Electrified Aggressive Momentum SignalWhat this can be used for:
If you've already decided you want to trade a symbol, this can identify points of momentum alignment.
If a strong move has recently happened and you're looking for a change in momentum.
How it works:
This is a weighted combination of a Stochastic RSI and two modified SuperTrend (ATR Trailing Stop) indicators:
The Stochastic RSI signal is based upon aligned momentum and is negated at the overbought and oversold points.
The SuperTrend formula uses high and low values for calculation and both fast and slow can be adjusted for sensitivity.
Philosophy:
Signals have to be useful to humans. If a signal occurs to late, you've missed it. The intent of this indicator is to assist in timing a trade at very short time-frames. It assumes your conviction about a trade already exists, but you are trying to get an optimal entry.
Opposing momentum (weak signal) within an uptrend can be a sign that you should wait before entering. The frequency of a signal can indicate the strength of the trend. As the frequency of the aligned signal value decreases so does the reward vs risk.
Поиск скриптов по запросу "墨尔本城vs西部联"
Dominant Cycle Adaptive MACDThis Indicator is based on classic MACD but with an exceptional smoothing.
This smoothing eliminates the noise of the classic MACD as you see in the Chart
Adaptive MACD is compiled using with two adaptive moving averages, one adaptive to the dominant cycle and the other adaptive to twice the dominant cycle. As the basic behind the MACD is the difference of two moving averages we cannot find much difference between the conventional MACD (12, 26) and the adaptive MACD. However the adaptive MACD is less prone for less whipsaws and it catches the trends very well at the same time the catches the turning points in time. The Adaptive MACD is definite one notch better than the conventional MACD.
Dominant Cycle Period is calculated using Ehler's Method {Mentioned in the code}
This is how the Adaptiveness Impacts the Price Chart
1. (12, 26 EMA) VS Adaptive Dominant Cycle EMA
2. See how the Adaptive Lengths {both FastLength and SlowLength changes with time!}
Enjoy!
Ehlers Instantaneous Trend Line VS Kalman 1-dimensional filterThis indicator is showing difference between Ehlers Instantaneous Trend Line and Kalman 1-dimensional filter.
It is useful to see overbought and oversold areas of the chart.
I am using it on BTCUSD pair on 30, 45, 1H intervals.
I hope you will find it as useful as I do.
Better OBVOBV with William C. Garrett's Approximation
In the classical OBV (On-Balance Volume) indicator, it simply takes the idea from traditional tape reading - treat the "up tick" as Buy, "down tick" as Sell, and it assumes no change in price as neutral* (*which is not the case in tape reading).
When it comes to interpret the daily volume as such, errors will add up cumulatively. For example, there are days when a Doji Star with high volume just merely one cent higher than yesterday price and the whole day volume would be taken as a BUY Volume....
Here is a gentlemen, William C. Garrett, attempted to break down the daily volume into two parts in his book - "Torque Analysis of Stock Market Cycle".
Published indicator has two modes: Cumulative and Time Segmented. Time Segmented Volume (TSV) - performs a MACD operation on the Garrett Money Flow.
Note on Divergence:
When using a indicator as Time Segmented Money Flow, divergence would surely occur on and off. This is where Wyckoff 3rd principle comes into play - "Effort vs Result" that is not matching. Meaning that the cumulation of shares goes in one direction while the price goes another direction.
COT GRAINS FUNDS NET POSITION(GRAINSTATS)- Retrieves fund net position from CFTC Commitments of Traders(COT) Reports
- Overlays fund net positions on left y-axis vs price on right y-axis
- Current supported Grain Products
- Corn (CBOT) (QUANDL: 002602)
- Soybeans (CBOT) (QUANDL: 005602)
- Kansas City Wheat(CBOT) (Hard Red Winter) (QUANDL: 001612)
- Oats (CBOT) (QUANDL: 004603)
- Soybean Meal (CBOT) (QUANDL: 026603)
- Soybean Oil (CBOT) (QUANDL: 007601)
- Wheat (CBOT) (Soft Red Winter) (QUANDL: 001602)
(MGEX WHEAT IS UNSUPPORTED)
FTX vs BitMex BTC Perp PremiumThis script compares FTX BTC/PERP (BTC perpetual futures contract) price to Binance BTC/USDT spot and colors it red. It also plots Bitmex's BTC perpetual futures compared to Binance spot and colors it blue.
FTX = red
Bitmex = blue
Now you can easily see that you can get a 1% discount on FTX perpetuals when it is tanking. This tells you that you can set your limit buy orders extra low if you like to catch crashes. Also, you can see what your average trader on each platform is willing to pay above or below the spot premium. In general you want to do the opposite of what the average trading is doing on these exchanges unless you're in the main bull or mania phase of the Bitcoin cycle.
If you want to use Bitstamp or your favorite exchange it can be changed in the settings. Let me know if you find any settings that work well for you.
May you profit and enjoy! :)
RS Line - Gauge Performance vs IndexOverview:
This implementation of the RS Line mimics how Investor's Business Daily and CANSLIM investors measure growth stock performance versus the S&P 500.
If you are looking at a weekly chart, the RS Line is the performance of the stock over the past week versus the S&P 500 over that same time frame. The same logic applies to the daily and monthly charts, only the time frames are different.
If a stock moves up for the day/week/month and the S&P 500 does not, the RS Line will move up. If a stock ends the day/week/month flat, yet the S&P 500 moves up, the RS Line will go down.
Usage:
- Look for an upward sloping line.
- The steeper the line, the better.
- Can be used for viewing long-term trend.
Pyramiding Entries On Early Trends (by Coinrule)Pyramiding the entries in a trading strategy may be risky but at the same time very profitable with a proper risk management approach. This strategy seeks to spot early signs of uptrends and increase the position's size while the right conditions persist.
Each trade comes with its stop-loss and take-profit to enforce a proportional risk/reward profile.
The strategy uses a mix of Moving Average based setups to define the buy-signal.
The Moving Average (200) is above the Moving Average (100), which prevents from buying when the uptrend is already in its late stages
The Moving Average (9) is above the Moving Average (100), indicating that the coin is not in a downtrend.
The price crossing above the Moving Average (9) confirms the potential upside used to fire the buy order.
Each entry comes with a stop-loss and a take-profit in a ratio of 1-to-1. After over 400 backtests, we opted for a 3% TP and 3% SL, which provides the best results.
The strategy is optimized on a 1-hour time frame.
The Advantages of this strategy are:
It offers the possibility of adjusting the size of the position proportionally to the confidence in the possibilities that an uptrend will eventually form.
Low drawdowns. On average, the percentage of trades in profit is above 60%, and the stop-loss equal to the take-profit reduces the overall risk.
This strategy returned good returns both with trading pairs with Fiat/stable coins and with BTC. Considering the mixed trends that cryptocurrencies experienced during 2020 vs BTC, this strengthens the strategy's reliability.
The strategy assumes each order to trade 20% of the available capital and pyramids the entries up to 7 times.
A trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange.
Everything RSIThis indicator includes:
RSI Candles set to the default 14 length (un check Borders in the Style tab to see the candlesticks better)
I like using the wicks as an early warning for a possible trend change, which is generally in the opposite direction of the wicks.
It's also easier for me to draw trend lines using the RSI Candles vs the rsi plot line.
40 ema of the RSI Candles
2nd RSI set to the 20 length , which plots just inside the wicks of the RSI Candles. This RSI also highlights Oversold and Overbought levels.
I sometimes leave the RSI Candle Borders checked and use the 20 RSI plot with the wicks of the RSI Candles
Signals to look for Short or Long opportunities , which use the 5 sma of the RSI Candles crossing under the overbought and over the
oversold levels. If you'd like to plot the 5 sma, remove the // at the beginning of the code on line 72.
3nd RSI set to the default 14 length which can be set to a different timeframe as the current chart. Default setting is the 1h.
This RSI plots a + at the top of the indicator when it's above the 50 level and an x at the bottom of the indicator when it's below the 50 level.
For me, this is just a visual aid when I'm scalping on lower timeframes.
If the 1h RSI is above the 50 level, I focus on long scalps. If the 1h RSI is below the 50 level, I focus on short scalps.
RSI Cloud which is formed by filling in the area between the 14 ema of both the 7 RSI and 28 RSI.
I used part of @FnM_Capital 's Trend-Sniper script for my RSI Candles. Thank you! You're extremely talented and deserve all of the credit for your work.
I'd also like to thank @SeanNance for answering all of my random coding questions!!!
I've added the indicator to the example twice to show a couple of the ways I view the RSI's.
The top indicator shows the RSI Candle Borders "un checked" and without the 2nd RSI plot.
The bottom indicator shows RSI Candle Borders "checked", using 2nd RSI plot with the RSI Candle Wicks.
bulls vs bear for VSA(Google translation from Russian.)
This indicator is based on the efficiency of bulls and bears for VSA and has the same parameters.
Its only difference is that all the values of the efficiency of bulls and bears for VSA indicator are sequentially added here.
I want to note that the Period spread - Bars from close to close argument was made for experimental purposes, and shows interesting results for values 3-4-5, in theory this is not very logical. Because then the estimate of the spread for this interval (3-4-5) will be divided by the estimate of the volume of the current bar.
Also added here is a moving average with a default period of 10 - theoretically, if the indicator is above the moving average - the initiative is behind the bulls - if below - the initiative is behind the bears.
It is recommended to use this indicator with the indicator:
efficiency of bulls and bears for VSA
Volume on bar VSA - indicator V2
Russian language.
Этот индикатор сделан на основе efficiency of bulls and bears for VSA и имеет такие же параметры.
Единственное отличие его в том, что тут последовательно складываются все значения индикатора efficiency of bulls and bears for VSA .
Хочу отметить, что аргумент Period spread - Bars from close to close (Период спреда - Баров от закрытия до закрытия) сделан в экспериментальных целях, и показывает интересные результаты на значения 3-4-5, в теории это не очень логично. Потому-что тогда будет делиться оценка спреда за этот интервал (3-4-5), на оценку величины объема текущего бара.
Также тут добавлена скользящая средняя с периодом по умолчанию 10 – теоретически, если индикатор над скользящей средней – инициатива за быками – если ниже – инициатива за медведями.
Это индикатор рекомендуется использовать с индикатором:
efficiency of bulls and bears for VSA
Volume on bar VSA - indicator V2
Price movement indicatorThis indicator shows us if the price is moving up or down based on the ratio of current price vs previous bar.
If this ratio is above 1 then this indicates that the price is increasing, while if the ratio is below 1 the price is decreasing.
HOW TO USE:
Set the desired EMA length, number of previous bars to compare with and offset.
Markets:
It can be used to all markets.
Sukrut relative strengthI am publising Relative Strength Comparative.
It is be used to compare a Stock's Performance against another stock/index (Default NIFTY50)
Example: ITC vs NIFTY 50 it will be ITC / NIFTY
RelativeStrengthComparative_IBD_YRKI am publising Relative Strength Comparative.
It is be used to compare a Stock's Performance against another stock/index (Default NIFTY50)
I also devised a Plot RS Rating which is inspired from IBD's RS Rating and matches to some extent. You can turn off/on the RS Rating as per need.
Example: ITC vs NIFTY 50 it will be ITC / NIFTY
The Indicator can be used in Multiple ways:
1) Check Relative Strength
2) Check RS Rating (This is not Accurate as of now since IBD compares the ratings of all the stocks in an Exchange)
3) Can be used as a Spread Chart for the Division (We need to not divide every time we change Stocks)
4) Design a Template exactly as MarketSmith by using the TradingView feature of "Move to --> Existing Pane Above"
The Formula i used for RS Rating is below with more weightage on the 3 month performance and lesser on 12 month Performance. I am open to Modification of this Formula if a better suggestion
// relative strength IBD style
ThreeMthRS = 0.4*(close/close)
SixMthRS = 0.2*(close/(close*2))
NineMthRS = 0.2*(close/(close*3))
TwelveMthRS = 0.2*(close/(close*4))
Buy the Dips (by Coinrule)Taking your first steps into automated trading may be challenging. Coinrule's mission is to make it as easy as possible, also for beginners.
Here follows the best trading strategy to get started with Coinrule. This strategy doesn't involve complex indicators, yet was proved to be effective in the long term for many coins. Results seem to be improved when trading a coin vs Bitcoin.
The strategy buys the dips of a coin to sell with a profit. A stop-loss protects every trade.
Crypto markets offer high volatility and, thus, excellent opportunities for trading. Excluding times of severe downtrend, buying the dip is a simple and effective long-term trading strategy. The buy-signal is set to a 2% drop in a 30-minutes time frame.
Each trade comes with a take profit and a stop loss. Both set at 2%.
You can adjust these percentages to the market volatility as an advanced setup. You can backtest the outcomes using the backtesting tool from Tradingview
The strategy assumes each order to trade 30% of the available capital. A trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange.
NYSE Advance/DeclineAn indicator for traders who trade intraday on the US market. It makes use of the symbols made available by TradingView to show the number of advancing vs declining stocks.
The GREEN AREA represents advancing issues. The INTENSE GREEN AREA represents the advancing issues at daily high.
The RED AREA represents declining issues. The INTENSE RED AREA represents the declining issues at daily low.
The part above the BLUE LINE represents issues which are trading above their VWAP (volume-weighted average prices).
Polynomial Regression Bands + Channel [DW]This is an experimental study designed to calculate polynomial regression for any order polynomial that TV is able to support.
This study aims to educate users on polynomial curve fitting, and the derivation process of Least Squares Moving Averages (LSMAs).
I also designed this study with the intent of showcasing some of the capabilities and potential applications of TV's fantastic new array functions.
Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as a polynomial of nth degree (order).
For clarification, linear regression can also be described as a first order polynomial regression. The process of deriving linear, quadratic, cubic, and higher order polynomial relationships is all the same.
In addition, although deriving a polynomial regression equation results in a nonlinear output, the process of solving for polynomials by least squares is actually a special case of multiple linear regression.
So, just like in multiple linear regression, polynomial regression can be solved in essentially the same way through a system of linear equations.
In this study, you are first given the option to smooth the input data using the 2 pole Super Smoother Filter from John Ehlers.
I chose this specific filter because I find it provides superior smoothing with low lag and fairly clean cutoff. You can, of course, implement your own filter functions to see how they compare if you feel like experimenting.
Filtering noise prior to regression calculation can be useful for providing a more stable estimation since least squares regression can be rather sensitive to noise.
This is especially true on lower sampling lengths and higher degree polynomials since the regression output becomes more "overfit" to the sample data.
Next, data arrays are populated for the x-axis and y-axis values. These are the main datasets utilized in the rest of the calculations.
To keep the calculations more numerically stable for higher periods and orders, the x array is filled with integers 1 through the sampling period rather than using current bar numbers.
This process can be thought of as shifting the origin of the x-axis as new data emerges.
This keeps the axis values significantly lower than the 10k+ bar values, thus maintaining more numerical stability at higher orders and sample lengths.
The data arrays are then used to create a pseudo 2D matrix of x power sums, and a vector of x power*y sums.
These matrices are a representation the system of equations that need to be solved in order to find the regression coefficients.
Below, you'll see some examples of the pattern of equations used to solve for our coefficients represented in augmented matrix form.
For example, the augmented matrix for the system equations required to solve a second order (quadratic) polynomial regression by least squares is formed like this:
(∑x^0 ∑x^1 ∑x^2 | ∑(x^0)y)
(∑x^1 ∑x^2 ∑x^3 | ∑(x^1)y)
(∑x^2 ∑x^3 ∑x^4 | ∑(x^2)y)
The augmented matrix for the third order (cubic) system is formed like this:
(∑x^0 ∑x^1 ∑x^2 ∑x^3 | ∑(x^0)y)
(∑x^1 ∑x^2 ∑x^3 ∑x^4 | ∑(x^1)y)
(∑x^2 ∑x^3 ∑x^4 ∑x^5 | ∑(x^2)y)
(∑x^3 ∑x^4 ∑x^5 ∑x^6 | ∑(x^3)y)
This pattern continues for any n ordered polynomial regression, in which the coefficient matrix is a n + 1 wide square matrix with the last term being ∑x^2n, and the last term of the result vector being ∑(x^n)y.
Thanks to this pattern, it's rather convenient to solve the for our regression coefficients of any nth degree polynomial by a number of different methods.
In this script, I utilize a process known as LU Decomposition to solve for the regression coefficients.
Lower-upper (LU) Decomposition is a neat form of matrix manipulation that expresses a 2D matrix as the product of lower and upper triangular matrices.
This decomposition method is incredibly handy for solving systems of equations, calculating determinants, and inverting matrices.
For a linear system Ax=b, where A is our coefficient matrix, x is our vector of unknowns, and b is our vector of results, LU Decomposition turns our system into LUx=b.
We can then factor this into two separate matrix equations and solve the system using these two simple steps:
1. Solve Ly=b for y, where y is a new vector of unknowns that satisfies the equation, using forward substitution.
2. Solve Ux=y for x using backward substitution. This gives us the values of our original unknowns - in this case, the coefficients for our regression equation.
After solving for the regression coefficients, the values are then plugged into our regression equation:
Y = a0 + a1*x + a1*x^2 + ... + an*x^n, where a() is the ()th coefficient in ascending order and n is the polynomial degree.
From here, an array of curve values for the period based on the current equation is populated, and standard deviation is added to and subtracted from the equation to calculate the channel high and low levels.
The calculated curve values can also be shifted to the left or right using the "Regression Offset" input
Changing the offset parameter will move the curve left for negative values, and right for positive values.
This offset parameter shifts the curve points within our window while using the same equation, allowing you to use offset datapoints on the regression curve to calculate the LSMA and bands.
The curve and channel's appearance is optionally approximated using Pine's v4 line tools to draw segments.
Since there is a limitation on how many lines can be displayed per script, each curve consists of 10 segments with lengths determined by a user defined step size. In total, there are 30 lines displayed at once when active.
By default, the step size is 10, meaning each segment is 10 bars long. This is because the default sampling period is 100, so this step size will show the approximate curve for the entire period.
When adjusting your sampling period, be sure to adjust your step size accordingly when curve drawing is active if you want to see the full approximate curve for the period.
Note that when you have a larger step size, you will see more seemingly "sharp" turning points on the polynomial curve, especially on higher degree polynomials.
The polynomial functions that are calculated are continuous and differentiable across all points. The perceived sharpness is simply due to our limitation on available lines to draw them.
The approximate channel drawings also come equipped with style inputs, so you can control the type, color, and width of the regression, channel high, and channel low curves.
I also included an input to determine if the curves are updated continuously, or only upon the closing of a bar for reduced runtime demands. More about why this is important in the notes below.
For additional reference, I also included the option to display the current regression equation.
This allows you to easily track the polynomial function you're using, and to confirm that the polynomial is properly supported within Pine.
There are some cases that aren't supported properly due to Pine's limitations. More about this in the notes on the bottom.
In addition, I included a line of text beneath the equation to indicate how many bars left or right the calculated curve data is currently shifted.
The display label comes equipped with style editing inputs, so you can control the size, background color, and text color of the equation display.
The Polynomial LSMA, high band, and low band in this script are generated by tracking the current endpoints of the regression, channel high, and channel low curves respectively.
The output of these bands is similar in nature to Bollinger Bands, but with an obviously different derivation process.
By displaying the LSMA and bands in tandem with the polynomial channel, it's easy to visualize how LSMAs are derived, and how the process that goes into them is drastically different from a typical moving average.
The main difference between LSMA and other MAs is that LSMA is showing the value of the regression curve on the current bar, which is the result of a modelled relationship between x and the expected value of y.
With other MA / filter types, they are typically just averaging or frequency filtering the samples. This is an important distinction in interpretation. However, both can be applied similarly when trading.
An important distinction with the LSMA in this script is that since we can model higher degree polynomial relationships, the LSMA here is not limited to only linear as it is in TV's built in LSMA.
Bar colors are also included in this script. The color scheme is based on disparity between source and the LSMA.
This script is a great study for educating yourself on the process that goes into polynomial regression, as well as one of the many processes computers utilize to solve systems of equations.
Also, the Polynomial LSMA and bands are great components to try implementing into your own analysis setup.
I hope you all enjoy it!
--------------------------------------------------------
NOTES:
- Even though the algorithm used in this script can be implemented to find any order polynomial relationship, TV has a limit on the significant figures for its floating point outputs.
This means that as you increase your sampling period and / or polynomial order, some higher order coefficients will be output as 0 due to floating point round-off.
There is currently no viable workaround for this issue since there isn't a way to calculate more significant figures than the limit.
However, in my humble opinion, fitting a polynomial higher than cubic to most time series data is "overkill" due to bias-variance tradeoff.
Although, this tradeoff is also dependent on the sampling period. Keep that in mind. A good rule of thumb is to aim for a nice "middle ground" between bias and variance.
If TV ever chooses to expand its significant figure limits, then it will be possible to accurately calculate even higher order polynomials and periods if you feel the desire to do so.
To test if your polynomial is properly supported within Pine's constraints, check the equation label.
If you see a coefficient value of 0 in front of any of the x values, reduce your period and / or polynomial order.
- Although this algorithm has less computational complexity than most other linear system solving methods, this script itself can still be rather demanding on runtime resources - especially when drawing the curves.
In the event you find your current configuration is throwing back an error saying that the calculation takes too long, there are a few things you can try:
-> Refresh your chart or hide and unhide the indicator.
The runtime environment on TV is very dynamic and the allocation of available memory varies with collective server usage.
By refreshing, you can often get it to process since you're basically just waiting for your allotment to increase. This method works well in a lot of cases.
-> Change the curve update frequency to "Close Only".
If you've tried refreshing multiple times and still have the error, your configuration may simply be too demanding of resources.
v4 drawing objects, most notably lines, can be highly taxing on the servers. That's why Pine has a limit on how many can be displayed in the first place.
By limiting the curve updates to only bar closes, this will significantly reduce the runtime needs of the lines since they will only be calculated once per bar.
Note that doing this will only limit the visual output of the curve segments. It has no impact on regression calculation, equation display, or LSMA and band displays.
-> Uncheck the display boxes for the drawing objects.
If you still have troubles after trying the above options, then simply stop displaying the curve - unless it's important to you.
As I mentioned, v4 drawing objects can be rather resource intensive. So a simple fix that often works when other things fail is to just stop them from being displayed.
-> Reduce sampling period, polynomial order, or curve drawing step size.
If you're having runtime errors and don't want to sacrifice the curve drawings, then you'll need to reduce the calculation complexity.
If you're using a large sampling period, or high order polynomial, the operational complexity becomes significantly higher than lower periods and orders.
When you have larger step sizes, more historical referencing is used for x-axis locations, which does have an impact as well.
By reducing these parameters, the runtime issue will often be solved.
Another important detail to note with this is that you may have configurations that work just fine in real time, but struggle to load properly in replay mode.
This is because the replay framework also requires its own allotment of runtime, so that must be taken into consideration as well.
- Please note that the line and label objects are reprinted as new data emerges. That's simply the nature of drawing objects vs standard plots.
I do not recommend or endorse basing your trading decisions based on the drawn curve. That component is merely to serve as a visual reference of the current polynomial relationship.
No repainting occurs with the Polynomial LSMA and bands though. Once the bar is closed, that bar's calculated values are set.
So when using the LSMA and bands for trading purposes, you can rest easy knowing that history won't change on you when you come back to view them.
- For those who intend on utilizing or modifying the functions and calculations in this script for their own scripts, I included debug dialogues in the script for all of the arrays to make the process easier.
To use the debugs, see the "Debugs" section at the bottom. All dialogues are commented out by default.
The debugs are displayed using label objects. By default, I have them all located to the right of current price.
If you wish to display multiple debugs at once, it will be up to you to decide on display locations at your leisure.
When using the debugs, I recommend commenting out the other drawing objects (or even all plots) in the script to prevent runtime issues and overlapping displays.
Log XZVery simple script that show the diffrence between ema vs rma and converting it to log type indicator
above 0 is buy
bellow zero sell
color buy trend is blue
color sell trend is red
can be used as nice filter to remove stupid signals
Hull Moving Average vs CandleA Hull moving average is applied to the chart with settings for user to change the period of the Hull MA.
And a value of a previous candle is compared to the HMA. If the HMA is above the previous candle value, then the strategy will buy.
If below, it will sell. The default setting for the HMA is 50 and the Candle is D1 (Daily timeframe)
So basically if the HMA value is greater than yesterdays candle open, buy. ( i use open as a price source to avoid repainting)
This example is put onto ETHBTC chart and currency changed from default to BTC and Equity set to 1 BTC and commission set to 0.25%
The ETHBTC chart seems to follow good old moving average more than other pairs.
The strategy can be used on any pair and timeframes.
A good idea for setting the HMA period is:
small timeframe=big period, big timeframe=small period.
range from period=3 to period=200 is about right, for charts from 4H to 1 minute.
This HMA strategy is fairly simple, best to use other sources of information in combination with moving averages.
And strict money management, risk/reward control is very important to any trading strategy.
Not intended as stand alone strategy but has potential for bot use, i will publish a study version soon, for use with alerts and API bots.
Configurable BB+RSI+Aroon strategy backtest for binary optionsI wanted to share this strategy that I use myself for binary options trading. After trading binary options for several years I have learned that every single day is unique... assets behave differently every single day. So, when I start the day I want to know which is the optimum combination of parameters in my indicators that will give me the signals I want during the day and I get that by doing a quick backtest of the parameters combination in a specific asset that same day. When trading Binary Options I usually do 3 or 4 trades max per day and, yes, there are moments in which even with the right backtest data the signals fail (I strongly believe that there is no strategy that guarantees 100% success in any type of trade, and this one here is not an exception - but has worked well with some assets). So, here is my contribution to improve your productivity by automating a bit that backtesting part.
How this script works?
It is a simple price crossunder / crossover Bollinger Bands (BB) with a confirmation from RSI overbought / oversold signals and a fast Aroon. You will see the BB plotted with its confirmations:
(1) a blue circle that plots in the chart when the price is coming back inside the channel (within the Bollinger Bands)
(2) an orange square that plots in the chart when the RSI is coming back from the overbought or oversold areas
(3) a triangle that could be red or green depending on the Aroon confirmation: Red if Aroon Down is crossing down Aroon Up or green if vice versa.
The strategy will call for long (Call) if:
(1) the price is crossing over the lower band of the Bollinger Bands, coming back inside the channel
(2) Aroon Up is crossing or has crossed above Aroon Down
(3) RSI is crossing over the oversold limit
Consequently, the strategy will call for a short (Put) if:
(1) the price is crossing down the upper band of the Bollinger Bands, coming back inside the channel
(2) Aroon Down is crossing or has crossed below Aroon Up
(3) RSI is crossing under the overbought limit
You can configure:
1. Aroon length (keep it as fast as possible: 3, 4 or 5 are recommended values)
2. The point where Aroon Up and Aroon Down cross to make the signal valid (50 is by default. It could also be 25 or 75)
3. The RSI length
4. RSI Overbought and Oversold limits (they do not need to be symmetric: you can use 29 and 93, for example)
5. Bollinger Bands length and standard deviation
6. Number of bars to keep your option open. Depending on the timeframe used, this will determine the time you will keep your binary option open. If you are in a 1 min chart and keep this parameter in 3, then you will need to configure your binary option to expire in 3 minutes.
How to evaluate your backtest?
In Binary Options you only need the success rate, so what I do is that when I am manually updating the parameters I keep my strategy tester window open checking the winning trades vs losing trades ratio ("Percent Profitable"). I personally will only keep an asset monitored looking for signals that day if the Percent Profitable on the backtest of the same day is above 80%.
Regarding the code: it is open, public and free. No need to ask for permission if you want to copy+paste and use it in whole or parts.
Happy pip hunting!
-marco
