BTMM|TDIThis is the trader's dynamic index inspired by Steve Mauro's BTMM strategy.
In addition to the RSI, Trendline, Baseline, Volatility Bands I have also included additional trend biases that are painted in the background to provide more confluence when the markets break out in either direction.
For convenience, a position size calculator is included for all users to quickly calculate lot sizes on forex pairs with difference account balance currencies. The calculator works accurately on forex pairs. DO NOT USE for crypto or indices as some brokers have unique contract sizes that could not be fully incorporated into the tool.
There is also data table that displays historical values of the RSI, Trendline, Baseline, and an EMA vs Price scoring procedure that covers the current candle (t0) and up to 3 candles back. The table is meant to provide a snapshot view of either bullish or bearish dominance that can be deciphered with a quick glance.
Komut dosyalarını "新泻天鹅vs川崎前锋" için ara
Helme-Nikias Weighted Burg AR-SE Extra. of Price [Loxx]Helme-Nikias Weighted Burg AR-SE Extra. of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm, but unlike the usual WB algo, this one uses Helme-Nikias weighting. This method is commonly used in speech modeling and speech prediction engines. This is a linear method of forecasting data. You'll notice that this method uses a different weighting calculation vs Weighted Burg method. This new weighting is the following:
w = math.pow(array.get(x, i - 1), 2), the squared lag of the source parameter
and
w += math.pow(array.get(x, i), 2), the sum of the squared source parameter
This take place of the rectangular, hamming and parabolic weighting used in the Weighted Burg method
Also, this method includes Levinson–Durbin algorithm. as was already discussed previously in the following indicator:
Levinson-Durbin Autocorrelation Extrapolation of Price
What is Helme-Nikias Weighted Burg Autoregressive Spectral Estimate Extrapolation of price?
In this paper a new stable modification of the weighted Burg technique for autoregressive (AR) spectral estimation is introduced based on data-adaptive weights that are proportional to the common power of the forward and backward AR process realizations. It is shown that AR spectra of short length sinusoidal signals generated by the new approach do not exhibit phase dependence or line-splitting. Further, it is demonstrated that improvements in resolution may be so obtained relative to other weighted Burg algorithms. The method suggested here is shown to resolve two closely-spaced peaks of dynamic range 24 dB whereas the modified Burg schemes employing rectangular, Hamming or "optimum" parabolic windows fail.
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
Things to know
Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Further reading
A high-resolution modified Burg algorithm for spectral estimation
Related Indicators
Levinson-Durbin Autocorrelation Extrapolation of Price
Weighted Burg AR Spectral Estimate Extrapolation of Price
Point of Control V2 The genesis of this project was to create a POC library that would be available to deliver volume profile information via pine to other scripts of indicators and strategies.
This is a republish of an invite only script to open access
This is the indicator version of the library function.
A few points of significance:
- Allows the choice of reset of the study period, day/week or bars. This is simple enough to expand to other conditions
- Bar count resets starting from the beginning of the data set (bar index =0) vs bars back from the end of the data set
- A 'period' in this context is the time between resets - the start of the POC (eg. start of Day or Week) until it resets (for example at the beginning of a next day or week)
- Automates the determination of the increment level rather than the user specifying ticks or price brackets
- Does not allow for setting the # of rows and then calculating the implied price increment levels
- When a period is complete it is often useful to look back at the POCs of historical periods, or extend them forward.
- This script will find the historical POCs around the current price and display them rather than extend all the historical POC lines to the right
- This script also looks across all the period POCs and identifies the master POC or what I call the Grand POC, and also the next 3 runner up POCs
This indicator is also available as a library.
BINANCE:BTCUSDT NSE:NIFTY OANDA:XAUUSD NASDAQ:AAPL TVC:USOIL
PointofControlLibrary "PointofControl"
POC_f()
The genesis of this project was to create a POC library that would be available to deliver volume profile information via pine to other scripts of indicators and strategies.
This is the indicator version of the library function.
A few things that would be unique with the built in
- it allows you to choose the kind of reset of the period, day/week or bars. This is simple enough to expand to other conditions
- it resets on bar count starting from the beginning of the data set (bar index =0) vs bars back from the end of the data set
- A 'period' in this context is the time between resets - the start of the POC until it resets (for example at the beginning of a new day or week)
- it will calculate an increment level rather than the user specifying ticks or price brackets
- it does not allow for setting the # of rows and then calculating the implied price levels
- When a period is complete it is often useful to look back at the POCs of historical periods, or extend them forward.
- This script will find the historical POCs around the current price and display them rather than extend all the historical POC lines to the right
- This script also looks across all the period POCs and identifies the master POC or what I call the Grand POC, and also the next 3 runner up POCs
There is a matching indicator to this library
EPS & SalesHi everyone,
I just adapted a little utility script to visualise EPS % increase (quarters vs Year -1) and sales.
I used the code from @ARUN_SAXENA and modified it to fix what I saw as issues.
(Using base 3M instead of 1M +
request.earnings(syminfo.tickerid, earnings.actual, ignore_invalid_symbol=true)
instead of
request.financial(syminfo.tickerid, "EARNINGS_PER_SHARE", "FQ")
Data will differ from MarketSmith because they use sometimes actual EPS sometimes standard, but think we can at least trust what we see in term of %
The tool is far from being perfect !
Trigonometric compare close vs obvTrigonometric compare
This is copy and mod of a script from alexgrower which did this great trigonometric math.
As there was this idea floating around from some unicorn doing it instead of close also with the ta.obv, why not compare them.
from a first idea:
green=bullish trend
red=baserish trend
blue=deciding and acceleration zone
or maybe SL hunting of whales
Plot1: trigonometrics for obv
Plot2: trigonometrics for close
Plot3: trigonometrics for obv-close
what to trade or how to trade no idea, just hat do post the basic idea of this compare.
have fun
Candle Strength IndicatorThe candle strength indicator depicts the average strength of the price action by evaluating bullish vs bearish candles.
The scale is relative to price fluctuation and the size of the candles for the particular ticker / market, so there are no significant levels.
A cross on the zero line would generally indicate a change in trend / sentiment.
This indicator may be useful as a filter for entries and use in confluence with other indicators.
Gold Silver SpreadGold silver Spread
Different Between Gold & Silver Price
Find Spread Opportunity
Gold Vs Silver Strength Strategy
Percentage Up/Down vs lowest/highestPercentage difference at current price (close) to lowest and highest certain number of bars ago (14, 36, 96).
Historical US Bond Yield CurvePreface: I'm just the bartender serving today's freshly blended concoction; I'd like to send a massive THANK YOU to all the coders and PineWizards for the locally-sourced ingredients. I am simply a code editor, not a code author. Many thanks to these original authors!
Source 1 (Aug 8, 2019):
Source 2 (Aug 11, 2019):
About the Indicator: The term yield curve refers to the yields of U.S. treasury bills, notes, and bonds in order from shortest to longest maturity date. The yield curve describes the shapes of the term structures of interest rates and their respective terms to maturity in years. The slope of the yield curve tells us how the bond market expects short-term interest rates to move in the future based on bond traders' expectations about economic activity and inflation. The best use of the yield curve is to get a sense of the economy's direction rather than to try to make an exact prediction. This indicator plots the U.S. yield curve as maturity (x-axis/time) vs yield (y-axis/price) in addition to historical yield curves and advanced data tickers . The visual array of historical yield curves helps investors visualize shifts in the yield curve that are useful when identifying & forecasting economic conditions. The bond market can help predict the direction of the economy which can be useful in crafting your investment strategy. An inverted 10y/2y yield curve for durations longer than 5 consecutive trading days signals an almost certain recession on the horizon. An inversion happens when short-term bonds pay better than longer-term bonds. There is Federal Reserve Board data that suggests the 10y3m may be a better predictor of recessions.
Features: Advanced dual data ticker that performs curve & important spread analysis, plus additional hover info. Advanced yield curve data labels with additional hover info. Customizable historical curves and color theme.
‼ IMPORTANT: Hover over labels/tables for advanced information. Chart asset and timeframe may affect the yield curve results; I have found consistently accurate results using BINANCE:BTCUSDT on 1d timeframe. Historical curve lookbacks will have an effect on whether the curve analysis says the curve is bull/bear steepening/flattening, so please use appropriate lookbacks.
⚠ DISCLAIMER: Not financial advice. Not a trading system. DYOR. I am not affiliated with the original authors, TradingView, Binance, or the Federal Reserve Board.
About the Editor: I am a former FINRA Registered Representative, inventor/patent holder, futures trader, and hobby PineScripter.
Goertzel Cycle Period [Loxx]Goertzel Cycle Period is an indicator that uses Goertzel algorithm to extract the cycle period of ticker's price input to then be injected into advanced, adaptive indicators and technical analysis algorithms.
The following information is extracted from: "MESA vs Goertzel-DFT, 2003 by Dennis Meyers"
Background
MESA which stands for Maximum Entropy Spectral Analysis is a widely used mathematical technique designed to find the frequencies present in data. MESA was developed by J.P Burg for his Ph.D dissertation at Stanford University in 1975. The use of the MESA technique for stocks has been written about in many articles and has been popularized as a trading technique by John Ehlers.
The Fourier Transform is a mathematical technique named after the famed French mathematician Jean Baptiste Joseph Fourier 1768-1830. In its digital form, namely the discrete-time Fourier Transform (DFT) series, is a widely used mathematical technique to find the frequencies of discrete time sampled data. The use of the DFT has been written about in many articles in this magazine (see references section).
Today, both MESA and DFT are widely used in science and engineering in digital signal processing. The application of MESA and Fourier mathematical techniques are prevalent in our everyday life from everything from television to cell phones to wireless internet to satellite communications.
MESA Advantages & Disadvantage
MESA is a mathematical technique that calculates the frequencies of a time series from the autoregressive coefficients of the time series. We have all heard of regression. The simplest regression is the straight line regression of price against time where price(t) = a+b*t and where a and b are calculated such that the square of the distance between price and the best fit straight line is minimized (also called least squares fitting). With autoregression we attempt to predict tomorrows price by a linear combination of M past prices.
One of the major advantages of MESA is that the frequency examined is not constrained to multiples of 1/N (1/N is equal to the DFT frequency spacing and N is equal to the number of sample points). For instance with the DFT and N data points we can only look a frequencies of 1/N, 2/N, Ö.., 0.5. With MESA we can examine any frequency band within that range and any frequency spacing between i/N and (i+1)/N . For example, if we had 100 bars of price data, we might be interested in looking for all cycles between 3 bars per cycle and 30 bars/ cycle only and with a frequency spacing of 0.5 bars/cycle. DFT would examine all bars per cycle of between 2 and 50 with a frequency spacing constrained to 1/100.
Another of the major advantages of MESA is that the dominant spectral (frequency) peaks of the price series, if they exist, can be identified with fewer samples than the DFT technique. For instance if we had a 10 bar price period and a high signal to noise ratio we could accurately identify this period with 40 data samples using the MESA technique. This same resolution might take 128 samples for the DFT. One major disadvantage of the MESA technique is that with low signal to noise ratios, that is below 6db (signal amplitude/noise amplitude < 2), the ability of MESA to find the dominant frequency peaks is severely diminished.(see Kay, Ref 10, p 437). With noisy price series this disadvantage can become a real problem. Another disadvantage of MESA is that when the dominant frequencies are found another procedure has to be used to get the amplitude and phases of these found frequencies. This two stage process can make MESA much slower than the DFT and FFT . The FFT stands for Fast Fourier Transform. The Fast Fourier Transform(FFT) is a computationally efficient algorithm which is a designed to rapidly evaluate the DFT. We will show in examples below the comparisons between the DFT & MESA using constructed signals with various noise levels.
DFT Advantages and Disadvantages.
The mathematical technique called the DFT takes a discrete time series(price) of N equally spaced samples and transforms or converts this time series through a mathematical operation into set of N complex numbers defined in what is called the frequency domain. Why would we what to do that? Well it turns out that we can do all kinds of neat analysis tricks in the frequency domain which are just to hard to do, computationally wise, with the original price series in the time domain. If we make the assumption that the price series we are examining is made up of signals of various frequencies plus noise, than in the frequency domain we can easily filter out the frequencies we have no interest in and minimize the noise in the data. We could then transform the resultant back into the time domain and produce a filtered price series that hopefully would be easier to trade. The advantages of the DFT and itís fast computation algorithm the FFT, are that it is extremely fast in calculating the frequencies of the input price series. In addition it can determine frequency peaks for very noisy price series even when the signal amplitude is less than the noise amplitude. One of the disadvantages of the FFT is that straight line, parabolic trends and edge effects in the price series can distort the frequency spectrum. In addition, end effects in the price series can distort the frequency spectrum. Another disadvantage of the FFT is that it needs a lot more data than MESA for spectral resolution. However this disadvantage has largely been nullified by the speed of today's computers.
Goertzel algorithm attempts to resolve these problems...
What is the Goertzel algorithm?
The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of the individual terms of the discrete Fourier transform (DFT). It is useful in certain practical applications, such as recognition of dual-tone multi-frequency signaling (DTMF) tones produced by the push buttons of the keypad of a traditional analog telephone. The algorithm was first described by Gerald Goertzel in 1958.
Like the DFT, the Goertzel algorithm analyses one selectable frequency component from a discrete signal. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. For covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms, but for computing a small number of selected frequency components, it is more numerically efficient. The simple structure of the Goertzel algorithm makes it well suited to small processors and embedded applications.
The main calculation in the Goertzel algorithm has the form of a digital filter, and for this reason the algorithm is often called a Goertzel filter
Where is Goertzel algorithm used?
This package contains the advanced mathematical technique called the Goertzel algorithm for discrete Fourier transforms. This mathematical technique is currently used in today's space-age satellite and communication applications and is applied here to stock and futures trading.
While the mathematical technique called the Goertzel algorithm is unknown to many, this algorithm is used everyday without even knowing it. When you press a cell phone button have you ever wondered how the telephone company knows what button tone you pushed? The answer is the Goertzel algorithm. This algorithm is built into tiny integrated circuits and immediately detects which of the 12 button tones(frequencies) you pushed.
Future Additions:
Bartels test for cycle significance, testing output cycles for utility
Hodrick Prescott Detrending, smoothing
Zero-Lag Regression Detrending, smoothing
High-pass or Double WMA filtering of source input price data
References:
1. Burg, J. P., ëMaximum Entropy Spectral Analysisî, Ph.D. dissertation, Stanford University, Stanford, CA. May 1975.
2. Kay, Steven M., ìModern Spectral Estimationî, Prentice Hall, 1988
3. Marple, Lawrence S. Jr., ìDigital Spectral Analysis With Applicationsî, Prentice Hall, 1987
4. Press, William H., et al, ìNumerical Receipts in C++: the Art of Scientific Computingî,
Cambridge Press, 2002.
5. Oppenheim, A, Schafer, R. and Buck, J., ìDiscrete Time Signal Processingî, Prentice Hall,
1996, pp663-634
6. Proakis, J. and Manolakis, D. ìDigital Signal Processing-Principles, Algorithms and
Applicationsî, Prentice Hall, 1996., pp480-481
7. Goertzel, G., ìAn Algorithm for he evaluation of finite trigonometric seriesî American Math
Month, Vol 65, 1958 pp34-35.
Metals:Backwardation/ContangoMETALS: Gold , Silver , Copper ( GC , SI, HG)
Quickly visualize carrying charge market vs backwardized market by comparing the price of the next 2 years of futures contracts.
Carrying charge (contract prices increasing into the future) = normal, representing the costs of carrying/storage of a commodity. When this is flipped to Backwardation (contract prices decreasing into the future): its a bullish sign: Buyers want this commodity, and they want it NOW.
Note: indicator does not map to time axis in the same way as price; it simply plots the progression of contract months out into the future; left to right; so timeframe DOESN'T MATTER for this plot
There's likely some more efficient way to write this; e.g. when plotting for Gold ( GC ); 21 of the security requests are redundant; but they are still made; and can make this slower to load
TO UPDATE(once a year will do): in REQUEST CONTRACTS section, delete old contracts (top) and add new ones (bottom). Then in PLOTTING section, Delete old contract labels (bottom); add new contract labels (top); adjust the X in 'bar_index-(X+_historical)' numbers accordingly
This is one of three similar indicators: Meats | Metals | Grains
-If you want to build from this; to work on other commodities ; be aware that Tradingview limits the number of contract calls to 40 (hence the 3 seperate indicators)
Tips:
-Right click and reset chart if you can't see the plot; or if you have trouble with the scaling.
-Right click and add to new scale if you prefer this not to overlay directly on price. Or move to new pane below.
--Added historical input: input days back in time; to see the historical shape of the Futures curve via selecting 'days back' snapshot
updated 15th June 2022
© twingall
Grains:Backwardation/ContangoGRAINS: Wheat , Soybeans , Corn (ZW, ZS, ZC )
Quickly visualize carrying charge market vs backwardized market by comparing the price of the next 2 years of futures contracts.
Carrying charge (contract prices increasing into the future) = normal, representing the costs of carrying/storage of a commodity. When this is flipped to Backwardation (contract prices decreasing into the future): its a bullish sign: Buyers want this commodity, and they want it NOW.
The above chart shows a nice example of backwardation.
Note: indicator does not map to time axis in the same way as price; it simply plots the progression of contract months out into the future; left to right; so timeframe DOESN'T MATTER for this plot
There's likely some more efficient way to write this; e.g. when plotting for Wheat (ZW); 15 of the security requests are redundant; but they are still made; and can make this slower to load
TO UPDATE(once a year will do): in REQUEST CONTRACTS section, delete old contracts (top) and add new ones (bottom). Then in PLOTTING section, Delete old contract labels (bottom); add new contract labels (top); adjust the X in 'bar_index-(X+_historical)' numbers accordingly
This is one of three similar indicators: Meats | Metals | Grains
-If you want to build from this; to work on other commodities ; be aware that Tradingview limits the number of contract calls to 40 (hence the 3 seperate indicators)
Tips:
-Right click and reset chart if you can't see the plot; or if you have trouble with the scaling.
-Right click and add to new scale if you prefer this not to overlay directly on price. Or move to new pane below.
--Added historical input: input days back in time; to see the historical shape of the Futures curve via selecting 'days back' snapshot
updated 15th June 2022
© twingall
Meats: Backwardation/CantangoMEATS: Live Cattle , Feeder Cattle, Lean Hogs (LE, GF , HE)
Quickly visualize carrying charge market vs backwardized market by comparing the price of the next 2 years of futures contracts.
Carrying charge (contract prices increasing into the future) = normal, representing the costs of carrying/storage of a commodity. When this is flipped to Backwardation (contract prices decreasing into the future): its a bullish sign: Buyers want this commodity, and they want it NOW.
Note: indicator does NOT map to time axis in the same way as price; it simply plots the progression of contract months out into the future; left to right; so timeframe DOESN'T MATTER for this plot
There's likely some more efficient way to write this; e.g. when plotting for Live Cattle (LE); 8 of the security requests are redundant; but they are still made; and can make this slower to load
TO UPDATE(once a year will do): in REQUEST CONTRACTS section, delete old contracts (top) and add new ones (bottom). Then in PLOTTING section, Delete old contract labels (bottom); add new contract labels (top); adjust the X in 'bar_index-(X+_historical)' numbers accordingly
This is one of three similar indicators: Meats | Metals | Grains
-If you want to build from this; to work on other commodities ; be aware that Tradingview limits the number of contract calls to 40 (hence the 3 seperate indicators)
Tips:
-Right click and reset chart if you can't see the plot; or if you have trouble with the scaling.
-Right click and add to new scale if you prefer this not to overlay directly on price. Or move to new pane below.
--Added historical input: input days back in time; to see the historical shape of the Futures curve via selecting 'days back' snapshot
updated 15th June 2022
© twingall
741 GME vs AMCA very simple script to compare 7 shares of $AMC versus 1 share of $GME.
The script uses the closing price of the given securities.
Bjorgum Double Tap█ OVERVIEW
Double Tap is a pattern recognition script aimed at detecting Double Tops and Double Bottoms. Double Tap can be applied to the broker emulator to observe historical results, run as a trading bot for live trade alerts in real time with entry signals, take profit, and stop orders, or to simply detect patterns.
█ CONCEPTS
How Is A Pattern Defined?
Doubles are technical formations that are both reversal patterns and breakout patterns. These formations typically have a distinctive “M” or a “W” shape with price action breaking beyond the neckline formed by the center of the pattern. They can be recognized when a pivot fails to break when tested for a second time and the retracement that follows breaks beyond the key level opposite. This can trap entrants that were playing in the direction of the prior trend. Entries are made on the breakout with a target projected beyond the neckline equal to the height of the pattern.
Pattern Recognition
Patterns are recognized through the use of zig-zag; a method of filtering price action by connecting swing highs and lows in an alternating fashion to establish trend, support and resistance, or derive shapes from price action. The script looks for the highest or lowest point in a given number of bars and updates a list with the values as they form. If the levels are exceeded, the values are updated. If the direction changes and a new significant point is made, a new point is added to the list and the process starts again. Meanwhile, we scan the list of values looking for the distinctive shape to form as previously described.
█ STRATEGY RESULTS
Back Testing
Historical back testing is the most common method to test a strategy due in part to the general ease of gathering quick results. The underlying theory is that any strategy that worked well in the past is likely to work well in the future, and conversely, any strategy that performed poorly in the past is likely to perform poorly in the future. It is easy to poke holes in this theory, however, as for one to accept it as gospel, one would have to assume that future results will match what has come to pass. The randomness of markets may see to it otherwise, so it is important to scrutinize results. Some commonly used methods are to compare to other markets or benchmarks, perform statistical analysis on the results over many iterations and on differing datasets, walk-forward testing, out-of-sample analysis, or a variety of other techniques. There are many ways to interpret the results, so it is important to do research and gain knowledge in the field prior to taking meaningful conclusions from them.
👉 In short, it would be naive to place trust in one good backtest and expect positive results to continue. For this reason, results have been omitted from this publication.
Repainting
Repainting is simply the difference in behaviour of a strategy in real time vs the results calculated on the historical dataset. The strategy, by default, will wait for confirmed signals and is thus designed to not repaint. Waiting for bar close for entires aligns results in the real time data feed to those calculated on historical bars, which contain far less data. By doing this we align the behaviour of the strategy on the 2 data types, which brings significance to the calculated results. To override this behaviour and introduce repainting one can select "Recalculate on every tick" from the properties tab. It is important to note that by doing this alerts may not align with results seen in the strategy tester when the chart is reloaded, and thus to do so is to forgo backtesting and restricts a strategy to forward testing only.
👉 It is possible to use this script as an indicator as opposed to a full strategy by disabling "Use Strategy" in the "Inputs" tab. Basic alerts for detection will be sent when patterns are detected as opposed to complex order syntax. For alerts mid-bar enable "Recalculate on every tick" , and for confirmed signals ensure it is disabled.
█ EXIT ORDERS
Limit and Stop Orders
By default, the strategy will place a stop loss at the invalidation point of the pattern. This point is beyond the pattern high in the case of Double Tops, or beneath the pattern low in the case of Double Bottoms. The target or take profit point is an equal-legs measurement, or 100% of the pattern height in the direction of the pattern bias. Both the stop and the limit level can be adjusted from the user menu as a percentage of the pattern height.
Trailing Stops
Optional from the menu is the implementation of an ATR based trailing stop. The trailing stop is designed to begin when the target projection is reached. From there, the script looks back a user-defined number of bars for the highest or lowest point +/- the ATR value. For tighter stops the user can look back a lesser number of bars, or decrease the ATR multiple. When using either Alertatron or Trading Connector, each change in the trail value will trigger an alert to update the stop order on the exchange to reflect the new trail price. This reduces latency and slippage that can occur when relying on alerts only as real exchange orders fill faster and remain in place in the event of a disruption in communication between your strategy and the exchange, which ensures a higher level of safety.
👉 It is important to note that in the case the trailing stop is enabled, limit orders are excluded from the exit criteria. Rather, the point in time that the limit value is exceeded is the point that the trail begins. As such, this method will exit by stop loss only.
█ ALERTS
Five Built-in 3rd Party Destinations
The following are five options for delivering alerts from Double Tap to live trade execution via third party API solutions or chat bots to share your trades on social media. These destinations can be selected from the input menu and alert syntax will automatically configure in alerts appropriately to manage trades.
Custom JSON
JSON, or JavaScript Object Notation, is a readable format for structuring data. It is used primarily to transmit data between a server and a web application. In regards to this script, this may be a custom intermediary web application designed to catch alerts and interface with an exchange API. The JSON message is a trade map for an application to read equipped with where its been, where its going, targets, stops, quantity; a full diagnostic of the current state and its previous state. A web application could be configured to follow the messages sent in this format and conduct trades in sync with alerts running on the TV server.
Below is an example of a rendered JSON alert:
{
"passphrase": "1234",
"time": "2022-05-01T17:50:05Z",
"ticker": "ETHUSDTPERP",
"plot": {
"stop_price": 2600.15,
"limit_price": 3100.45
},
"strategy": {
"position_size": 0.1,
"order_action": "buy",
"market_position": "long",
"market_position_size": 0,
"prev_market_position": "flat",
"prev_market_position_size": 0
}
}
Trading Connector
Trading Connector is a third party fully autonomous Chrome extension designed to catch alert webhooks from TradingView and interface with MT4/MT5 to execute live trades from your machine. Alerts to Trading Connector are simple; just select the destination from the input drop down menu, set your ticker in the "TC Ticker" box in the "Alert Strings" section and enter your URL in the alert window when configuring your alert.
Alertatron
Alertatron is an automated algo platform for cryptocurrency trading that is designed to automate your trading strategies. Although the platform is currently restricted to crypto, it offers a versatile interface with high flexibility syntax for complex market orders and conditions. To direct alerts to Alertatron, select the platform from the 3rd party drop down, configure your API key in the ”Alertatron Key” box and add your URL in the alert message box when making alerts.
3 Commas
3 Commas is an easy and quick to use click-and-go third party crypto API solution. Alerts are simple without overly complex syntax. Messages are simply pasted into alerts and executed as alerts are triggered. There are 4 boxes at the bottom of the "Inputs" tab where the appropriate messages to be placed. These messages can be copied from 3 Commas after the bots are set up and pasted directly into the settings menu. Remember to select 3 Commas as a destination from the third party drop down and place the appropriate URL in the alert message window.
Discord
Some may wish to share their trades with their friends in a Discord chat via webhook chat bot. Messages are configured to notify of the pattern type with targets and stop values. A bot can be configured through the integration menu in a Discord chat to which you have appropriate access. Select Discord from the 3rd party drop down menu and place your chat bot URL in the alert message window when configuring alerts.
👉 For further information regarding alert setup, refer to the platform specific instructions given by the chosen third party provider.
█ IMPORTANT NOTES
Setting Alerts
For alert messages to be properly delivered on order fills it is necessary to place the following placeholder in the alert message box when creating an alert.
{{strategy.order.alert_message}}
This placeholder will auto-populate the alert message with the appropriate syntax that is designated for the 3rd party selected in the user menu.
Order Sizing and Commissions
The values that are sent in alert messages are populated from live metrics calculated by the strategy. This means that the actual values in the "Properties" tab are used and must be set by the user. The initial capital, order size, commission, etc. are all used in the calculations, so it is important to set these prior to executing live trades. Be sure to set the commission to the values used by the exchange as well.
👉 It is important to understand that the calculations on the account size take place from the beginning of the price history of the strategy. This means that if historical results have inflated or depleted the account size from the beginning of trade history until now, the values sent in alerts will reflect the calculated size based on the inputs in the "Properties" tab. To start fresh, the user must set the date in the "Inputs" tab to the current date as to remove trades from the trade history. Failure to follow this instruction can result in an unexpected order size being sent in the alert.
█ FOR PINECODERS
• With the recent introduction of matrices in Pine, the script utilizes a matrix to track pivot points with the bars they occurred on, while tracking if that pivot has been traded against to prevent duplicate detections after a trade is exited.
• Alert messages are populated with placeholders ; capability that previously was only possible in alertcondition() , but has recently been extended to `strategy.*()` functions for use in the `alert_message` argument. This allows delivery of live trade values to populate in strategy alert messages.
• New arguments have been added to strategy.exit() , which allow differentiated messages to be sent based on whether the exit occurred at the stop or the limit. The new arguments used in this script are `alert_profit` and `alert_loss` to send messages to Discord
RSI vs BITFINEX BTC Longs/Shorts Margin Ratio Percentage RankThis indicator plots the RSI of the current token with a color based on percentage rank of the RSI of BITFINEX:BTCUSDLONGS divided by BITFINEX:BTCUSDSHORTS, with a plot of the moving average of the RSI. It can optionally plot the RSI in white and the ratio RSI in color, or the ratio as background color. It can also plot the raw ratio rather than the percentage rank if selected.
I was interested in the ratio of BITFINEX:BTCUSDLONGS to BITFINEX:BTCUSDSHORTS as a measure of market sentiment and how that sentiment would magnify RSI changes. The volatility of the BTCUSDLONGS : BTCUSDSHORTS ratio was too low to get a good read, using a percent rank of the RSI of the ratio made the results more visible.
This indicator should be used on a BTC chart.
Number of New Highs - Number of New Lows in US MarketShow numbers of new highs vs numbers of new lows for Nasdaq, NYSE and AMEA
Polarity Divergences█ OVERVIEW
This indicator looks at the polarity of the intrabars composing a chart bar and fills chart candles orange when a majority of intrabars does not have the same polarity as that of the chart bar.
█ CONCEPTS
Bar polarity
By bar polarity , we mean the direction of a bar, which is determined by looking at the bar's close vs its open .
Intrabars
Intrabars are chart bars at a lower timeframe than the chart's. Each 1H chart bar of a 24x7 market will, for example, usually contain 60 bars at the lower timeframe of 1min, provided there was market activity during each minute of the hour. Mining information from intrabars can be useful in that it offers traders visibility on the activity inside a chart bar.
Lower timeframes (LTFs)
A lower timeframe is a timeframe that is smaller than the chart's timeframe. This script determines which LTF to use by examining the chart's timeframe. The LTF determines how many intrabars are examined for each chart bar; the lower the timeframe, the more intrabars are analyzed. This table shows which LTF is used for each range of chart timeframes:
Chart TF LTF
< 1D 1min
< 1W 30min
>= 1W 1D
█ HOW TO USE IT
The idea underlying this indicator is that if the polarity of a chart bar is up or down, the majority of its constituent intrabars should also be up or down. If that is not the case, then we can consider the bar to be abnormal, and so worthy of our attention. You will notice that such anomalies often occur before reversals, but irregularities do not necessarily announce reversals, as they also occur on pauses in trends or in trend-less conditions.
The indicator will update in real time, as intrabars composing the realtime chart bar are gradually built. The closer the realtime bar is to being closed, the more reliable the result will be, as that is when the indicator can analyze the most intrabars.
█ NOTES FOR Pine Script™ CODERS
This script uses the recently released request.security_lower_tf() Pine Script™ function discussed in this blog post . It works differently from the usual request.security() in that it can only be used at LTFs, and it returns an array containing one value per intrabar. This makes it much easier for programmers to access intrabar information.
A script can access a maximum of 100K intrabars; that is the reason we step the LTF by taking into account the chart's timeframe. By progressively increasing the LTF as the chart's timeframe increases, we provide users with optimal coverage of the chart bars. Always using a 1min LTF would, for example, limit the chart coverage to the last 69 bars on a 1D chart of a 24x7 market because each chart bar will usually contain 1440 intrabars (100K / 1440 = 69.4). By using a 30min LTF for 1D charts, we cover 100K / 48 = 2083 bars.
Look first. Then leap.
Volume Impulse & Candlestick Patterns - FontiramisuIndicator showing volume impulse & engulfing candlestick pattern.
You can set up multiple parameter for both events.
Volume Impulse :
Volume Period : Lenght of the average volume calculated.
Volume Multiplier : Factor to compare actual volume with average volume.
Engulfing Pattern :
VS avg body : Let you chose to compare body candle to average body of the last few candles (define with parameter : lenghtSizeAvgBody ), otherwise it will be compared to the last body candle.
Engulfing Multiplier : Factor to compare and validate the pattern.
EHMA Range StrategyThis script is a modified version of @borserman's script for the Exponential Hull Moving Average
All credit for the EHMA goes to him :)
In addition to the EHMA, this script works with a range around the EHMA (which can be modified), in an attempt to be robust against fake signals. Many times a bar will close below a moving average, only to reverse again the next bar, which eats away at your profits. Especially on shorter timeframes, but also on choppy longer timeframes this can make a strategy unattractive to use.
With the range around the EHMA, the strategy only enters a long/exit-short position if a bar crosses above the upper range. Vice versa, it only enters a short/exit-long position if a bar crosses below the lower range. This avoids positions if bars behave choppy within the EHMA range & only enters a position if the market is confident in it's direction. Having said that, fakeouts are still possible, but a lot less frequent. Having backtested this strategy vs the regular EHMA strategy (and having experimented with various settings), this version seems to be a lot more robust & profitable!
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as good as in historical backtesting.
This post and the script don’t provide any financial advice.
Ranged Volume DCA Strategy - R3c0nTraderUpdate: Republishing this as Public Open-Source script.
Credits:
Thank you "EvoCrypto" for granting me permission to use "Ranged Volume" to create this strategy.
Thank you "junyou0424" for granting me permission to use "DCA Bot with SuperTrend Emulator" which I used for adding bot inputs, calculations, and strategy
What does this do?
This script is mainly used for backtesting a Ranged Volume strategy to see how a 3Commas bot would perform.
I created this script out of necessity and I wanted a way to test a 3Commas DCA bot with a strategy based on “Volume.”
I came across "EvoCrypto’s" "Ranged Volume" study and strategy in TradingView and I liked it. I wanted to configure it so it can be used for DCA bot backtesting. I used parts from "junyou0424’s" "DCA Bot with SuperTrend Emulator" to add the following:
1. The Start Time and End Time
2. Price deviation to open safety orders (%)
3. Target Take Profit (%)
4. Trailing deviation
5. Base Order and Safety Order
6. Safety order volume scale
7. Safety order step scale
8. Max safety orders
In addition to the above, I also added chart indicators for "Take Profit" as well as "Safety Order"
Pre-requisites:
You can use this script without a 3Commas account and see how 3Commas DCA Bot and Ranged Volume strategy would perform vs. a non-DCA strategy. However, I highly recommend signing up for their free account and going through their training. This would give you a base understanding on the settings you will see in this strategy and why you will need to know them.
That said these are the pre-requisites I suggest you have:
1. Base Knowledge of 3Commas DCA bots
2. Base knowledge of settings such as “Max safety trades count”, “safety order volume scale” and “safety order step scale”. If these are alien to you, I suggest you read up on these.
3. Knowledge of setting up a Single-pair 3Commas bot for receiving custom TradingView signal.
4. A paper-bot to test your ideas. (Do not use a real money bot until you have tested it sufficiently with a paper-bot. You alone are responsible for your results!)
5. Add the study I created called "R3c0nTrader’s Ranged Volume Study” which adds a separate chart in its own pane showing the volume spikes. It will also generate the “buy” signals for your bot. NOTE: The study also has the same color scheme as this strategy and having the colors in both the strategy and the study will make things easier to see. If you use EvoCrypto’s Ranged Volume Study instead, just keep in mind that the colors won’t match, and you will have to manually match them.
6. Make your buy signals from your strategy are the same as in your study! To do this, use the same “Volume Range Length” you entered in the STRATEGY and enter that value for the “Volume Range Length” in the STUDY. Also ensure you have the same settings for “Heikin Ashi” (On or Off).
Comparisons of Ranged Volume Strategy vs Ranged Volume DCA Strategy
BTCUSD
Beware of Strategies that claim super high profits. This can easily be done by lowering the initial capital to something unrealistic. If I did that with this strategy and set the initial capital $100 and base order size to $100, I get a net profit of 2,864% which is not realistic.
How to Use
1. On the “Inputs” tab:
a. Set your Start and End Time to backtest against.
b. Set your “Volume Range Length” (number of bars to look back)
c. “Heikin Ashi Colors” – Usually I leave this enabled
d. “Show Bar Colors” – Leave enabled
e. “Show Break-Out” – Leave enabled
f. “Show Range” – Leave enabled
g. Set your other inputs which are those settings you would find in your 3Commas bot that you want to test (e.g., Price deviation to open safety orders, Target Take Profit, Base order, Safety order, etc.).
h. Quick Example for BTCUSD on 2hr chart:
i. Price deviation to open safety orders (%) = 6
ii. Target Take Profit (%) = 14
iii. Trailing deviation = 0
iv. Base order = 100
v. Safety order = 200
vi. Safety order volume scale = 2
vii. Safety order step scale = 1.4
viii. Max safety order = 5
2. On the “Properties” tab, set your initial capital, base currency, etc.
a. Initial capital – Default is 10,000 (Please use realistic values here. The amount here should be able to cover ALL your safety orders if they were triggered. Ideally, you should have funds left over and not use all trade capital.)
b. Base currency – Select your currency
c. Order Size - Not used. Use the “Inputs” tab to change your base order size.
d. Leave “Pyramiding” set to 999. This acts as a ceiling to the “Max safety orders” on the “Inputs” tab. It must always be higher than your “Max safety orders.” For example, if you set your “Max safety orders” to “4” and “Pyramiding” to “4” then it effectively means you have “3” “Max safety orders” and not “4” because it is counting each successive entry including the initial order.
e. “Commission” - Optional
f. “Verify price for limit orders” – Leave at zero. This does not change anything that I can tell.
g. Optional - Enter a value for “Commission”
h. Slippage – Optional. Slippage does not occur in backtesting but does occur in real trading but it can be simulated. Example use case for tracking performance of a real money bot: You enter the start date and time of your bot’s trade into this strategy and you notice some values are a little off due to slippage (average price, take profit, safety orders are not lining up) then you would go back here and increase the slippage until those lines up close enough with your actuals.
i. Margin for long positions – I don’t use this honestly.
j. Margin for short positions – I don’t use this honestly.
k. Recalculate “After order is filled” and “On every tick” – I don’t use this honestly.
3. “Style” tab
a. Ranged Volume Bar Coloring - You must disable bar coloring in any studies you added or this may not work properly
i. Color 0 – Default Yellow; appears when a volume breakout occurs
ii. Color 1 – Default Red; appears when a volume breakdown occurs
iii. Color 2 – Light Blue; appears when Close is higher than the Open
iv. Color 3 – Dark Blue; appears when the Close is lower than the Open
b. Take profit – Default Green; take profit line
c. Safety order – Default Light Blue; safety order line
d. No Safety Orders left – Default Red; when a trade runs out of safety orders, the line turns red and there is no safety orders left underneath to catch any further falling price movements.
e. Avg Position Price – Default Orange; your average position price for any given trade.
f. Take Profit Plot Area – Default Green; creates a highlighted area for your take profit
g. SO Plot Area – Default Light Blue; creates a highlighted area for your safety orders
h. Trades on chart – Show or hide your trades on the chart
i. Signal labels – Show or hide the trade signal labels on the chart
j. Quantity – Show or hide the trade quantity on the chart
Explanation of Chart lines and colors on chart
