these values are compared to similar time periods without going to lower time frames. Candle segmentation can also change a trader’s perspective on how valuable the information is. A “low” volume candle, for instance, could signify high value short-term stopping volume if the volume is all concentrated in one segment.
The Candles are flexible. The physics information embedded on the candles need not be from the same price security or market internal as the chart when using the Physics Source option, and multiple Candles can be overlayed together. You could embed stock price Candles with market volume, market price Candles with stock momentum, market structure with internal acceleration, stock price with stock force, etc. My particular use case is scalping the SPX futures market (ES), whose price action is also dictated by the volume action in the associated cash market, or SPY, as well as a host of other securities. Physics allows you to embed the ES volume on the SPY price action, or the SPY volume on the ES price action, or you can combine them both by overlaying two Candle streams and increasing the Number of Overlays option to two. That option decreases the transparency levels of your coloring scheme so that overlaying multiple Candles converges toward the same visual color intensity as if you had one. The Candle and Physics Sources allows for both Symbols and Spreads to visualize Candle physics from a single ticker or some mathematical transformation of tickers.
Due to certain TradingView programming restrictions, each Candle can only be made out of a maximum of 8 candle segments, or an “8-bit” resolution. Since limits are just an opportunity to go beyond, the user has the option to stack multiple Candle indicators together to further increase the candle resolution. If you don’t want to see the Candles for some particular period of the day, you can hide them, or use the hiding feature to have multiple Candles calibrated to show multiple parts of the trading day. Securities tend to have low volume after hours with sharp spikes at the open or close. Multiple Candles can be used for multiple parts of the trading day to accommodate these different cycles in volume.
The Candles do not need be associated with the nominal security listed on the TV chart. The Candle Source allows the user to look at AAPL Candles, for instance, while on a TSLA or SPY chart, each with their respective volume actions integrated into the candles, for instance, to allow the user to see multiple security price and volume correlation on a single chart.
The physics information currently embeddable on Candles are volume or time, velocity, momentum, acceleration, force, and kinetic energy. In order to apply equations of motion containing a mass variable to financial securities, some analogous value for mass must be assumed. Traders often regard volume or time as inextricable variables to a securities price that can indicate the direction and strength of a move. Since mass is the inextricable variable to calculating the momentum, force, or kinetic energy of motion, the user has the option to assume either time or volume is analogous to mass. Volume may be a better option for mass as it is not strictly dependent on the speed of a security, whereas time is.
Data transformations and outlier statistics are used to color code the intensity of the physics for each candle segment relative to past periodic behavior. A million shares during pre-market or a million shares during noontime may be more intense signals than a typical million shares traded at the open, and should have more intense color signals. To account for a specific cyclic behavior in the market, the user can specify the Window and Cycle Time Frames. The Window Time Frame splits up a Cycle into windows, samples and aggregates the statistics for each window, then compares the current physics values against past values in the same window. Intraday traders may benefit from using a Daily Cycle with a 30-minute Window Time Frame and 1-minute Sample Time Frame. These settings sample and compare the physics of 1-minute candles within the current 30-minute window to the same 30-minute window statistics for all past trading days, up until the data limit imposed by TradingView, or until the Data Collection Start Date specified in the settings. Longer-term traders may benefit from using a Monthly Cycle with a Weekly Time Frame, or a Yearly Cycle with a Quarterly Time Frame.
Multiple statistics and data transformation methods are available to convey relative intensity in different ways for different trading signals. Physics Candles allows for both Normal and Log-Normal assumptions in the physics distribution. The data can then be transformed by Linear, Logarithmic, Z-Score, or Power-Law scoring, where scoring simply assigns an intensity to the relative physics value of each candle segment based on some mathematical transformation. Z-scoring often renders adequate detection by scoring the segment value, such as volume or momentum, according to the mean and standard deviation of the data set in each window of the cycle. Logarithmic or power-law transformation with a gamma below 1 decreases the disparity between intensities so more less-important signals will show up, whereas the power-law transformation with gamma values above 1 increases the disparity between intensities, so less more-important signals will show up. These scores are then converted to color and transparency between the Min Score and the Max Score Cutoffs. The Auto-Normalization feature can automatically pick these cutoffs specific to each window based on the mean and standard deviation of the data set, or the user can manually set them. Physics was developed with novices in mind so that most users could calibrate their own settings by plotting the candle segment distributions directly on the chart and fiddling with the settings to see how different cutoffs capture different portions of the distribution and affect the relative color intensities differently. Security distributions are often skewed with fat-tails, known as kurtosis, where high-volume segments for example, have a higher-probabilities than expected for a normal distribution. These distribution are really log-normal, so that taking the logarithm leads to a standard bell-shaped distribution. Taking the Z-score of the Log-Normal distribution could make the most statistical sense, but color sensitivity is a discretionary preference.
Background Philosophy
This indicator was developed to study and trade the physics of motion in financial securities from a visually intuitive perspective. Newton’s laws of motion are loosely applied to financial motion:
“A body remains at rest, or in motion at a constant speed in a straight line, unless acted upon by a force”.
Financial securities remain at rest, or in motion at constant speed up or down, unless acted upon by the force of traders exchanging securities.
“When a body is acted upon by a force, the time rate of change of its momentum equals the force”.
Momentum is the product of mass and velocity, and force is the product of mass and acceleration. Traders render force on the security through the mass of their trading activity and the acceleration of price movement.
“If two bodies exert forces on each other, these forces have the same magnitude but opposite directions.”
Force arises from the interaction of traders, buyers and sellers. One body of motion, traders’ capitalization, exerts an equal and opposite force on another body of motion, the financial security. A securities movement arises at the expense of a buyer or seller’s capitalization.
Volume
The premise of this indicator assumes that volume, v, is an analogous means of measuring physical mass, m. This premise allows the application of the equations of motion to the movement of financial securities. We know from E=mc^2 that mass has energy. Energy can be used to create motion as kinetic energy. Taking a simple hypothetical example, the interaction of one short seller looking to cover lower and one buyer looking to sell higher exchange shares in a security at an agreed upon price to create volume or mass, and therefore, potential energy. Eventually the short seller will actively cover and buy the security from the previous buyer, moving the security higher, or the buyer will actively sell to the short seller, moving the security lower. The potential energy inherent in the initial consolidation or trading activity between buy and seller is now converted to kinetic energy on the subsequent trading activity that moves the securities price. The more potential energy that is created in the consolidation, the more kinetic energy there is to move price. This is why point and figure traders are said to give price targets based on the level of volatility or size of a consolidation range, or why Gann traders square price and time, as time is roughly proportional to mass and trading activity. The build-up of potential energy between short sellers and buyers in GME or TSLA led to their explosive moves beyond their standard fundamental valuations.
Position
Position, p, is simply the price or value of a financial security or market internal.
Time
Time, t, is another means of measuring mass to discover price behavior beyond the time snapshots that simple candle charts provide. We know from E=mc^2 that time is related to rest mass and energy given the speed of light, c, where time ≈ distance * sqrt(mass/E). This relation can also be derived from F=ma. The more mass there is, the longer it takes to compute the physics of a system. The more energy there is, the shorter it takes to compute the physics of a system. Similarly, more time is required to build a “resting” low-volatility trading consolidation with more mass. More energy added to that trading consolidation by competing buyers and sellers decreases the time it takes to build that same mass. Time is also related to price through velocity.
Velocity = (p(t1) – p(t0)) / p(t0)
Velocity, v, is the relative percent change of a securities price, p, over a period of time, t0 to t1. The period of time is between subsequent candles, and since time is constant between candles within the same timeframe, it is not used to calculate velocity or acceleration. Price moves faster with higher velocity, and slower with slower velocity, over the same fixed period of time. The product of velocity and mass gives momentum.
Momentum = mv
This indicator uses physics definition of momentum, not finance’s. In finance, momentum is defined as the amount of change in a securities price, either relative or absolute. This is definition is unfortunate, pun intended, since a one dollar move in a security from a thousand shares traded between a few traders has the exact same “momentum” as a one dollar move from millions of shares traded between hundreds of traders with everything else equal. If momentum is related to the energy of the move, momentum should consider both the level of activity in a price move, and the amount of that price move. If we equate mass to volume to account for the level of trading activity and use physics definition of momentum as the product of mass and velocity, this revised definition now gives a thousand-times more momentum to a one-dollar price move that has a thousand-times more volume behind it. If you want to use finance’s volume-less definition of momentum, use velocity in this indicator.
Acceleration = v(t1) – v(t0)
Acceleration, a, is the difference between velocities over some period of time, t0 to t1. Positive acceleration is necessary to increase a securities speed in the positive direction, while negative acceleration is necessary to decrease it. Acceleration is related to force by mass.
Force = ma
Force is required to change the speed of a securities valuation. Price movements with considerable force have considerably more impact on future direction. A change in direction requires force.
Kinetic Energy = 0.5mv^2
Kinetic energy is the energy that a financial security gains from the change in its velocity by force. The built-up of potential energy in trading consolidations can be converted to kinetic energy on a breakout from the consolidation.
Cycle Theory and Relativity
Just as the physics of motion is relative to a point of reference, so too should the physics of financial securities be relative to a point of reference. An object moving at a 100 mph towards another object moving in the same direction at 100 mph will not appear to be moving relative to each other, nor will they collide, but from an outsider observer, the objects are going 100 mph and will collide with significant impact if they run into a stationary object relative to the observer. Similarly, trading with a hundred thousand shares at the open when the average volume is a couple million may have a much smaller impact on the price compared to trading a hundred thousand shares pre-market when the average volume is ten thousand shares. The point of reference used in this indicator is the average statistics collected for a given Window Time Frame for every Cycle Time Frame. The physics values are normalized relative to these statistics.
Examples
The main chart of this publication shows the Force Candles for the SPY. An intense force candle is observed pre-market that implicates the directional overtone of the day. The assumption that direction should follow force arises from physical observation. If a large object is accelerating intensely in a particular direction, it may be fair to assume that the object continues its direction for the time being unless acted upon by another force.
The second example shows a similar Force Candle for the SPY that counters the assumption made in the first example and emphasizes the importance of both motion and context. While it’s fair to assume that a heavy highly accelerating object should continue its course, if that object runs into an obstacle, say a brick wall, it’s course may deviate. This example shows SPY running into the 50% retracement wall from the low of Mar 2020, a significant support level noted in literature. The example also conveys Gann’s idea of “lost motion”, where the SPY penetrated the 50% price but did not break through it. A brick wall is not one atom thick and price support is not one tick thick. An object can penetrate only one layer of a wall and not go through it.
The third example shows how Volume Candles can be used to identify scalping opportunities on the SPY and conveys why price behavior is as important as motion and context. It doesn’t take a brick wall to impede direction if you know that the person driving the car tends to forget to feed the cats before they leave. In the chart below, the SPY breaks down to a confluence of the 5-day SMA, 20-day SMA, and an important daily trendline (not shown) after the bullish bounce from the 50% retracement days earlier. High volume candles on the SMA signify stopping volume that reverse price direction. The character of the day changes. Bulls become more aggressive than bears with higher volume on upswings and resistance, whiles bears take on a defensive position with lower volume on downswings and support. High volume stopping candles are seen after rallies, and can tell you when to take profit, get out of a position, or go short. The character change can indicate that its relatively safe to re-enter bullish positions on many major supports, especially given the overarching bullish theme from the large reaction off the 50% retracement level.
The last example emphasizes the importance of relativity. The Volume Candles in the chart below are brightest pre-market even though the open has much higher volume since the pre-market activity is much higher compared to past pre-markets than the open is compared to past opens. Pre-market behavior is a good indicator for the character of the day. These bullish Volume Candles are some of the brightest seen since the bounce off the 50% retracement and indicates that bulls are making a relatively greater attempt to bring the SPY higher at the start of the day.
Infrequently Asked Questions
Where do I start?
The default settings are what I use to scalp the SPY throughout most of the extended trading day, on a one-minute chart using SPY volume. I also overlay another Candle set containing ES future volume on the SPY price structure by setting the Physics Source to ES1! and the Number of Overlays setting to 2 for each Candle stream in order to account for pre- and post-market trading activity better. Since the closing volume is exponential-like up until the end of the regular trading day, adding additional Candle streams with a tighter Window Time Frame (e.g., 2-5 minute) in the last 15 minutes of trading can be beneficial. The Hide feature can allow you to set certain intraday timeframes to hide one Candle set in order to show another Candle set during that time.
How crazy can you get with this indicator?
I hope you can answer this question better. One interesting use case is embedding the velocity of market volume onto an internal market structure. The PCTABOVEVWAP.US is a market statistic that indicates the percent of securities above their VWAP among US stocks and is helpful for determining short term trends in the US market. When securities are rising above their VWAP, the average long is up on the day and a rising PCTABOVEVWAP.US can be viewed as more bullish. When securities are falling below their VWAP, the average short is up on the day and a falling PCTABOVEVWAP.US can be viewed as more bearish. (UPVOL.US - DNVOL.US) / TVOL.US is a “spread” symbol, in TV parlance, that indicates the decimal percent difference between advancing volume and declining volume in the US market, showing the relative flow of volume between stocks that are up on the day, and stocks that are down on the day. Setting PCTABOVEVWAP.US in the Candle Source, (UPVOL.US - DNVOL.US) / TVOL.US in the Physics Source, and selecting the Physics to Velocity will embed the relative velocity of the spread symbol onto the PCTABOVEVWAP.US candles. This can be helpful in seeing short term trends in the US market that have an increasing amount of volume behind them compared to other trends. The chart below shows Volume Candles (top) and these Spread Candles (bottom). The first top at 9:30 and second top at 10:30, the high of the day, break down when the spread candles light up, showing a high velocity volume transfer from up stocks to down stocks.
How do I plot the indicator distribution and why should I even care?
The distribution is visually helpful in seeing how different normalization settings effect the distribution of candle segments. It is also helpful in seeing what physics intensities you want to ignore or show by segmenting part of the distribution within the Min and Max Cutoff values. The intensity of color is proportional to the physics value between the Min and Max Cutoff values, which correspond to the Min and Max Colors in your color scheme. Any physics value outside these Min and Max Cutoffs will be the same as the Min and Max Colors.
Select the Print Windows feature to show the window numbers according to the Cycle Time Frame and Window Time Frame settings. The window numbers are labeled at the start of each window and are candle width in size, so you may need to zoom into to see them. Selecting the Plot Window feature and input the window number of interest to shows the distribution of physics values for that particular window along with some statistics.
A log-normal volume distribution of segmented z-scores is shown below for 30-minute opening of the SPY. The Min and Max Cutoff at the top of the graph contain the part of the distribution whose intensities will be linearly color-coded between the Min and Max Colors of the color scheme. The part of the distribution below the Min Cutoff will be treated as lowest quality signals and set to the Min Color, while the few segments above the Max Cutoff will be treated as the highest quality signals and set to the Max Color.
What do I do if I don’t see anything?
Troubleshooting issues with this indicator can involve checking for error messages shown near the indicator name on the chart or using the Data Validation section to evaluate the statistics and normalization cutoffs. For example, if the Plot Window number is set to a window number that doesn’t exist, an error message will tell you and you won’t see any candles. You can use the Print Windows option to show windows that do exist for you current settings. The auto-normalization cutoff values may be inappropriate for your particular use case and literally cut the candles out of the chart. Try changing the chart time frame to see if they are appropriate for your cycle, sample and window time frames. If you get a “Timeframe passed to the request.security_lower_tf() function must be lower than the timeframe of the main chart” error, this means that the chart timeframe should be increased above the sample time frame. If you get a “Symbol resolve error”, ensure that you have correct symbol or spread in the Candle or Physics Source.
How do I see a relative physics values without cycles?
Set the Window Time Frame to be equal to the Cycle Time Frame. This will aggregate all the statistics into one bucket and show the physics values, such as volume, relative to all the past volumes that TV will allow.
How do I see candles without segmentation?
Segmentation can be very helpful in one context or annoying in another. Segmentation can be removed by setting the candle resolution value to 1.
Notes
I have yet to find a trading platform that consistently provides accurate real-time volume and pricing information, lacking adequate end-user data validation or quality control. I can provide plenty of examples of real-time volume counts or prices provided by TradingView and other platforms that were significantly off from what they should have been when comparing against the exchanges own data, and later retroactively corrected or not corrected at all. Since no indicator can work accurately with inaccurate data, please use at your own discretion.
The first version is a beta version. Debugging and validating code in Pine script is difficult without proper unit testing. Please report any bugs with enough information to reproduce them and indicate why they are important. I also encourage you to export the data from TradingView and verify the calculations for your particular use case.
The indicator works on real-time updates that occur at a higher frequency than the candle time frame, which TV incorrectly refers to as ticks. They use this terminology inaccurately as updates are really aggregated tick data that can take place at different prices and may not accurately reflect the real tick price action. Consequently, this inaccuracy also impacts the real-time segmentation accuracy to some degree. TV does not provide a means of retaining “tick” information, so the higher granularity of information seen real-time will be lost on a disconnect.
TV does not provide time and sales information. The volume and price information collected using the Sample Time Frame is intraday, which provides only part of the picture. Intraday volume is generally 50 to 80% of the end of day volume. Consequently, the daily+ OHLC prices are intraday, and may differ significantly from exchanged settled OHLC prices.
The Cycle and Window Time Frames refer to calendar days and time, not trading days or time. For example, the first window week of a monthly cycle is the first seven days of the month, not the first Monday through Friday of trading for the month.
Chart Time Frames that are higher than the Window Time Frames average the normalized physics for price action that occurred within a given Candle segment. It does not average price action that did not occur.
One of the main performance bottleneck in TradingView’s Pine Script is client-side drawing and plotting. The performance of this indicator can be increased by lowering the resolution (the number of sub-candles this indicator plots), getting a faster computer, or increasing the performance of your computer like plugging your laptop in and eliminating unnecessary processes.
The statistical integrity of this indicator relies on the number of samples collected per sample window in a given cycle. Higher sample counts can be obtained by increasing the chart time frame or upgrading the TradingView plan for a higher bar count. While increasing the chart time frame doesn’t increase the visual number of bars plotted on the chart, it does increase the number of bars that can be pulled at a lower time frame, up to 100,000.
Due to a limitation in Pine Scripts request_lower_tf() function, using a spread symbol will only work for regular trading hours, not extended trading hours.
Ideally, velocity or momentum should be calculated between candle closes. To eliminate the need to deal with price gaps that would lead to an incorrect statistical distributions, momentum is calculated between candle open and closes as a percent change of the price or value, which should not be an issue for most liquid securities.
SPX and Federal Net Liquidity differenceScript for applying Federal Net Liquidity to the SPX post-2020 monetary policy. Original indicator from jlb05013 with adjustments to make it more readable and usable. When the indicator is above 250 the SPX is overbought and when it's below -250 the SPX is oversold.
It's not perfect, I'm just publishing because I didn't see it already out there.
True_Range_%Average True range percent show the the latest true range value as percentage of previous close.
Standard ATR shows the average of absolute value of True range. This is problem when price level changes over time. Because Stocks trading at higher price level e.g $1,000 will have high ATR value as compared to stocks trading $ 50. This may look like volatility has increase recently which is of-course not true. As you can see in the chart, ATR value of period before 2020 is lower than the recent period.
True Range Percentage solves value. With this script you can also find when there is a Volatility spike (1.5 time of avg) or Low volatility (0.7 times of avg).
Volatility is cyclic in nature. It oscillates between high and low. Observing this behavior can be extremely usefully in timing entry and exits.
CDC Action Zone + 3MA Edited by Chayo// Edit from CDC Action Zone V3 2020
// Thanks you very much piriya33
TheBigBangTraders BreakoutName: TheBigBangTraders Breakout
Category: Trend Follower
Operating mode: Spot
Trades duration: Intraday
Timeframe: 1H
Suggested usage: the purpose of this strategy is to help to investigate if the asset is sensitive to breakout approach.
Entry: Trigger point can be choose by the user between:
High of the N days ago
High of the N weeks ago
Exit: End of Day
Usage:
⁃ It can be useful to use this script to test the behaviour of a definite asset
⁃ This is a raw system that can be considered a base to realize a complete breakout strategy
Configuration:
- N/A
Backtesting
⁃ Exchange: BINANCE
⁃ Pair: ETHUSDT
⁃ Timeframe: !H
⁃ Fee 0.075%
⁃ Slippage 0
- Start : 2020-01-03
How you or we can improve? Source code is open so share your ideas!
PuetzUWS [time, price] multiFractal mirrors, SPX 1872-2020This script is simply provided because a few rare people may actually be able to use one or two coding ideas. It is not possible to provide useful (description, explanation)s here. Maybe you can find those with a webSearch. If anybody is interested in the basic concept, just copy the code and run with it.
As the original was in violation of PineScript rules, I've removed many links, including :
- documentation of my code
- external sources of code
- blog solutions to Pine script programming
- math, science references, people
Hopefully it will won't be rejected this time, if so, too bad. I only made it through 10% of the conceptual objectives, and I do not believe any of the rest of the concepts are do-able in Pine Script. The current coding is (incomplete, unstable) but does give a faint idea of my "first step" intents. I have stopped all work, as I have to get back to my real projects (nothing to do with markets).
Root mean squared error range (RMSER)Similarly to Bollinger bands, the RMSER gives a support and resistance areas for the trading price. Unlike bollinger bands, which use standard deviation, this support and resistance is calculated with 2 * the root mean squared error away from the moving average. This works very well with indices, like $SPX, and prices only fall outside the range during black swan events like the 2020 crash.
[blackcat] L1 Vitali Apirine OBVMLevel 1
Background
Traders’ Tips of April 2020, the focus is Vitali Apirine’s article in the April issue, “On-Balance Volume Modified (OBVM)”.
Function
In “On-Balance Volume Modified (OBVM)” in this issue, author Vitali Apirine presents a new indicator called OBVM that is based on the classic on-balance volume indicator originally developed by Joe Granville. The author has smoothed the OBV calculation and has added a signal line to help the trader identify entry and exit points. Apirine also notes that the OBVM indicator is useful in helping to identify divergences.
Remarks
Feedbacks are appreciated.
[blackcat] L1 Vitali Apirine Compare Price Momentum OscillatorLevel 1
Background
Traders’ Tips of August 2020, the focus is Vitali Apirine’s article in the August issue, “The Compare Price Momentum Oscillator (CPMO)”.
Function
In his article in this issue, “The Compare Price Momentum Oscillator (CPMO),” author Vitali Apirine reintroduces us to the DecisionPoint PMO originally developed by Carl Swenlin and presents a new way to use it to compare the relative momentum of two different securities. Trading signals can be derived in a number of ways including momentum, signal line, and zero-line crossovers.
Remarks
This is a Level 1 free and open source indicator.
Feedbacks are appreciated.
[blackcat] L2 James Garofallou RSI In 4 DimLevel 2
Background
Traders’ Tips of September 2020, the focus is James Garofallou’s article in the September issue, “Tracking Relative Strength In Four Dimensions”.
Function
In “Tracking Relative Strength In Four Dimensions” in this issue, author James Garofallou introduces us to a new method of measuring the relative strength of a security. This new technique creates a much broader reference than would be obtained by using a single security or index and combines several dimensions, as the author calls them, into a single rank value. This study compares a security to another in four dimensions, as explained in the article. James Garofallou presents a metric for a security’s strength relative to 11 major market sectors and over several time periods. All this is squeezed into a single value. The first step is the RS2. It normalizes the security to a market index, then calculates four moving averages and encodes their relations in a returned number. I just modified it by using most BTC-correlated instruments to reflect how BTC response to their performance.
Remarks
This is a Level 2 free and open source indicator.
Feedbacks are appreciated.
Esqvair's Neural Reversal Probability IndicatorIntroduction
Esqvair's Neural Reversal Probability Indicator is the indicator that shows probability of reversal.
Warning: This script should only be used on 1 minute chart.
How to use
When a signal appears (by default it is a green bar), a reversal should be expected.
The signal appears when the indicator value >= Threshold.
If you want more signals, you must lower the threshold, if less, you must increase the threshold.
For some assets, like Forex pairs, you have to optimize the threshold yourself, but for most stocks, the default threshold works well.
How well a threshold fits an asset depends on the volatility of the asset.
For most assets, the indicator ranges from 35 to 75.
Settings
Smoothing - The default is 1, which means no smoothing. Indicator smoothing by SMA.
Threshold - default 71.0 is responsible for the occurrence of signals, read "How to use" part to learn more
The Indicator
This indicator is a pre-trained neural network that was trained outside of TradingView and then its structure and weights values were converted to PineScript.
Warning: A neural network is a black box in the sense that although it can approximate any function, studying its structure will not give you any idea about the structure of the function being approximated.
Possible questions
Why does the indicator value most time range from 35 to 75 when the probability should ranges from 0 to 100?
-Due to some randomness in the markets, a neural network can never be 100% sure.
What data was used to train the neural network?
-This was BTCUSD 1 minute chart data from 02/05/2020 to 02/05/2022.
Where did you train the neural network and convert it to PineScript?
-I used a programming language that I know.
NormalizedOscillatorsLibrary "NormalizedOscillators"
Collection of some common Oscillators. All are zero-mean and normalized to fit in the -1..1 range. Some are modified, so that the internal smoothing function could be configurable (for example, to enable Hann Windowing, that John F. Ehlers uses frequently). Some are modified for other reasons (see comments in the code), but never without a reason. This collection is neither encyclopaedic, nor reference, however I try to find the most correct implementation. Suggestions are welcome.
rsi2(upper, lower) RSI - second step
Parameters:
upper : Upwards momentum
lower : Downwards momentum
Returns: Oscillator value
Modified by Ehlers from Wilder's implementation to have a zero mean (oscillator from -1 to +1)
Originally: 100.0 - (100.0 / (1.0 + upper / lower))
Ignoring the 100 scale factor, we get: upper / (upper + lower)
Multiplying by two and subtracting 1, we get: (2 * upper) / (upper + lower) - 1 = (upper - lower) / (upper + lower)
rms(src, len) Root mean square (RMS)
Parameters:
src : Source series
len : Lookback period
Based on by John F. Ehlers implementation
ift(src) Inverse Fisher Transform
Parameters:
src : Source series
Returns: Normalized series
Based on by John F. Ehlers implementation
The input values have been multiplied by 2 (was "2*src", now "4*src") to force expansion - not compression
The inputs may be further modified, if needed
stoch(src, len) Stochastic
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
ssstoch(src, len) Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Introduced in the January 2014 issue of Stocks and Commodities
This is not an implementation of MESA Stochastic, as it is based on Highpass filter not present in the function (but you can construct it)
This implementation is scaled by 0.95, so that Super Smoother does not exceed 1/-1
I do not know, if this the right way to fix this issue, but it works for now
netKendall(src, len) Noise Elimination Technology by John F. Ehlers
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Introduced in the December 2020 issue of Stocks and Commodities
Uses simplified Kendall correlation algorithm
Implementation by @QuantTherapy:
rsi(src, len, smooth) RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
vrsi(src, len, smooth) Volume-scaled RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
This is my own version of RSI. It scales price movements by the proportion of RMS of volume
mrsi(src, len, smooth) Momentum RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018)
rrsi(src, len, smooth) Rocket RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018)
Does not include Fisher Transform of the original implementation, as the output must be normalized
Does not include momentum smoothing length configuration, so always assumes half the lookback length
mfi(src, len, smooth) Money Flow Index
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
lrsi(src, in_gamma, len) Laguerre RSI by John F. Ehlers
Parameters:
src : Source series
in_gamma : Damping factor (default is -1 to generate from len)
len : Lookback period (alternatively, if gamma is not set)
Returns: Oscillator series
The original implementation is with gamma. As it is impossible to collect gamma in my system, where the only user input is length,
an alternative calculation is included, where gamma is set by dividing len by 30. Maybe different calculation would be better?
fe(len) Choppiness Index or Fractal Energy
Parameters:
len : Lookback period
Returns: Oscillator series
The Choppiness Index (CHOP) was created by E. W. Dreiss
This indicator is sometimes called Fractal Energy
er(src, len) Efficiency ratio
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Based on Kaufman Adaptive Moving Average calculation
This is the correct Efficiency ratio calculation, and most other implementations are wrong:
the number of bar differences is 1 less than the length, otherwise we are adding the change outside of the measured range!
For reference, see Stocks and Commodities June 1995
dmi(len, smooth) Directional Movement Index
Parameters:
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Based on the original Tradingview algorithm
Modified with inspiration from John F. Ehlers DMH (but not implementing the DMH algorithm!)
Only ADX is returned
Rescaled to fit -1 to +1
Unlike most oscillators, there is no src parameter as DMI works directly with high and low values
fdmi(len, smooth) Fast Directional Movement Index
Parameters:
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Same as DMI, but without secondary smoothing. Can be smoothed later. Instead, +DM and -DM smoothing can be configured
doOsc(type, src, len, smooth) Execute a particular Oscillator from the list
Parameters:
type : Oscillator type to use
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Chande Momentum Oscillator (CMO) is RSI without smoothing. No idea, why some authors use different calculations
LRSI with Fractal Energy is a combo oscillator that uses Fractal Energy to tune LRSI gamma, as seen here: www.prorealcode.com
doPostfilter(type, src, len) Execute a particular Oscillator Postfilter from the list
Parameters:
type : Oscillator type to use
src : Source series
len : Lookback period
Returns: Oscillator series
BTC Cap Dominance RSIBTC Cap Dominance RSI indicator is a combination of the RSI of Bitcoin Market Cap and the RSI of Bitcoin Dominance. The concept of this indicator is to get a good grasp of the bitcoin market flow by combining bitcoin dominance as well as bitcoin market cap.
BTC Cap Dominance (BCD) RSI is defined as:
BCD RSI = (BTC Cap RSI + BTC Dominance RSI) / 2
Case 1 (Bull market):
Both Cap RSI and Dominance RSI values are high
Case 2 (Neutral market):
Cap RSI is high but Dominance RSI is low
Cap RSI is low but Dominance RSI is high
Case 3 (Bear market):
Both Cap RSI and Dominance RSI values are low
(Note) Please note that the market capitalization symbols (CRYPTOCAP:TOTAL and CRYPTOCAP:TOTAL2) of TradingView started in January 2020, so you can check the indicator value from this point on.
Heikin Ashi Candles [Improved] - real closing priceHeikin Ashi Candles - with real closing price!, and with a Heikin Ashi based moving average!
One of the main functionality of Heikin Ashi is to provide a smoothed price to get clarity in trends. This script builds upon the Heikin Ashi Candlestick I published in 2020 that took the standard Heikin Ashi calculations and added the actual closing price.
The improved version adds a new smoothing calculation to give traders an even better way to display trends. In addition to that, a Heikin Ashi based moving average is added to guide you to take trades in the trend direction. The best part is the users have the ability to display both the real closing price and the Improved Heikin Ashi closing price.
With a few modifications within the smoothing settings, the users can get features like Candle Cloud that provides traders with functionalities like Ichimoku clouds.
Usage:
This indicator serves as a trend tool with enhanced features of the original Heikin Ashi calculations. The indicator help traders to take trades in the same direction of the trend. The new improved version gives traders a new better way to make use of all Heikin Ashi strategies that have been developed during the years. Apply the same methods with this new version.
Disclaimer: No financial advice, only for educational/entertainment purposes.
XLNX-AMD arb calcOn October 27th 2020, AMD formally announced an acquisition of Xilinx , Inc for 35B.
Per the terms of the deal, each 1 Xilinx share was to be converted into 1.7234 AMD shares.
This script visualizes the upside gap between current Xilinx price and expected closing price as a percentage.
Permabull Profit RatioCumulative profit/loss of market bulls.
Price is only half the story - volume is just as important. This indicator combines both to calculate the ratio of profitable longs to losing longs.
Presumably the banks like to reset this to negative territory now and then (eg March 2020) - which is always a great time to buy. Right now we see a modern record of profitable longs - probably not the best entry, but not an indicator of imminent doom. However it does mean that the "fall will be great" when it comes.
Price is only half the story - volume is just as important. This indicator combines both to calculate the cumulative profit/loss of market bulls.
How Old Is this Bull Run Getting? Check MA Test Bars SinceThere are many price-based techniques for anticipating the end of a move. However, the simple passage of time can also help because bull markets don’t last forever. While old age doesn’t necessarily cause investors to sell, a reversal becomes more likely the longer a trend lasts.
So, how long have prices been going up? There are various ways to measure that. Our earlier script, MA streak , offered one solution by counting the number of bars that a given moving average has been rising or falling.
Today’s script takes a different approach by counting the number of candles since price touched or crossed a given moving average. It tracks the 50-day simple moving average (SMA) by default. It can be adjusted to other types like exponential and weighted with the AvgType input.
In the chart above, Bars Since MA Test was adjusted to use the 200-day SMA. Viewing the S&P 500 with this study helps put the current market into context.
We can see that prices last touched the 200-day SMA 386 sessions ago (June 29, 2020). That’s relatively long based on history, but not unprecedented. For example, the indicator was at 407 in February 2018 as the market pulled back. It also hit 475 in October 2014 (following the breakout above 2007 highs).
Additionally, the S&P 500 is nearing the record of the 1990s bull market (393 candles on July 12, 1996).
Before that, you have to look all the way back to the 1950s, when it twice peaked at 627.
The conclusion? The current run without a test of the 200-day SMA is above average, but not yet record-setting. It may be interesting to watch as earnings season approaches and the Federal Reserve looks to tighten monetary policy.
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TAPLOT Wick PlayWick play is a candle stick long setup popularized by 2020 US Investing Champion Oliver Kell
Wick Play is a setup in which:
Strong stock with momentum to the upside closes off the highs of the day in yesterdays session.
Today's session opens in the candle "wick" of yesterdays bar.
If stock holds in the wick and doesn't trade in the body of yesterdays candle, it is a sign of strength.
Trigger happens on day 3 when price moves over the high of the wick inside day.
It is important to note that this setup should not be traded in isolation. Not every wick play is buyable.
This is to be used in the context of strong stocks like True Market Leaders and with buying momentum behind them.
Indicator places an up arrow under the candle setup. It also colors the candle itself. All configurable through indicator settings.
Cheers!
Yield Curve Inversion IndicatorIntroduction
The last time (as of this publishing) that this indicator detected an inverted interest rate yield curve was on February 20th, 2020 at 12:30pm EST, the afternoon before the S&P500 began one of its largest crashes in US history. The vast majority of major economic recessions since the 1950's have been preceded by an interest rate yield curve inversion. I created this indicator originally as an input to study the impacts of more conservative risk management on quantitative trading strategies following a yield curve inversion event. It is being shared with the community as a quick indicator to check to see the comparative status of short term and long term interest rates, and as an indicator where you can easily check to see if we are experiencing an inverted yield curve in real-time.
Background of the significance of an inverted yield curve:
"What an inverted yield curve really means is that most investors believe that short-term interest rates are going to fall sharply at some point in the future. As a practical matter, recessions usually cause interest rates to fall. Historically, inversions of the yield curve have preceded recessions in the U.S. Due to this historical correlation, the yield curve is often seen as a way to predict the turning points of the business cycle. When the yield curve inverts, short-term interest rates become higher than long-term rates. This type of yield curve is the rarest of the three main curve types and is considered to be a predictor of economic recession. Because of the rarity of yield curve inversions, they typically draw attention from all parts of the financial world." (www.investopedia.com)
Settings and Usage
This indicator pulls in pricing data from tickers that represent short term and long term interest rates, and compares them. The red line represents short term interest rates, and the green line represents long term interest rates. When the red line is above the green line, it indicates that we are experiencing a yield curve inversion. Small blue crosses also appear on the bottom of the indicator during an inversion to further highlight the event visually. This indicator pulls in the same information on the same two interest rate tickers regardless of what chart it is applied to.
Other Thoughts
This script uses the f_secureSecurity function as a best practice. For those that are versed in PineScript, code from this indicator could be adapted to be applied to an interest rate chart that allows custom alerts to be created the moment that there is an inverted interest rate yield curve.
FBMKLCI VolumeVolume for FBMKLCI Malaysia. Note that the volume starts on 27th Oct 2020 due to the change of securities in index. There are 30 securities combined into single volume.
