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Charles Recession WatchThe “Recession Watch” indicator tracks 7 key economic metrics which have historically preceded US recessions. It provides a real-time indication of incoming recession risk. This indicator gives a picture of when risk is increasing, and therefore when you might want to start taking some money out of risky assets. All of the last seven recessions were preceded by a risk score of 3 or higher. Six of them were preceded by a risk score of 4 or higher. Unfortunately data prior to 1965 was inconsistent and prior recessions could not be considered. Based on the indicator hit rate at successfully flagging recessions over the last 50 years, risk scores have the following approximate probabilities of recession: - 0-1: Low - 2: 25% within next 18 months - 3: 30% within next 12 months - 4-7: 50% within next 12 months Note that a score of 3 is not necessarily a cause for panic. After all, there are substantial rewards to be had in the lead up to recessions (averaging 19% following yield curve inversion). For the brave, staying invested until the score jumps to 4+, or until the S&P500 drops below the 200day MA, will likely yield the best returns. Notes on use: - use MONTHLY time period only (the economic metrics are reported monthly) - If you want to view the risk Score (1-7) you need to set your chart axis to "Logarithmic" Enjoy and good luck!
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capriole_charles tarafından
2
Forecasting - Drift MethodIntroduction Nothing fancy in terms of code, take this post as an educational post where i provide information rather than an useful tool. Time-Series Forecasting And The Drift Method In time-series analysis one can use many many forecasting methods, some share similarities but they can all by classified in groups and sub-groups, the drift method is a forecasting method that unlike averages/naive methods does not have a constant (flat) forecast, instead the drift method can increase or decrease over time, this is why its a great method when it comes to forecasting linear trends. Basically a drift forecast is like a linear extrapolation, first you take the first and last point of your data and draw a line between those points, extend this line into the future and you have a forecast, thats pretty much it. One of the advantage of this method is first its simplicity, everyone could do it by hand without any mathematical calculations, then its ability to be non-conservative, conservative methods involve methods that fit the data very well such as linear/non-linear regression that best fit a curve to the data using the method of least-squares, those methods take into consideration all the data points, however the drift method only care about the first and last point. Understanding Bias And Variance In order to follow with the ability of methods to be non-conservative i want to introduce the concept of bias and variance, which are essentials in time-series analysis and machine learning. First lets talk about training a model, when forecasting a time-series we can divide our data set in two, the first part being the training set and the second one the testing set. In the training set we fit a model to the training data, for example : We use 200 data points, we split this set in two sets, the first one is for training which is in blue, and the other one for testing which is in green. Basically the Bias is related to how well a forecasting model fit the training set, while the variance is related to how well the model fit the testing set. In our case we can see that the drift line does not fit the training set very well, it is then said to have high bias. If we check the testing set : We can see that it does not fit the testing set very well, so the model is said to have high variance. It can be better to talk of bias and variance when using regression, but i think you get it. This is an important concept in machine learning, you'll often see the term "overfitting" which relate to a model fitting the training set really well, those models have a low to no bias, however when it comes to testing they don't fit well at all, they have high variance. Conclusion On The Drift Method The drift method is good at forecasting linear trends, and thats all...you see, when forecasting financial data you need models that are able to capture the complexity of the price structure as well as being robust to noise and outliers, the drift method isn't able to capture such complexity, its not a super smart method, same goes for linear regression. This is why more peoples are switching to more advanced models such a neural networks that can sometimes capture such complexity and return decent results. So this method might not be the best but if you like lines then here you go.
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alexgrover tarafından
18
Correlation Matrix by DaveattHi everyone A co-pinescripter friend told me this was impossible to do and we bet a free dinner tomorrow. Guess who's going to be invited to a very fancy restaurant tomorrow :) :) :) (hint: not him) What's the today script is about? This script is based on this MT4 correlation matrix Asset correlation is a measure of how investments move in relation to one another and when. ... Under what is known as modern portfolio theory, you can reduce the overall risk in an investment portfolio and even boost your overall returns by investing in asset combinations that are not correlated. I did it because it wasn't existing before with this format. What I discovered was only correlations shown as plot lines... #this #is #not #pretty How does it work? The correlation matrix will not be based on the current asset of the chart BUT will be based on the current timeframe (confusing? if yes, read it again until you'll get it) - Numbers of bars back: numbers of bars used for the correlation calculation - High correlation level: Correlation upper threshold. If above, then the correlation will be green - Low correlation level: Correlation lower threshold. If below, then the correlation will be red If the correlation is between the high and low levels, then it will be displayed in orange - FOREX/INDEX: You can choose between displaying the correlation matrix between 3 FOREX or 3 INDEX assets Also... So far the scale doesn't respond too well to the matrix so you'll have to adapt the scale manually. I'll publish a V2 if I'll find a way to solve this issue from the code directly #new #challenge A quick final note on why I'm sharing so much? It challenges me to think out of the norm, get out of my bubble and explore areas of Pinescript that I still don't know. This "a script a day" challenge allows me to speed up my learning curve on Pinescript by a billion factor (and I get a few interesting gigs as well) Let's bring this indicator to 100 LIKES guys !!!!! I think it deserves it, don't you think? :) PS Before all copy/pasters will add a version with crypto tomorrow, don't bother, I already did it and will post it in a few minutes for FREE :p ____________________________________________________________ Be sure to hit the thumbs up as it shows me that I'm not doing this for nothing and will motivate to deliver more quality content in the future. - I'm an officially approved PineEditor/LUA/MT4 approved mentor on codementor. You can request a coaching with me if you want and I'll teach you how to build kick-ass indicators and strategies Jump on a 1 to 1 coaching with me - You can also hire for a custom dev of your indicator/strategy/bot/chrome extension/python
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Daveatt tarafından
10
Robust Weighting OscillatorIntroduction A simple oscillator using a modified lowess architecture, good in term of smoothness and reactivity. Lowess Regression Lowess or local regression is a non-parametric (can be used with data not fitting a normal distribution) smoothing method. This method fit a curve to the data using least squares. In order to have a lowess regression one must use tricube kernel for the weightings w , the weightings are determined using a k-nearest-neighbor model. lowess is then calculated like so : Σ (wG(y-a-bx)^2) Our indicator use G , a , b and remove the square as well as replacing x by y Conclusion The oscillator is simple and nothing revolutionary but its still interesting to have new indicators. Lowess would be a great method to be made on pinescript, i have an estimate but its not that good. Some codes use a simple line equation in order to estimate a lowess smoother, i can describe it as ax + b where a is a smooth oscillator, b some kind of filter defined by lp + bp with lp a smooth low pass filter and bp a bandpass filter, x is a variable dependent of the smoothing span.
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alexgrover tarafından
12
APEX - Bollinger Bands WidthBollinger Bands Width (BBW) is an indicator derived from the Bollinger Bands indicator. BBW are measuring the volatility of an asset. The plotted curve will help you identify high and low volatility areas. Some strategies work only if there is some level of Volatility whereas others not enjoy it. When creating your strategy have a look at numbers between 0.02 – 0.10 (2 to 10 percent on 5m timeframe ) is the most common value. You can also easily avoid big pumps/dumps by using BBW in your strategy.
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QuantTherapy tarafından
Hull Moving Average and Daily Candle CrossoverHull Moving Average. Alan Hull developed Hull Moving Average in 2005 in his quest to create a moving average that is "responsive to current price activity while maintaining curve smoothness". Hull claims that his moving average "almost eliminates lag altogether and manages to improve smoothing at the same time" This strategy has Lag built in, the signal will appear 1 or 2 candles lagged, but it wont repaint the signal.... in theory! Does this repaint? you tell me. thankyou Change the settings every time you change timeframe or pair
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SeaSide420 tarafından
7
Moving Average Stop and Reverse (MASAR) [cI8DH]This indicator is an alternative to Parabolic Stop and Reverse indicator. It is primarily used to identify points of potential stops and reverses. Instead of using a static parabolic curve, this indicator adjusts dynamically based on the changes in moving average of the price. Read here to learn more about the usage of this indicator . I tested the strategy version of this indicator on Bitstamp:BTCUSD and compared the results to the Parabolic SAR. I changed the settings on both indicators to achieve the best results on each indicator. This indicator outperformed the Parabolic SAR by a large margin. You need to calibrate the indicator depending on the asset and time frame. It works best in trending markets.
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cI8DH tarafından
13
Inverse Fisher Transform on STOCHASTIC (modified graphics)Modified the graphic representation of the script from John Ehlers - From California, USA, he is a veteran trader. With 35 years trading experience he has seen it all. John has an engineering background that led to his technical approach to trading ignoring fundamental analysis (with one important exception). John strongly believes in cycles. He’d rather exit a trade when the cycle ends or a new one starts. He uses the MESA principle to make predictions about cycles in the market and trades one hundred percent automatically. In the show John reveals: • What is more appropriate than trading individual stocks • The one thing he relies upon in his approach to the market • The detail surrounding his unique trading style • What important thing underpins the market and gives every trader an edge About INVERSE FISHER TRANSFORM: The purpose of technical indicators is to help with your timing decisions to buy or sell. Hopefully, the signals are clear and unequivocal. However, more often than not your decision to pull the trigger is accompanied by crossing your fingers. Even if you have placed only a few trades you know the drill. In this article I will show you a way to make your oscillator-type indicators make clear black-or-white indication of the time to buy or sell. I will do this by using the Inverse Fisher Transform to alter the Probability Distribution Function (PDF) of your indicators. In the past12 I have noted that the PDF of price and indicators do not have a Gaussian, or Normal, probability distribution. A Gaussian PDF is the familiar bell-shaped curve where the long “tails” mean that wide deviations from the mean occur with relatively low probability. The Fisher Transform can be applied to almost any normalized data set to make the resulting PDF nearly Gaussian, with the result that the turning points are sharply peaked and easy to identify. The Fisher Transform is defined by the equation 1) Whereas the Fisher Transform is expansive, the Inverse Fisher Transform is compressive. The Inverse Fisher Transform is found by solving equation 1 for x in terms of y. The Inverse Fisher Transform is: 2) The transfer response of the Inverse Fisher Transform is shown in Figure 1. If the input falls between –0.5 and +0.5, the output is nearly the same as the input. For larger absolute values (say, larger than 2), the output is compressed to be no larger than unity. The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
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hard6out tarafından
1
MA Study: Different Types and More [NeoButane]A study of moving averages that utilizes different tricks I've learned to optimize them. Included is Bollinger Bands, Guppy (GMMA) and Super Guppy. The method used to make it MtF should be more precise and smoother than regular MtF methods that use the security function. For intraday timeframes, each number represents each hour, with 24 equal to 1 day. For daily, 3 is 3 day, for weekly, 4 is the 4 weekly, etc. If you're on a higher timeframe than the one selected, the length will not change. Log-space is used to make calculations work on many cryptos. The rules for color changing Guppy is changed to make it not as choppy on MAs other than EMA. Note that length does not affect SWMA and VWAP and source does not affect VWAP. A short summary of each moving average can be found here: medium.com List of included MAs: ALMA: Arnaud Legoux Double EMA EMA: Exponential Hull MA KAMA: Kaufman Adaptive Linear Regression Curve LSMA: Least Squares SMA: Simple SMMA/RMA: Smoothed/Running SWMA: Symm. Weighted TMA: Triangular Triple EMA VWMA: Volume Weighted WMA: Weighted ZLEMA: Zero Lag VWAP: Vol Weighted Average Welles Wilder MA
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NeoButane tarafından
11
Logistic CorrelationLogistic Correlation is a correlation oscillator using a logistic function. A Logistic Function is a Sigmoid Function who stabilize the variance of data.The logistic function have the same function as the inverse fisher transform but with an advantage over it, the k constant can control the steepness of the curve, lowers k's will preserve the original form of the data while highers one will transform it into a more square shaped form. 10 k 20 k
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alexgrover tarafından
7