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Forecasting

Grand Trend Forecasting - A Simple And Original Approach Today we'll link time series forecasting with signal processing in order to provide an original and funny trend forecasting method, the post share lot of information, if you just want to see how to use the indicator then go to the section "Using The Indicator". Time series forecasting is an area dealing with the prediction of future values of a series by using a specific model, the model is the main tool that is used for forecasting, and is often an expression based on a set of predictor terms and parameters, for example the linear regression (model) is a 1st order polynomial (expression) using 2 parameters and a predictor variable ax + b . Today we won't be using the linear regression nor the LSMA. In time series analysis we can describe the time series with a model, in the case of the closing price a simple model could be as follows : Price = Trend + Cycles + Noise The variables of the model are the components, such model is additive since we add the component with each others, we should be familiar with each components of the model, the trend represent a simple long term variation of high amplitude, the cycles are periodic fluctuations centered around 0 of varying period and amplitude, the noise component represent shorter term irregular variations with mean 0. As a trader we are mostly interested by the cycles and the trend, altho the cycles are relatively more technical to trade and can constitute parasitic fluctuations (think about retracements in a trend affecting your trend indicator, causing potential false signals). If you are curious, in signal processing combining components has a specific name, "synthesis" , here we are dealing with additive synthesis, other type of synthesis are more specific to audio processing and are relatively more complex, but could be used in technical analysis. So what to do with our components ? If we want to trade the trend, we should estimate right ? Estimating the trend component involve removing the cycle and noise component from the price, if you have read stuff about filters you should know where i'am going, yep, we should use filters, in the case of keeping the trend we can use a simple moving average of relatively high period, and here we go. However the lag problem, which is recurrent, come back again, we end up with information easier to interpret (here the trend, which is a simple fluctuation such as a line or other smooth curve) at the cost of decision timing, that is unfortunate but as i said the information, here the moving average output, is relatively simple, and could be easily forecasted right ? If you plot a moving average of high period it would be easier for you to forecast its future values. And thats what we aim to do today, provide an estimate of the trend that should be easy to forecast, and should fit to the price relatively well in order to produce forecast that could determine the position of future closing prices observations. Estimating And Forecasting The Trend The parameter of the indicator dealing with the estimation of the trend is length , with higher values of length attenuating the cycle and noise component in the price, note however that high values of length can return a really long term trend unlike a simple moving average, so a small value of length, 14 for example can still produce relatively correct estimate of trend. here length = 14. The rough estimate of the trend is t in the code, and is an IIR filter, that is, it is based on recursion. Now i'll pass on the filter design explanation but in short, weights are constants, with higher weights allocated to the previous length values of the filter, you can see on the code that the first part of t is similar to an exponential moving average with : t(n) = 0.9t(n-length) + 0.1*Price However while the EMA only use the precedent value for the recursion, here we use the precedent length value, this would just output a noisy and really slow output, therefore in order to create a better fit we add : 0.9*(t(n-length) - t(n-2length)) , and this create the rough trend estimate that you can see in blue. On the parameters, 0.9 is used since it gives the best estimate in my opinion, higher values would create more periodic output and lower values would just create a rougher output. The blue line still contain a residual of the cycle/noise component, this is why it is smoothed with a simple moving average of period length. If you are curious, a filter estimating the trend but still containing noisy fluctuations is called "Notch" filter, such filter would depending on the cutoff remove/attenuate mid term cyclic fluctuations while preserving the trend and the noise, its the opposite of a bandpass filter. In order to forecast values, we simply sum our trend estimate with the trend estimate change with period equal to the forecasting horizon period, this is a really really simple forecasting method, but it can produce decent results, it can also allows the forecast to start from the last point of the trend estimate. Using The Indicator We explained the length parameter in the precedent section, src is the input series which the trend is estimated, forecast determine the forecasting horizon, recommend values for forecast should be equal to length, length/2 or length*2, altho i strongly recommend length. here length and forecast are both equal to 14 . The corrective parameter affect the trend estimate, it reduce the overshoot and can led to a curve that might fit better to the price. The indicator with the non corrective version above, and the corrective one below. The source parameter determine the source of the forecast, when "Noisy" is selected the source is the blue line, and produce a noisy forecast, when "Smooth" is selected the source is the moving average of t , this create a smoother forecast. The width interval control...the width of the intervals, they can be seen above and under the forecast plot, they are constructed by adding/subtracting the forecast with the forecast moving average absolute error with respect to the price. Prediction intervals are often associated with a probability (determining the probability of future values being between the interval) here we can't determine such probability with accuracy, this require (i think) an analysis of the forecasting distribution as well as assumptions on the distribution of the forecasting error. Finally it is possible to see historical forecasts, that is, forecasts previously generated by checking the "Show Historical Forecasts" option. Examples Good forecasts mostly occur when the price is close to the trend estimate, this include the following highlighted periods on AMD 15TF with default settings : We can see the same thing at the end of EURUSD : However we can't always obtain suitable fits, here it is isn't sufficient on BTCUSD : We can see wide intervals, we could change length or use the corrective option to get better results, another option is to use a log scale. We will end the examples with the log SPX, who posses a linear trend, so for example a linear model such as a linear regression would be really adapted, lets see how the indicator perform : Not a great fit, we could try to use an higher length value and use "Smooth" : Most recent fits are quite decent. Conclusions A forecasting indicator has been presented in this post. The indicator use an original approach toward estimating the trend component in the closing price. Of course i should have given statistics related to the forecasting error, however such analysis is worth doing with better methods and in more advanced environment allowing for optimization. But we have learned some stuff related to signal processing as well as time series analysis, seeing a time series as the sum of various components is really helpful when it comes to make sense of chaotic and noisy series and is a basic topic in time series analysis. You can see that in this new year i work harder on the visual of my indicators without trying to fall in the label addict trap, something that i wasn't really doing before, let me know what do you think of it. Thanks for reading !
อินดิเคเตอร์ Pine Script™
โดย alexgrover
25
Scripting Tutorial B - TManyMA - Commission/FeesThis script is for a triple moving average strategy where the user can select from different types of moving averages, price sources, lookback periods and resolutions. Features: - 3 Moving Averages with variable MA types, periods, price sources, resolutions and the ability to disable each individually. - Crossovers are plotted on the chart with detailed information regarding the crossover (Ex: 50 SMA crossed over 200 SMA ) - Forecasting available for all three MAs. MA values are forecasted 5 values out and plotted as if a continuation to the MA. - Forecast bias also applies to all forecasting. Bias means we can forecast based on an anticipated bullish , bearish or neutral direction in the market. - To understand bias, please read the source code, or if you can't read the code just send me a message on here or Twitter . Twitter should be linked to my profile. - Ribbons added and on by default. Optional setting to disable the ribbons. 5 ribbons between MA1 and MA2 and another 5 between MA2 and MA3. - Ribbons are alpha-color coded based on their relation to their default MAs. - Ribbons are only visible between MAs if the MAs being compared share the same Type, Resolution, and Source because there is no way to consolidate those three in a simple manner. - Ribbon values are calculated based on calculated MA Periods between the MAs. - Converted the existing study into a strategy. - Strategy only enters long positions with a market order when MA crossovers occur. - Strategy exits positions when crossunders occur. - Trades 100% of the equity with one order/position by default. - Ability to disable trading certain crosses with input checks. - Ability to exit trades with a take profit or stop loss. - User input to allow quick changes to the take profit or stop loss percentages. - Strategy now calculates on every tick - Strategy also includes fixed commission values based on Coinbase standard order fees This script is meant as an educational script with well-formatted styling, and references for specific functions. *** PLEASE NOTE - THIS STRATEGY IS MEANT FOR LEARNING PURPOSES. DEPENDING ON IT'S CONFIGURATION IT MAY OR MAY NOT BE USEFUL FOR ACTUAL TRADING. THE STRATEGY IS NOT FINANCIAL ADVICE ***
กลยุทธ์ Pine Script™
โดย BigBitsIO
Scripting Tutorial 9 - TManyMA Strategy - Long Market Order OnlyThis script is for a triple moving average strategy where the user can select from different types of moving averages, price sources, lookback periods and resolutions. Features: - 3 Moving Averages with variable MA types, periods, price sources, resolutions and the ability to disable each individually - Crossovers are plotted on the chart with detailed information regarding the crossover (Ex: 50 SMA crossed over 200 SMA ) - Forecasting available for all three MAs. MA values are forecasted 5 values out and plotted as if a continuation to the MA. - Forecast bias also applies to all forecasting. Bias means we can forecast based on an anticipated bullish, bearish or neutral direction in the market. - To understand bias, please read the source code, or if you can't read the code just send me a message on here or Twitter. Twitter should be linked to my profile. - Ribbons added and on by default. Optional setting to disable the ribbons. 5 ribbons between MA1 and MA2 and another 5 between MA2 and MA3. - Ribbons are alpha-color coded based on their relation to their default MAs. - Ribbons are only visible between MAs if the MAs being compared share the same Type, Resolution, and Source because there is no way to consolidate those three in a simple manner. - Ribbon values are calculated based on calculated MA Periods between the MAs. - Converted the existing study into a strategy - Strategy only enters long positions with a market order when MA crossovers occur - Strategy exits positions when crossunders occur - Trades 100% of the equity with one order/position by default - Ability to disable trading certain crosses with input checks This script is meant as an educational script with well-formatted styling, and references for specific functions. *** PLEASE NOTE - THIS STRATEGY IS MEANT FOR LEARNING PURPOSES. DEPENDING ON IT'S CONFIGURATION IT MAY OR MAY NOT BE USEFUL FOR ACTUAL TRADING. THE STRATEGY IS NOT FINANCIAL ADVICE ***
กลยุทธ์ Pine Script™
โดย BigBitsIO
Scripting Tutorial 7 - Triple Many Moving Averages ResolutionsThis script is for a triple moving average indicator where the user can select from different types of moving averages, price sources, lookback periods and resolutions. Features: - 3 Moving Averages with variable MA types, periods, price sources, resolutions and the ability to disable each individually - Crossovers are plotted on the chart with detailed information regarding the crossover (Ex: 50 SMA crossed over 200 SMA ) - Forecasting available for all three MAs. MA values are forecasted 5 values out and plotted as if a continuation to the MA. - Forecast bias also applies to all forecasting. Bias means we can forecast based on an anticipated bullish, bearish or neutral direction in the market. - To understand bias, please read the source code, or if you can't read the code just send me a message on here or Twitter. Twitter should be linked to my profile. This script is meant as an educational script with well-formatted styling, and references for specific functions.
อินดิเคเตอร์ Pine Script™
โดย BigBitsIO
2
Scripting Tutorial 6 - Triple Many Moving Averages ForecastingThis script is for a triple moving average indicator where the user can select from different types of moving averages, price sources and lookback periods. Features: - 3 Moving Averages with variable MA types, periods, price sources and ability to disable each individually - Crossovers are plotted on the chart with detailed information regarding the crossover (Ex: 50 SMA crossed over 200 SMA ) - Forecasting available for all three MAs. MA values are forecasted 5 values out and plotted as if a continuation to the MA. - Forecast bias also applies to all forecasting. Bias means we can forecast based on an anticipated bullish, bearish or neutral direction in the market. - To understand bias, please read the source code, or if you can't read the code just send me a message on here or Twitter. Twitter should be linked on my profile. This script is meant as an educational script with well-formatted styling, and references for specific functions.
อินดิเคเตอร์ Pine Script™
โดย BigBitsIO
6
Forecasting - Holt’s Linear Trend ForecastingHolt's Forecasting method Holt (1957) extended simple exponential smoothing to allow the forecasting of data with a trend. This method involves a forecast equation and two smoothing equations (one for the level and one for the trend): Forecast equation: ŷ = l + h * b Level equation: l = alpha * y + (1 - alpha) * (l + b) Trend equation: b = beta * (l - l) + (1 - beta) * b where: l (or l) denotes an estimate of the level of the series at time t, b (or b) denotes an estimate of the trend (slope) of the series at time t, alpha is the smoothing parameter for the level, 0 ≤ alpha ≤ 1, and beta is the smoothing parameter for the trend, 0 ≤ beta ≤ 1. As with simple exponential smoothing, the level equation here shows that l is a weighted average of observation y and the one-step-ahead training forecast for time t, here given by l+b. The trend equation shows that b is a weighted average of the estimated trend at time t based on l-l and b, the previous estimate of the trend. The forecast function is not flat but trending. The h-step-ahead forecast is equal to the last estimated level plus h times the last estimated trend value. Hence the forecasts are a linear function of h.
อินดิเคเตอร์ Pine Script™
โดย capissimo
3
Stationary Extrapolated Levels OscillatorIntroduction The oscillator version of the stationary extrapolated levels indicator. The methodology behind the extrapolated levels where to minimize the risk of making a decision based only on a forecast, therefore the indicator plotted levels in order to determine possible reversal points, signals where generated when the detrended series crossed over/under those levels. The Indicator First we detrend the price, this is because forecasting the trend is often harder than a series without trend (stationarity > non-stationarity) , then we forecast the detrended price with a linear extrapolation over a period of length and apply a max/min filter twice to the forecast, the max/min filters are just the highest and lowest function in pine. So the max/min filter have lag length/2 , by applying it two times we have a lag of length which is the period of the forecast. Because we use highest and lowest we can apply min-max normalization in the form of : x' = (x - min(x, min'))/(max(x,max') - min(x, min')) where x is the detrended price, max' the highest of the forecast of x and min' the lowest of the forecast of x . This result in a scaled oscillator in a range of (1,0), When the indicator is equal to 1 or 0 there are high chances of reversals, more in depth this mean that the detrended price have crossed the highest/lowest of the forecast, when the indicator is equal to 0 or 1 for a long time this mean that the forecast was quite inaccurate, you can minimize risk by focusing on the cross between the detrended price and the 0.8/0.2 levels. Conclusion I've shown an oscillator version of my previous "Stationary extrapolated levels" indicator, the method involving taking the highest and lowest of the forecast is a great way to minimize the risk involved by time-series forecasting driven decisions. So i hope you find an use to it. Thanks for reading !
อินดิเคเตอร์ Pine Script™
โดย alexgrover
13
EW Correction on choppiness with CB Composite index [yoxxx]Sometimes it is more than convenient to differ fast from a genuine high or a B in an expanded flat (a very impulsive counter within a correction, resulting in an higher high than the genuine.) I tried to use the typical choppiness of Bs in general to indicate them (orange box in example). Therefore i used a momentum of close, relative to the bar's heights (=vola). I turned out, that it works like charme to show ALL correction phases in a trend cycle. (And hence the impulses as well.) (Credit:The choppiness of the Bs is a hint of Constance Brown, which i adopted. Bow to her.) Note1: I tested it on all Timeframes, but mainly on D; and only in bull markets. Note 2: I added the composite index of Constance Brown which is faster than the rsi and - special skill - can inidcate divergences to prices where rsi fails. Use it as a hint for choppiness changes to come. Since its strength lies in being not normalized, sometimes you may adjust the composite for fitiing it to the EW bars.I inserted a line for that in the script. Feedback welcome! yoxxx
อินดิเคเตอร์ Pine Script™
โดย dep0tyoxxx
1
Alpha-Sutte ModelThe Alpha-Sutte model is an ongoing project run by Ansari Saleh Ahmar, a lecturer and researcher at Universitas Negeri Makassar in Indonesia, that attempts to make forecasts for time series like how Arima and Holt-Winters models do. Currently Ahmar and his team have conducted research and published papers comparing the efficacy of the Alpha-Sutte and other models, such as Arima and Holt-Winters, on topics ranging from forecasting Turkey's CPI data, Bitcoin prices, Apple's stock prices, primary energy supply of Indonesia, to infant mortality rates in China. The Alpha-Sutte model in comparison to the other two models listed above shows promise in providing a more accurate forecast, and the project has been able to receive some of its funding from organizations such as the US Agency for International Development, which is a part of the US Federal Government, so maybe the project has some actual merit. How it works: In this model there are four values presented at the top of the window. 1) The first value in blue is the value of the Alpha-Sutte model whose purpose is to forecast the price of the current bar. 2) The second value in yellow is an adaptive version of the Alpha-Sutte model that I made. The purpose of the adaptive Alpha-Sutte model is to expand upon the Alpha-Sutte by allowing new information to be introduced, causing the value to change during the current period, hence the adaptiveness of it. 3) The third value in aqua is the moving average of the low% Sutte line which is a predictive line that is based off of the close and low of the current and previous periods. 4) The fourth value in red is the moving average of the high% Sutte line which is a predictive line that is based off of the close and high of the current and previous periods. Trend signals: If low% Sutte (aqua value/line) is greater than high% Sutte (red value/line) then this is a buy signal. If high% Sutte (red value/line) is greater than low% Sutte (aqua value/line) then this is a sell signal. Caveat: Even though this model's purpose is to forecast the future, will it be able to predict periods of large movements? No, of course not, but it will adjust quickly to try to make more accurate forecasts for the next period. This was also a reason why I made an adaptive version of this model to try to reduce some of the discrepancies between the Alpha Sutte and price when there is a large unexpected move. *WARNING before using this I would highly recommend that you look up "Sutte Indicator" online and read some of the papers about this model before you use this , even though this model has shown merit when compared to Arima and Holt-Winter models this is still an ongoing project.* Hopefully this project will actually come to something in the near future as the calculation for this time series predictive model is much easier to calculate and program in pine editor than something like an Arima model. *Also, if you know how to use R language there is a package for the "Alpha-Sutte model".*
อินดิเคเตอร์ Pine Script™
โดย SegaRKO
6
Linear ExtrapolationBasic extrapolator for forecast a time-series, all forecasts are mades length periods ahead. This is not a estimation of the exact price This should only be used for forecasting direction, dont expect the price to be at the same value of its forecast. Bias, Mean absolute error, Mean percentage error...etc look useless here, its better to use correlation as a accuracy measurement. Correlation(Forecast ,close,period) Rescaling for a better forecast ? Transforming a non-stationary signal to a stationary signal can increase the forecasting accuracy, this can be done by detrending. Here is a list of somes detrending methods: Auto-Bias : price - price Mean-Bias : price - price moving average Log transform : log(price/price moving average) Correlation : correlation(price,n,period)
อินดิเคเตอร์ Pine Script™
โดย alexgrover
7