AIAE IndicatorAggregate (or Average) Investor Allocation to Equities.
When it comes to predicting long-term equity returns, several well-known indicators come to mind—for example, the CAPE ratio, Tobin’s Q, and Market Cap to GDP, to name a few.
Yet there is another indicator without nearly as high of a profile that has outperformed the aforementioned indicators significantly when it comes to both forecasting and tactical asset allocation.
That indicator, known as the Aggregate (or Average) Investor Allocation to Equities (AIAE), was developed by the pseudonymous financial pundit, Jesse Livermore, and published on his blog in 2013.
In an essay titled, “The Single Greatest Predictor of Future Stock Market Returns,” Livermore makes the case that the primary driver of long-term equity returns is not valuation, but rather the supply of equities relative to the combined supply of bonds and cash.
Accordingly, the AIAE is computed by taking the total market value of equities and dividing by the sum of a) the total market value of equities, b) the total market value of bonds, and c) the total amount of cash available to investors (i.e., that in circulation plus bank deposits):
This ratio gives the market-wide allocation to equities (or, equivalently, the average investor allocation to equities weighted by portfolio size). (Note that every share of stock, every bond, and every unit of cash in existence must be held in some portfolio somewhere at all times.)
Livermore explains that, in practice, the total market value of bonds plus cash can be estimated by the total liabilities held by the five classes of economic borrowers: Households, Non-Financial Corporations, State and Local Governments, the Federal Government, and the Rest of the World.
This follows from the fact that if these entities borrow directly from investors, new bonds are created. Whereas, if they borrow directly from banks, new bank deposits (cash) are created.
As the economy grows, the supply of bonds and cash steadily increases. Historically, the rate of increase of the supply of bonds and cash has been about 7.5% per annum. Consequently, if the market portfolio is to maintain the same allocation to equities, the supply of equities must increase at the exact same rate.
The supply of equities can increase either by new equity issuance or by price increases. Historically, net new equity issuance has been negligible (with issuances being offset by buybacks and acquisitions). Thus, in order for equities not to become an ever-smaller portion of the average investor’s portfolio, the price of stocks must rise over the long-term.
While we often hear that stock prices follow earnings, in the 1980s earnings fell slightly from the beginning of the decade to the end of the decade, yet stocks rose at an annualized rate of 17% during that time. How could this be?
Well, at the beginning of the decade the average investor’s portfolio had a 25% allocation to equities. During the decade, the supply of bonds and cash rose strongly. If the price of equities had not risen, the average investor’s allocation to equities would have fallen to a mere 13% (as the supply of cash and bonds grew). Thus, equities had no choice but to rise despite the fall in earnings.
Cycles
Turtle Soup IndicatorTurtle Soup Indicator plots a shape when we have a 20-period high or 20-period low.
Turtle Soup Setup
The Turtle Soup setup was published in the book Street Smarts by Laurence A Connors and Linda Raschke. You can learn about it there. It is a great setup for false breakouts or breakdowns in the group failure tests.
Going long
1) We have a new 20-period low
2) that must have occured at least four trading sessions earlier <- this is very important
Then we place a buy stop above 5-10 ticks or 5 to 10 cents above the previous 20-period low.
If filled immediately place a good til cancelled sell stop one tick or one cent below todays low.
Turtle Soup Plus One
Similar to above but occurs one day later. It should close at/below previous 20-period low.
Buy stop at earlier 20 day low. Cancel fi not filled on day 2.
Take partials within 2-6 bars on this one and trail stop rest of position.
Going short
Reverse
Time frames
Works on all timeframes. Only adjust stoplosses accordingly to chosen timeframe.
Settings
You can change the color, shape and placement of the indicator shape. I actually prefer a grey color for both highs and lows as the color actually doesn't add much information. The placement says it all but it is up to you to change this as you like.
90cycle @joshuuu90 minute cycle is a concept about certain time windows of the day.
This indicator has two different options. One uses the 90 minute cycle times mentioned by traderdaye, the other uses the cls operational times split up into 90 minutes session.
e.g. we can often see a fake move happening in the 90 minute window between 2.30am and 4am ny time.
The indicator draws vertical lines at the start/end of each session and the user is able to only display certain sessions (asia, london, new york am and pm)
For the traderdayes option, the indicator also counts the windows from 1 to 4 and calls them q1,q2,q3,q4 (q-quarter)
⚠️ Open Source ⚠️
Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated.
⚠️ Terms and Conditions ⚠️
This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms.
90 Minute Cycles + MTFCredit goes to LuxAlgo for the inspiration from 'Sessions' which allowed users to analyse specific price movements within a user defined period with tools such as trendline, mean and vwap.
Settings
Sessions
Enable Session: Allows to enable or disable all associated elements with a specific user set session.
Session Time: Opening and closing times of the user set session in the hh:mm format.
Range: Highlights the associated session range on the chart.
Ranges Settings
Range Area colour: Set each range to a specific colour.
Range Label: Shows the session label at the mid-point of the session interval.
Usage
By breaking 24hrs in quarters, starting with an Asian range of 18:00 NY time you can visualise the principles of Accumulation, Manipulation, Distribution and Rebalance. Know as AMD or PO3 (Power of Three), the principle is that the Manipulation phase will break above or below the Accumulation, before moving in an apposing direction and then rebalancing. This only works when there is a higher timeframe PD array or liquidity to support an apposing move.
Further to the daily quarters, each one can then be broken down again into 90min cycles. Again, each represents AMD, allowing the user an opportunity to watch for reversals during the 90min manipulation phase.
Note: Ensure the Asian Cycle always begins at 18:00 NY time.
The example shows that the 90min cycle occurs, followed by an apposing move away in price action
Here is the Daily cycle, highlighting the Manipulation phase.
Enjoy!
7 Closes above/below 5 SMAThis script looks for 7 consecutive closes above/below the 5-period SMA. The indicator is inspired by legendary trader Linda Raschke's work.
Usage
The script can can be used in three main ways. I think you will find more uses.
First are the two models for which the indicator was created, both inspired by Raschke:
1) Persistency of trend / Extended run setup.
Around 10-12 times per year we get a persistency of trend in instruments in general.
After 7 consecutive closes above/below the 5-period as price pulls back we can look to enter in the direction of the main trend as it moves up/down above/below 5 ma again. You should use price action trading to pinpoint the entries. Now try to hold this as long as possible. Way longer than you can percieve or think is possible. Up to 24-28 periods is what we are looking for in these cases.
2) Normal usage.
When the trend is not persistent, it is possible to use this as an oscillating signal, for a shorter term trade, where we can look for a short or long term reversal setup in price action.
3) I also use it at as a learning to see the swing trades clearer. You can also use it as a visual aid for developing new variances of the classic swing trading setup.
Read and listen to Linda Raschkes work to learn more.
TIme frames
The principles works in all time frames but may change depending on calendar differences. We will see more instances/year in shorter time frames.
Why closes above the 5 SMA
As you may or may not know the 5 SMA is a very important indicator. You can think of it like this, If price is above 5, it is innocent until proven guilty but if price is below 5 we use the french law system which means it is guilty until proven innocent. 7 closes above 5 is a very good predictor of possible short term direction changes.
Use together with:
I prefer to use this indicator together with either regular SMA:s, one short and one macro term. For example 10 ma and 100 ma.
Or you can use it with a a Hull 21-period MA together with a 240-period WMA.
Settings:
I added settings so you can change preferences for changing shape, where to display the shape and in what color
Visual aid
I wanted to keep one dot for each consecutive day, this way we will get a grouping of days and dots. The amount in this group can be of use in itself to inform you of the strength of trend. This can inform you if this oscillation predicts a short term eversal or a continuation. You need skills in reading price action to use this to your advantage.
TGIF StatsTGIF - "Thank God it's Friday"
After a heavily bearish week (tuesday, wednesday and thursday) price sometimes looks for some retracement on fridays. Vice versa for bullish weeks.
This script shows how often that specific scenario happens and displays that data in a table.
The user has the option to input a starting year for the statistic and is able to filter between bearish or bullish weeks.
*disclaimer : if paired with a higher timeframe pd array taught by ICT the stats should be better, that's not included in the code though*
⚠️ Open Source ⚠️
Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated.
⚠️ Terms and Conditions ⚠️
This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms.
Fierytrading: Volatility DepthDear Tradingview community,
I'd like to share one of my staple indicators with you. The volatility depth indicator calculates the volatility over a 7-day period and plots it on your chart.
This indicator only works for the DAILY chart on BTC/USD.
Colors
I've color coded the indicator as follows:
- Red: Extreme Volatility
- Orange: High Volatility
- Yellow: Normal Volatility
- Green: Low Volatility
Red: extreme changes in price. Often during local tops and bottoms.
Orange: higher than average moves in price. Often before or after a "red" period. Often seen in the middle of bear or bull markets.
Yellow: normal price action. Often seen during early stage bull-markets and late stage bear-markets.
Green: very low price movement. Often during times of indecision. Once this indicator becomes green, you can expect a big move in either direction. Low volatility is always followed by high volatility.
In a long-term uptrend, a green period often signals a bullish break out. In a long-term downtrend it often signals a bearish break out.
How to use
Save the indicator and apply it to your chart. You can change the length in the settings, but it's optimized for 7 days, so no need to change it.
I've build in alerts for all 4 different volatility periods. In most cases, the low volatility alert is enough.
Good luck!
Market Cycle IndicatorThe Market Cycle Indicator is a tool that integrates the elements of RSI, Stochastic RSI, and Donchian Channels. It is designed to detect market cycles, enabling traders to enter and exit the market at the most opportune times.
This indicator provides a unique perspective on the market, combining multiple strategies into one unified and weighted approach. By factoring in the inputs from each of these popular technical analysis methods, it offers a more holistic view of the market trends and cycles.
Parameter Details:
Donchian Channels (DCO):
- donchianPeriod: Sets the period for the Donchian Channel calculation. Default is set to 14.
- donchianSmoothing: Sets the smoothing factor for the Donchian Channel calculation. Default is set to 3.
- donchianPrice: Selects the price type to be used in the Donchian Channel calculation. Default is set to the closing price.
Relative Strength Index (RSI):
- rsiPeriod: Sets the period for the RSI calculation. Default is set to 14.
- rsiSmoothing: Sets the smoothing factor for the RSI calculation. Default is set to 3.
- rsiPrice: Selects the price type to be used in the RSI calculation. Default is set to the closing price.
Stochastic RSI (StochRSI):
- srsiPeriod: Sets the period for the Stochastic RSI calculation. Default is set to 20.
- srsiSmoothing: Sets the smoothing factor for the Stochastic RSI calculation. Default is set to 3.
- srsiK: Sets the period for the %K line in the Stochastic RSI calculation. Default is set to 5.
- srsiD: Sets the period for the %D line in the Stochastic RSI calculation. Default is set to 5.
- srsiPrice: Selects the price type to be used in the Stochastic RSI calculation. Default is set to the closing price.
Weights:
- rsiWeight: Sets the weight for the RSI in the final aggregate calculation. Default is set to 1.
- srsiWeight: Sets the weight for the Stochastic RSI in the final aggregate calculation. Default is set to 1.
- dcoWeight: Sets the weight for the Donchian Channel in the final aggregate calculation. Default is set to 1.
Limits:
- limitHigh: Sets the upper limit for the indicator. Default is set to 80.
- limitLow: Sets the lower limit for the indicator. Default is set to 20.
By customizing these parameters, users can tweak the indicator to align with their own trading strategies and risk tolerance levels. Whether you're a novice or an experienced trader, the Comprehensive Market Cycle Indicator provides valuable insights into the market's behavior.
Uses library HelperTA
Buying/Selling Pressure Cycle (PreCy)No lag estimation of the buying/selling pressure for each candle.
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WHY PreCY?
How much bearish pressure is there behind a group of bullish candles ?
Is this bearish pressure increasing?
When might it overcome the bullish pressure?
Those were my questions when I started this indicator. It lead me through the rabbit hole, where I discovered some secrets about the market. So I pushed deeper, and developped it a lot more, in order to understand what is really happening "behind the scene".
There are now 3 ways to read this indicator. It might look complicated at first, but the reward is to be able to anticipate and understand a lot more.
You can show/hide all the plots in the settings. So you can choose the way you prefer to use it.
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FIRST WAY TO READ PreCy : The SIGNAL line
Go in the settings of PreCy, in "DISPLAY", uncheck "The pivot lines of the SIGNAL" and "The CYCLE areas". Make sure "The SIGNAL line" is checked.
The SIGNAL shows an estimation of the buying/selling pressure of each candle, going from 100 (100% bullish candle) to -100 (100% bearish candle). A doji would be shown close to zero.
Formula: Estimated % of buying pressure - Estimated % of selling pressure
It is a very choppy line in general, but its colors help make sense of it.
When this choppiness alternates between the extremes, then there is not much pressure on each candle, and it's very unpredictable.
When the pressure increases, the SIGNAL's amplitude changes. It "compresses", meaning there is some interest in the market. It can compress by alternating above and below zero, or it can stay above zero (bullish), or below zero (bearish) for a while.
When the SIGNAL becomes linear (in opposition to choppy), there is a lot of pressure, and it is directional. The participants agree for a move in a chosen direction.
The trajectory of the SIGNAL can help anticipate when a move is going to happen (directional increase of pressure), or stop (returning to zero) and possibly reverse (crossing zero).
Advanced uses:
The SIGNAL can make more sense on a specific timeframe, that would be aligned with the frequency of the orders at that moment. So it is a good idea to switch between timeframes until it gets less choppy, and more directional.
It is interesting to follow any regular progression of the SIGNAL, as it can reveal the intentions of the market makers to go in a certain direction discretely. There can be almost no volume and no move in the price action, yet the SIGNAL gets linear and moves away from one extreme, slowly crosses the zeroline, and pushes to the other extreme at the same time as the amplitude of the price action increases drastically.
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SECOND WAY TO READ PreCy : The PIVOTS of the SIGNAL line
Go in the settings of PreCy, in "DISPLAY", and uncheck "The CYCLE areas". Make sure "The SIGNAL line" and "The pivot lines of the SIGNAL" are checked.
The PIVOTS help make sense of the apparent chaos of the SIGNAL. They can reveal the overall direction of the choppy moves.
Especially when the 2 PIVOTS lines are parallel and oriented.
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THIRD WAY TO READ PreCy : The CYCLE
Go in the settings of PreCy, in "DISPLAY", and uncheck "The SIGNAL line" and "The pivot lines of the SIGNAL". Make sure "The CYCLE areas" is checked.
The CYCLE is a Moving Average of the SIGNAL in relation to each candle's size.
Formula: 6 periods Moving Average of the SIGNAL * (body of the current candle / 200 periods Moving Average of the candle's bodies)
The result goes from 200 to -200.
The CYCLE shows longer term indications of the pressures of the market.
Analysing the trajectory of the CYCLE can help predict the direction of the price.
When the CYCLE goes above or below the gray low intensity zone, it signals some interest in the move.
When the CYCLE stays above 100 or below -100, it is a sign of strength in the move.
When it stayed out of the gray low intensity zone, then returns inside it, it is a strong signal of a probable change of behavior.
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ALERTS
In the settings, you can pick the alerts you're interested in.
To activate them, right click on the chart (or alt+a), choose "Add alert on Buying/Selling Pressure Cycle (PreCy)" then "Any alert()", then "Create".
Feel free to activate them on different timeframes. The alerts show which timeframe they are from (ex: "TF:15" for the 15 minutes TF).
I have added a lot more conditions to my PreCy, taken from FREMA Trend, for ex. You can do the same with your favorite scripts, to make PreCy more accurate for your style.
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Borrowed scripts:
To estimate the buying and selling pressures, PreCy uses the wicks calculations of "Volume net histogram" by RafaelZioni
To filter the alerts, PreCy uses the calculations of "Amplitude" by Koholintian:
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DO NOT BASE YOUR TRADING DECISIONS ON 1 SINGLE INDICATOR'S SIGNALS.
Always confirm your ideas by other means, like price action and indicators of a different nature.
Benner-Fibonacci Reversal Points [CC]This is an original script based on a very old idea called the Benner Theory from the Civil War times. Benner discovered a pattern in pig iron prices (no clue what those are), and this turned out to be a parallel idea to indicators based on Fibonacci numbers. Because a year is 365 days (nearly 377, which is a Fibonacci number), made up of 52 weeks (nearly 55, which is another Fibonacci number), or 12 months (nearly 13, which is another Fibonacci number), Benner theorized that he could find both past and future turning points in the market by using a pattern he found. He discovered that peaks in prices seemed to follow a pattern of 8-9-10, meaning that after a recent peak, it would be 8 bars until the next peak, 9 bars until after that peak for the next, and 10 bars until the following peak. For past peaks, he would just need to reverse this pattern, and so the previous peak would be 10 bars before the most current peak, 9 bars before that peak, and 8 bars before the previous one, and these patterns seemed to repeat. For troughs, he found a pattern of 16,18,20 which follows the same logic, and this idea also seemed to work on long-term peaks and troughs as well.
This is my version of the Benner theory and the major difference between my version and his is that he would manually select a year or date and either work backwards or forwards from that point. I chose to go with an adaptive version that will automatically detect those points and plot those past and future points. I have included several options such as allowing the algorithm to be calculated in reverse which seems to work well for Crypto for some reason. I also have both short and long term options to only show one or both if you choose and of course the option to enable repainting or leave it disabled.
Big thanks to @HeWhoMustNotBeNamed and @RicardoSantos for helping me fix some bugs in my code and for @kerpiciwuasile for suggesting this idea in the first place.
OverNightSession @joshuuuThis indicator highlights the Overnightsession (ONS), taught by TheCurrenyMerchant.
The Overnightsession is from 4-8 am UTC-5. This session can be used to form trades, e.g. after one side has been taken out.
It has the options to display Projection and the equilibrium level. Equilibrium level (50%) can be used to identify if price is currently in premium/discount of the range and the projections (standard deviations of the range) can be used to identify possible targets.
A classic setup he teaches is:
Price trades agressively out of the range taking liquidity. As soon as we trade above the high of the candle that took liquidity, that candle can be considered an orderblock, where the 50% level can be used for long setups.
⚠️ Open Source ⚠️
Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated.
⚠️ Terms and Conditions ⚠️
This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms.
BTC bottom top MACRO indicator based on: Cost per transaction(w)Predicting tops and bottoms in any market is a challenging task, and the Bitcoin market is no exception. Many traders and analysts use a combination of various indicators and models to help them make educated guesses about where the market might be heading. One such metric that can provide valuable insights is the Bitcoin cost per transaction indicator.
Here's how it could potentially be superior to just using price action for predicting macro tops and bottoms:
Transaction Cost as an Indicator of Network Activity: The cost per transaction on the Bitcoin network can give an indication of how much activity is taking place. When transaction costs are high, it may signal increased network usage, which often coincides with periods of market enthusiasm or FOMO (Fear of Missing Out) that can precede market tops. Conversely, lower transaction costs might indicate reduced network activity, potentially signaling a lack of investor interest that might precede market bottoms.
Reflects Real-World Use and Demand: Unlike price action, which can be influenced by speculative trading and may not always reflect the underlying fundamentals, the cost per transaction is directly tied to the use of the Bitcoin network. It offers a more fundamental approach to understanding market dynamics.
Complements Price Action Analysis: While price action can give signals about potential tops and bottoms based on historical price patterns and technical analysis, the cost per transaction can add an additional layer of information by reflecting network activity. In this way, the two can be used together to give a more complete picture of the market.
May Precede Price Changes: Changes in transaction costs could potentially precede price changes, giving advanced warning of tops and bottoms. For instance, a sudden increase in transaction costs might indicate a surge in network activity and investor interest, potentially signaling a market top. On the other hand, a decrease in transaction costs might suggest declining network activity and investor interest, potentially signaling a market bottom.
However, it's important to note that while the cost per transaction can provide valuable insights, it's not a foolproof method for predicting market tops and bottoms. Like all indicators, it should be used in conjunction with other tools and analysis methods, and traders should also consider the broader market context. As always, past performance is not indicative of future results, and all trading and investment strategies carry the risk of loss.
ICT Time Windows by Scuba SteveJust an easy to use time based indicator that allows you to track ICT London Open Killzone, New York Open killzone, AM session Silver Bullet time window & PM Session Silver Bullet Time Window, and last but not least the Last Hour of trading which often has nice moves.
Digital Root 9 Time HighlightsTitle: Digital Root 9 Indicator
Description: The Digital Root 9 Indicator is a custom TradingView tool that identifies all times in which the digital root of the current time is 9. The digital root is calculated by summing the digits of the current time and then continuing to sum the resulting digits until a single digit is obtained. For instance, the time 3:33 has a digital root of 9 because 3+3+3=9.
What sets the Digital Root 9 Indicator apart from other TradingView indicators is its focus on identifying times with a numerological significance. It is particularly useful for traders who incorporate numerology into their trading strategies and are looking for a tool that highlights these significant times.
To use the Digital Root 9 Indicator, simply add it to your TradingView chart. The indicator will highlight all times when the digital root of the current time is 9, allowing you to see at a glance which times have numerological significance. You can customize the indicator's color scheme and other settings to suit your preferences.
The Digital Root 9 Indicator is intended to help traders identify times when the potential for luck and prosperity is heightened according to numerology. However, it should not be used as the sole basis for making trading decisions. It is important to conduct thorough analysis and risk management before making any trades.
The Digital Root 9 Indicator is suitable for use in any market condition and time frame.
Seasonality [TFO]This Seasonality indicator is meant to provide insight into an asset's average performance over specified periods of time (Daily, Monthly, and Quarterly). It is based on a 252 trading day calendar, not a 365 day calendar. Therefore, some estimations are used in order to aggregate the Daily data into higher timeframes, as we assume every Month to be 21 trading days, and every Quarter to be 63 trading days. Instead of collecting data on the 1st day of a given month, we are actually treating it as the "nth" trading day of the year. Some years exceed 252 trading days, some fall short; however 252 is the average that we are working with for US stocks and indices. Results may vary for non-US markets.
Main features:
- Statistics Table
- Performance Analysis
- Seasonal Pivots
The Statistics Table provides a summarized view of the current seasonality: whether the average Day/Month/Quarter tends to be bullish or bearish, what the average percent change is, and what the current (actual) change is relative to the historical value. It is shown in the top right of this chart.
The Performance Analysis shows a histogram of the average percentage performance for the selected timeframe. Here we have options for Daily, Monthly, and Quarterly. The previous chart showed the Monthly timeframe, here we have the Daily and Quarterly.
Lastly, Seasonal Pivots show where highs and lows tend to be created throughout the year, based on an aggregation of the Daily performance data collected over the available years. If we anchor our data to the beginning of the current year, and then manually offset it by ~252 (depending on the year), we can line this data up with the previous years' data and observe how well these Seasonal Pivots lined up with major Daily highs and lows.
Styling options are available for every major component of this indicator. Please consider sharing if you find it useful!
Weekly and daily separatorsThis script plots vertical line between each trading week (thick, solid) and smaller lines (dotted) between each trading day. This helps kepping a better overview on the aspect of time on the higher timeframes below 1D. The distance of the lines to the top and bottom of the chart is controlled by your chart settings menu under Appearance -> Margins.
ICT MakrosThis script highlights the ICT trading makros and silverbullet timewindows with different background colors on your chart. The drawings are only visible on the timeframe 1min - 5min because they become useless above and i didnt code the logic for below 1min
SuperTrend Long Strategy +TrendFilterThis strategy aims to identify long (buy) opportunities in the market using the SuperTrend indicator. It utilizes the Average True Range (ATR) and a multiplier to determine the dynamic support levels for entering long positions. This presentation will provide an overview of the strategy's components, explain its usage, and highlight that it focuses on long trades.
Components of the Strategy:
1. ATR Period: This input determines the period used for calculating the Average True Range (ATR). A higher value may result in smoother trend lines but may lag behind recent price changes.
2. Source (src): This input determines the price source used for calculations, with "hl2" (the average of high and low prices) set as the default.
3. ATR Multiplier: This input specifies the multiplier applied to the ATR value to determine the distance of the support levels from the source.
4. Change ATR Calculation Method: This input allows toggling between two methods of ATR calculation: the default method using atr() or a simple moving average (SMA) of ATR values (sma(tr, Periods)).
5. Show Buy/Sell Signals: This input enables or disables the display of buy and sell signals on the chart.
6. Highlighter On/Off: This input controls whether highlighting of up and down trends is displayed on the chart.
7. Bar Coloring On/Off: This input determines whether the bars on the chart are colored based on the trend direction.
8. The "SuperTrend Long STRATEGY" has been enhanced by incorporating a trend filter. A moving average is used as the filter to confirm the prevailing trend before executing trades. This addition effectively reduces false signals and improves the strategy's reliability, all while maintaining its original name.
Strategy Logic:
1. The strategy calculates the upper (up) and lower (dn) trend lines based on the ATR value and the chosen multiplier.
2. The trend variable keeps track of the current trend, with 1 indicating an uptrend and -1 indicating a downtrend.
3. Buy and sell signals are generated based on the change in trend direction.
4. The strategy includes an optional highlighting feature that colors the chart background based on the current trend.
5. Additionally, the bar coloring feature colors the bars based on the direction of the last trend change.
Usage:
1. ATR Period and ATR Multiplier can be adjusted based on the desired sensitivity and risk tolerance.
2. Buy and sell signals can be displayed using the Show Buy/Sell Signals input, providing clear indications of entry and exit points.
3. The Highlighter On/Off input allows users to visually identify the prevailing trend by coloring the chart background.
4. The Bar Coloring On/Off input offers a quick visual reference for the most recent trend change.
Long Strategy:
The SuperTrend Long Strategy is specifically designed to identify long (buy) opportunities. It generates buy signals when the current trend changes from a downtrend to an uptrend, indicating a potential entry point for long positions. The strategy aims to capture upward price movements and maximize profits during bullish market conditions.
The SuperTrend Long Strategy provides traders with a systematic approach to identifying long trade opportunities. By leveraging the SuperTrend indicator and dynamic support levels, this strategy aims to generate buy signals in uptrending markets. Traders can customize the inputs and utilize the visual features to adapt the strategy to their specific trading preferences.
The modification adds a trend filter to the "SuperTrend Long STRATEGY" to improve its effectiveness. The trend filter uses a moving average to confirm the prevailing trend before taking trades. This addition helps filter out false signals and enhances the strategy's reliability without changing its name.
Recessions & crises shading (custom dates & stats)Shades your chart background to flag events such as crises or recessions, in similar fashion to what you see on FRED charts. The advantage of this indicator over others is that you can quickly input custom event dates as text in the menu to analyse their impact for your specific symbol. The script automatically labels, calculates and displays the peak to through percentage corrections on your current chart.
By default the indicator is configured to show the last 6 US recessions. If you have custom events which will benefit others, just paste the input string in the comments below so one can simply copy/paste in their indicator.
Example event input (No spaces allowed except for the label name. Enter dates as YYYY-MM-DD.)
2020-02-01,2020-03-31,COVID-19
2007-12-01,2009-05-31,Subprime mortgages
2001-03-01,2001-10-30,Dot-com bubble
1990-07-01,1991-03-01,Oil shock
1981-07-01,1982-11-01,US unemployment
1980-01-01,1980-07-01,Volker
1973-11-01,1975-03-01,OPEC
Potential Gain/Loss IndicatorThis indicator calculates the gains and losses in percentage based on the highest high (ATH) and lowest low (ATL) of a given period. It takes the period as an input parameter and calculates the ATH and ATL within that period.
The indicator then calculates the potential gains in percentage if the price goes back to the ATH, as well as the potential losses in percentage if the price goes back to the ATL.
A filled area chart is plotted to show the difference between gains and losses (gains - losses) using a stepline, with green color when positive and red color when negative. The coefficient parameter allows for adjusting the scale of the gains and losses.
# Parameters
1. `period` (integer): The period used for calculating the highest high (ATH) and lowest low (ATL) within the given range. The default value is 50, and the user can select any value greater than or equal to 1.
2. `coef` (float): A coefficient to adjust the scale of the gains and losses. The default value is 0.5, and the user can select any value greater than or equal to 0.1.
Divergences in 52 Week Moving Averages, Adjusted and SmoothedThis script description is intended to be holistic and comprehensive for the understanding of the interested parties who view the script.
Following the PineCoders suggestions, I have provided detailed breakdowns both within the code and in the description immediately below:
► Description
This description is intended to be detailed and meaningful, conveying the understanding of the script’s intention to the user:
The theory: Divergences and extreme readings in 52-Week highs on major indexes can provide a view into a potential pending move in the opposite direction of how the market has been trending. By comparing the 52-Week Hi/Lo indices and applying an Exponential Moving Average (EMA), we can assess how extreme a move is from the average. If the move provides an extreme reading, it would potentially be beneficial to “fade” the move (take a position in the opposing direction).
The intention: The intentionality of this script is to provide a visualization of when the highly-probable opportunity to fade over a multi-day or multi-week period arises. In addition to this, based on backtesting prior moves and reading the various levels of significant reversals, three tiers: “Standard”, “Sensitive”, and “Highly Sensitive” have been applied, the user can choose which sensitivity level they would like to see, there are far less false positives on the Standard and Sensitive settings, while Highly Sensitive often signals multiple times with the move coming a few days later.
The application: The settings allow the user to customize their sensitivity to the fade signals, with the ability to customize the visual that shows up as well. For higher-highs that are fade-worthy, the signal will appear on the top of the candle, for lower-lows that are fade-worthy, the signal will appear on the bottom of the candle. The users risk criteria should be the primary driver of the entry/exit, although when backtesting it appears that the significant move is typically completed within a 2-4 week period at max and 3-5 day period at minimum.
A personal note: I am a futures trader intraday but would very strongly caution users when using this strategy with futures (unless their risk tolerance is higher than most). The most beneficial strategy when fading moves would be to enter in tranches, starting at the first signal and adding on any pullback (as long as the pullback is not below the initial entry point). 1-6 Week Date-To-Expiry options would be the primary method for applying this strategy. I would also like to add that SPY/SPX options (SPDR S&P 500 ETF Trust / CBOE S&P 500 Index) are the most liquid options that could be applied in this strategy.
► Description (additional)
With the understanding that few users can read pinescript (Pine), the description above contains all of the necessary information that is necessary for a user to understand the intention for script utilization. For those who do understand Pine, the code is commented in each section in order to provide an understanding of the underlying functions, calculations, and thought process that went on during the writing of the script.
► Description (additional)
This script’s description contains no delegations, all aspects of the script as well as the initial idea behind it are contained in the description above, which is self-contained in it’s entirety with a clear and defined purpose that is written with the intent to holistically capture the intent of the potential use for this indicator.
► General House Rule #2
This script and the description (as well as my profile) contain no links or associations to promotion of any kind, I am not a business, I am not an individual that will in any way make money from this script or the promotion of another person, idea, company, entity, or legal persons (foreign or domestic).
► Originality and usefulness
This is an original and custom script (and idea) that is not a rehashing or a copy of any code from any other programmers in the tradingview community.
Endpointed SSA of Price [Loxx]The Endpointed SSA of Price: A Comprehensive Tool for Market Analysis and Decision-Making
The financial markets present sophisticated challenges for traders and investors as they navigate the complexities of market behavior. To effectively interpret and capitalize on these complexities, it is crucial to employ powerful analytical tools that can reveal hidden patterns and trends. One such tool is the Endpointed SSA of Price, which combines the strengths of Caterpillar Singular Spectrum Analysis, a sophisticated time series decomposition method, with insights from the fields of economics, artificial intelligence, and machine learning.
The Endpointed SSA of Price has its roots in the interdisciplinary fusion of mathematical techniques, economic understanding, and advancements in artificial intelligence. This unique combination allows for a versatile and reliable tool that can aid traders and investors in making informed decisions based on comprehensive market analysis.
The Endpointed SSA of Price is not only valuable for experienced traders but also serves as a useful resource for those new to the financial markets. By providing a deeper understanding of market forces, this innovative indicator equips users with the knowledge and confidence to better assess risks and opportunities in their financial pursuits.
█ Exploring Caterpillar SSA: Applications in AI, Machine Learning, and Finance
Caterpillar SSA (Singular Spectrum Analysis) is a non-parametric method for time series analysis and signal processing. It is based on a combination of principles from classical time series analysis, multivariate statistics, and the theory of random processes. The method was initially developed in the early 1990s by a group of Russian mathematicians, including Golyandina, Nekrutkin, and Zhigljavsky.
Background Information:
SSA is an advanced technique for decomposing time series data into a sum of interpretable components, such as trend, seasonality, and noise. This decomposition allows for a better understanding of the underlying structure of the data and facilitates forecasting, smoothing, and anomaly detection. Caterpillar SSA is a particular implementation of SSA that has proven to be computationally efficient and effective for handling large datasets.
Uses in AI and Machine Learning:
In recent years, Caterpillar SSA has found applications in various fields of artificial intelligence (AI) and machine learning. Some of these applications include:
1. Feature extraction: Caterpillar SSA can be used to extract meaningful features from time series data, which can then serve as inputs for machine learning models. These features can help improve the performance of various models, such as regression, classification, and clustering algorithms.
2. Dimensionality reduction: Caterpillar SSA can be employed as a dimensionality reduction technique, similar to Principal Component Analysis (PCA). It helps identify the most significant components of a high-dimensional dataset, reducing the computational complexity and mitigating the "curse of dimensionality" in machine learning tasks.
3. Anomaly detection: The decomposition of a time series into interpretable components through Caterpillar SSA can help in identifying unusual patterns or outliers in the data. Machine learning models trained on these decomposed components can detect anomalies more effectively, as the noise component is separated from the signal.
4. Forecasting: Caterpillar SSA has been used in combination with machine learning techniques, such as neural networks, to improve forecasting accuracy. By decomposing a time series into its underlying components, machine learning models can better capture the trends and seasonality in the data, resulting in more accurate predictions.
Application in Financial Markets and Economics:
Caterpillar SSA has been employed in various domains within financial markets and economics. Some notable applications include:
1. Stock price analysis: Caterpillar SSA can be used to analyze and forecast stock prices by decomposing them into trend, seasonal, and noise components. This decomposition can help traders and investors better understand market dynamics, detect potential turning points, and make more informed decisions.
2. Economic indicators: Caterpillar SSA has been used to analyze and forecast economic indicators, such as GDP, inflation, and unemployment rates. By decomposing these time series, researchers can better understand the underlying factors driving economic fluctuations and develop more accurate forecasting models.
3. Portfolio optimization: By applying Caterpillar SSA to financial time series data, portfolio managers can better understand the relationships between different assets and make more informed decisions regarding asset allocation and risk management.
Application in the Indicator:
In the given indicator, Caterpillar SSA is applied to a financial time series (price data) to smooth the series and detect significant trends or turning points. The method is used to decompose the price data into a set number of components, which are then combined to generate a smoothed signal. This signal can help traders and investors identify potential entry and exit points for their trades.
The indicator applies the Caterpillar SSA method by first constructing the trajectory matrix using the price data, then computing the singular value decomposition (SVD) of the matrix, and finally reconstructing the time series using a selected number of components. The reconstructed series serves as a smoothed version of the original price data, highlighting significant trends and turning points. The indicator can be customized by adjusting the lag, number of computations, and number of components used in the reconstruction process. By fine-tuning these parameters, traders and investors can optimize the indicator to better match their specific trading style and risk tolerance.
Caterpillar SSA is versatile and can be applied to various types of financial instruments, such as stocks, bonds, commodities, and currencies. It can also be combined with other technical analysis tools or indicators to create a comprehensive trading system. For example, a trader might use Caterpillar SSA to identify the primary trend in a market and then employ additional indicators, such as moving averages or RSI, to confirm the trend and generate trading signals.
In summary, Caterpillar SSA is a powerful time series analysis technique that has found applications in AI and machine learning, as well as financial markets and economics. By decomposing a time series into interpretable components, Caterpillar SSA enables better understanding of the underlying structure of the data, facilitating forecasting, smoothing, and anomaly detection. In the context of financial trading, the technique is used to analyze price data, detect significant trends or turning points, and inform trading decisions.
█ Input Parameters
This indicator takes several inputs that affect its signal output. These inputs can be classified into three categories: Basic Settings, UI Options, and Computation Parameters.
Source: This input represents the source of price data, which is typically the closing price of an asset. The user can select other price data, such as opening price, high price, or low price. The selected price data is then utilized in the Caterpillar SSA calculation process.
Lag: The lag input determines the window size used for the time series decomposition. A higher lag value implies that the SSA algorithm will consider a longer range of historical data when extracting the underlying trend and components. This parameter is crucial, as it directly impacts the resulting smoothed series and the quality of extracted components.
Number of Computations: This input, denoted as 'ncomp,' specifies the number of eigencomponents to be considered in the reconstruction of the time series. A smaller value results in a smoother output signal, while a higher value retains more details in the series, potentially capturing short-term fluctuations.
SSA Period Normalization: This input is used to normalize the SSA period, which adjusts the significance of each eigencomponent to the overall signal. It helps in making the algorithm adaptive to different timeframes and market conditions.
Number of Bars: This input specifies the number of bars to be processed by the algorithm. It controls the range of data used for calculations and directly affects the computation time and the output signal.
Number of Bars to Render: This input sets the number of bars to be plotted on the chart. A higher value slows down the computation but provides a more comprehensive view of the indicator's performance over a longer period. This value controls how far back the indicator is rendered.
Color bars: This boolean input determines whether the bars should be colored according to the signal's direction. If set to true, the bars are colored using the defined colors, which visually indicate the trend direction.
Show signals: This boolean input controls the display of buy and sell signals on the chart. If set to true, the indicator plots shapes (triangles) to represent long and short trade signals.
Static Computation Parameters:
The indicator also includes several internal parameters that affect the Caterpillar SSA algorithm, such as Maxncomp, MaxLag, and MaxArrayLength. These parameters set the maximum allowed values for the number of computations, the lag, and the array length, ensuring that the calculations remain within reasonable limits and do not consume excessive computational resources.
█ A Note on Endpionted, Non-repainting Indicators
An endpointed indicator is one that does not recalculate or repaint its past values based on new incoming data. In other words, the indicator's previous signals remain the same even as new price data is added. This is an important feature because it ensures that the signals generated by the indicator are reliable and accurate, even after the fact.
When an indicator is non-repainting or endpointed, it means that the trader can have confidence in the signals being generated, knowing that they will not change as new data comes in. This allows traders to make informed decisions based on historical signals, without the fear of the signals being invalidated in the future.
In the case of the Endpointed SSA of Price, this non-repainting property is particularly valuable because it allows traders to identify trend changes and reversals with a high degree of accuracy, which can be used to inform trading decisions. This can be especially important in volatile markets where quick decisions need to be made.
Quinn-Fernandes Fourier Transform of Filtered Price [Loxx]Down the Rabbit Hole We Go: A Deep Dive into the Mysteries of Quinn-Fernandes Fast Fourier Transform and Hodrick-Prescott Filtering
In the ever-evolving landscape of financial markets, the ability to accurately identify and exploit underlying market patterns is of paramount importance. As market participants continuously search for innovative tools to gain an edge in their trading and investment strategies, advanced mathematical techniques, such as the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter, have emerged as powerful analytical tools. This comprehensive analysis aims to delve into the rich history and theoretical foundations of these techniques, exploring their applications in financial time series analysis, particularly in the context of a sophisticated trading indicator. Furthermore, we will critically assess the limitations and challenges associated with these transformative tools, while offering practical insights and recommendations for overcoming these hurdles to maximize their potential in the financial domain.
Our investigation will begin with a comprehensive examination of the origins and development of both the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter. We will trace their roots from classical Fourier analysis and time series smoothing to their modern-day adaptive iterations. We will elucidate the key concepts and mathematical underpinnings of these techniques and demonstrate how they are synergistically used in the context of the trading indicator under study.
As we progress, we will carefully consider the potential drawbacks and challenges associated with using the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter as integral components of a trading indicator. By providing a critical evaluation of their computational complexity, sensitivity to input parameters, assumptions about data stationarity, performance in noisy environments, and their nature as lagging indicators, we aim to offer a balanced and comprehensive understanding of these powerful analytical tools.
In conclusion, this in-depth analysis of the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter aims to provide a solid foundation for financial market participants seeking to harness the potential of these advanced techniques in their trading and investment strategies. By shedding light on their history, applications, and limitations, we hope to equip traders and investors with the knowledge and insights necessary to make informed decisions and, ultimately, achieve greater success in the highly competitive world of finance.
█ Fourier Transform and Hodrick-Prescott Filter in Financial Time Series Analysis
Financial time series analysis plays a crucial role in making informed decisions about investments and trading strategies. Among the various methods used in this domain, the Fourier Transform and the Hodrick-Prescott (HP) Filter have emerged as powerful techniques for processing and analyzing financial data. This section aims to provide a comprehensive understanding of these two methodologies, their significance in financial time series analysis, and their combined application to enhance trading strategies.
█ The Quinn-Fernandes Fourier Transform: History, Applications, and Use in Financial Time Series Analysis
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique developed by John J. Quinn and Mauricio A. Fernandes in the early 1990s. It builds upon the classical Fourier Transform by introducing an adaptive approach that improves the identification of dominant frequencies in noisy signals. This section will explore the history of the Quinn-Fernandes Fourier Transform, its applications in various domains, and its specific use in financial time series analysis.
History of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform was introduced in a 1993 paper titled "The Application of Adaptive Estimation to the Interpolation of Missing Values in Noisy Signals." In this paper, Quinn and Fernandes developed an adaptive spectral estimation algorithm to address the limitations of the classical Fourier Transform when analyzing noisy signals.
The classical Fourier Transform is a powerful mathematical tool that decomposes a function or a time series into a sum of sinusoids, making it easier to identify underlying patterns and trends. However, its performance can be negatively impacted by noise and missing data points, leading to inaccurate frequency identification.
Quinn and Fernandes sought to address these issues by developing an adaptive algorithm that could more accurately identify the dominant frequencies in a noisy signal, even when data points were missing. This adaptive algorithm, now known as the Quinn-Fernandes Fourier Transform, employs an iterative approach to refine the frequency estimates, ultimately resulting in improved spectral estimation.
Applications of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform has found applications in various fields, including signal processing, telecommunications, geophysics, and biomedical engineering. Its ability to accurately identify dominant frequencies in noisy signals makes it a valuable tool for analyzing and interpreting data in these domains.
For example, in telecommunications, the Quinn-Fernandes Fourier Transform can be used to analyze the performance of communication systems and identify interference patterns. In geophysics, it can help detect and analyze seismic signals and vibrations, leading to improved understanding of geological processes. In biomedical engineering, the technique can be employed to analyze physiological signals, such as electrocardiograms, leading to more accurate diagnoses and better patient care.
Use of the Quinn-Fernandes Fourier Transform in Financial Time Series Analysis
In financial time series analysis, the Quinn-Fernandes Fourier Transform can be a powerful tool for isolating the dominant cycles and frequencies in asset price data. By more accurately identifying these critical cycles, traders can better understand the underlying dynamics of financial markets and develop more effective trading strategies.
The Quinn-Fernandes Fourier Transform is used in conjunction with the Hodrick-Prescott Filter, a technique that separates the underlying trend from the cyclical component in a time series. By first applying the Hodrick-Prescott Filter to the financial data, short-term fluctuations and noise are removed, resulting in a smoothed representation of the underlying trend. This smoothed data is then subjected to the Quinn-Fernandes Fourier Transform, allowing for more accurate identification of the dominant cycles and frequencies in the asset price data.
By employing the Quinn-Fernandes Fourier Transform in this manner, traders can gain a deeper understanding of the underlying dynamics of financial time series and develop more effective trading strategies. The enhanced knowledge of market cycles and frequencies can lead to improved risk management and ultimately, better investment performance.
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique that has proven valuable in various domains, including financial time series analysis. Its adaptive approach to frequency identification addresses the limitations of the classical Fourier Transform when analyzing noisy signals, leading to more accurate and reliable analysis. By employing the Quinn-Fernandes Fourier Transform in financial time series analysis, traders can gain a deeper understanding of the underlying financial instrument.
Drawbacks to the Quinn-Fernandes algorithm
While the Quinn-Fernandes Fourier Transform is an effective tool for identifying dominant cycles and frequencies in financial time series, it is not without its drawbacks. Some of the limitations and challenges associated with this indicator include:
1. Computational complexity: The adaptive nature of the Quinn-Fernandes Fourier Transform requires iterative calculations, which can lead to increased computational complexity. This can be particularly challenging when analyzing large datasets or when the indicator is used in real-time trading environments.
2. Sensitivity to input parameters: The performance of the Quinn-Fernandes Fourier Transform is dependent on the choice of input parameters, such as the number of harmonic periods, frequency tolerance, and Hodrick-Prescott filter settings. Choosing inappropriate parameter values can lead to inaccurate frequency identification or reduced performance. Finding the optimal parameter settings can be challenging, and may require trial and error or a more sophisticated optimization process.
3. Assumption of stationary data: The Quinn-Fernandes Fourier Transform assumes that the underlying data is stationary, meaning that its statistical properties do not change over time. However, financial time series data is often non-stationary, with changing trends and volatility. This can limit the effectiveness of the indicator and may require additional preprocessing steps, such as detrending or differencing, to ensure the data meets the assumptions of the algorithm.
4. Limitations in noisy environments: Although the Quinn-Fernandes Fourier Transform is designed to handle noisy signals, its performance may still be negatively impacted by significant noise levels. In such cases, the identification of dominant frequencies may become less reliable, leading to suboptimal trading signals or strategies.
5. Lagging indicator: As with many technical analysis tools, the Quinn-Fernandes Fourier Transform is a lagging indicator, meaning that it is based on past data. While it can provide valuable insights into historical market dynamics, its ability to predict future price movements may be limited. This can result in false signals or late entries and exits, potentially reducing the effectiveness of trading strategies based on this indicator.
Despite these drawbacks, the Quinn-Fernandes Fourier Transform remains a valuable tool for financial time series analysis when used appropriately. By being aware of its limitations and adjusting input parameters or preprocessing steps as needed, traders can still benefit from its ability to identify dominant cycles and frequencies in financial data, and use this information to inform their trading strategies.
█ Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
Another significant advantage of the HP Filter is its ability to adapt to changes in the underlying trend. This feature makes it particularly well-suited for analyzing financial time series, which often exhibit non-stationary behavior. By employing the HP Filter to smooth financial data, traders can more accurately identify and analyze the long-term trends that drive asset prices, ultimately leading to better-informed investment decisions.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
█ Combined Application of Fourier Transform and Hodrick-Prescott Filter
The integration of the Fourier Transform and the Hodrick-Prescott Filter in financial time series analysis can offer several benefits. By first applying the HP Filter to the financial data, traders can remove short-term fluctuations and noise, effectively isolating the underlying trend. This smoothed data can then be subjected to the Fourier Transform, allowing for the identification of dominant cycles and frequencies with greater precision.
By combining these two powerful techniques, traders can gain a more comprehensive understanding of the underlying dynamics of financial time series. This enhanced knowledge can lead to the development of more effective trading strategies, better risk management, and ultimately, improved investment performance.
The Fourier Transform and the Hodrick-Prescott Filter are powerful tools for financial time series analysis. Each technique offers unique benefits, with the Fourier Transform being adept at identifying dominant cycles and frequencies, and the HP Filter excelling at isolating long-term trends from short-term noise. By combining these methodologies, traders can develop a deeper understanding of the underlying dynamics of financial time series, leading to more informed investment decisions and improved trading strategies. As the financial markets continue to evolve, the combined application of these techniques will undoubtedly remain an essential aspect of modern financial analysis.
█ Features
Endpointed and Non-repainting
This is an endpointed and non-repainting indicator. These are crucial factors that contribute to its usefulness and reliability in trading and investment strategies. Let us break down these concepts and discuss why they matter in the context of a financial indicator.
1. Endpoint nature: An endpoint indicator uses the most recent data points to calculate its values, ensuring that the output is timely and reflective of the current market conditions. This is in contrast to non-endpoint indicators, which may use earlier data points in their calculations, potentially leading to less timely or less relevant results. By utilizing the most recent data available, the endpoint nature of this indicator ensures that it remains up-to-date and relevant, providing traders and investors with valuable and actionable insights into the market dynamics.
2. Non-repainting characteristic: A non-repainting indicator is one that does not change its values or signals after they have been generated. This means that once a signal or a value has been plotted on the chart, it will remain there, and future data will not affect it. This is crucial for traders and investors, as it offers a sense of consistency and certainty when making decisions based on the indicator's output.
Repainting indicators, on the other hand, can change their values or signals as new data comes in, effectively "repainting" the past. This can be problematic for several reasons:
a. Misleading results: Repainting indicators can create the illusion of a highly accurate or successful trading system when backtesting, as the indicator may adapt its past signals to fit the historical price data. This can lead to overly optimistic performance results that may not hold up in real-time trading.
b. Decision-making uncertainty: When an indicator repaints, it becomes challenging for traders and investors to trust its signals, as the signal that prompted a trade may change or disappear after the fact. This can create confusion and indecision, making it difficult to execute a consistent trading strategy.
The endpoint and non-repainting characteristics of this indicator contribute to its overall reliability and effectiveness as a tool for trading and investment decision-making. By providing timely and consistent information, this indicator helps traders and investors make well-informed decisions that are less likely to be influenced by misleading or shifting data.
Inputs
Source: This input determines the source of the price data to be used for the calculations. Users can select from options like closing price, opening price, high, low, etc., based on their preferences. Changing the source of the price data (e.g., from closing price to opening price) will alter the base data used for calculations, which may lead to different patterns and cycles being identified.
Calculation Bars: This input represents the number of past bars used for the calculation. A higher value will use more historical data for the analysis, while a lower value will focus on more recent price data. Increasing the number of past bars used for calculation will incorporate more historical data into the analysis. This may lead to a more comprehensive understanding of long-term trends but could also result in a slower response to recent price changes. Decreasing this value will focus more on recent data, potentially making the indicator more responsive to short-term fluctuations.
Harmonic Period: This input represents the harmonic period, which is the number of harmonics used in the Fourier Transform. A higher value will result in more harmonics being used, potentially capturing more complex cycles in the price data. Increasing the harmonic period will include more harmonics in the Fourier Transform, potentially capturing more complex cycles in the price data. However, this may also introduce more noise and make it harder to identify clear patterns. Decreasing this value will focus on simpler cycles and may make the analysis clearer, but it might miss out on more complex patterns.
Frequency Tolerance: This input represents the frequency tolerance, which determines how close the frequencies of the harmonics must be to be considered part of the same cycle. A higher value will allow for more variation between harmonics, while a lower value will require the frequencies to be more similar. Increasing the frequency tolerance will allow for more variation between harmonics, potentially capturing a broader range of cycles. However, this may also introduce noise and make it more difficult to identify clear patterns. Decreasing this value will require the frequencies to be more similar, potentially making the analysis clearer, but it might miss out on some cycles.
Number of Bars to Render: This input determines the number of bars to render on the chart. A higher value will result in more historical data being displayed, but it may also slow down the computation due to the increased amount of data being processed. Increasing the number of bars to render on the chart will display more historical data, providing a broader context for the analysis. However, this may also slow down the computation due to the increased amount of data being processed. Decreasing this value will speed up the computation, but it will provide less historical context for the analysis.
Smoothing Mode: This input allows the user to choose between two smoothing modes for the source price data: no smoothing or Hodrick-Prescott (HP) smoothing. The choice depends on the user's preference for how the price data should be processed before the Fourier Transform is applied. Choosing between no smoothing and Hodrick-Prescott (HP) smoothing will affect the preprocessing of the price data. Using HP smoothing will remove some of the short-term fluctuations from the data, potentially making the analysis clearer and more focused on longer-term trends. Not using smoothing will retain the original price fluctuations, which may provide more detail but also introduce noise into the analysis.
Hodrick-Prescott Filter Period: This input represents the Hodrick-Prescott filter period, which is used if the user chooses to apply HP smoothing to the price data. A higher value will result in a smoother curve, while a lower value will retain more of the original price fluctuations. Increasing the Hodrick-Prescott filter period will result in a smoother curve for the price data, emphasizing longer-term trends and minimizing short-term fluctuations. Decreasing this value will retain more of the original price fluctuations, potentially providing more detail but also introducing noise into the analysis.
Alets and signals
This indicator featues alerts, signals and bar coloring. You have to option to turn these on/off in the settings menu.
Maximum Bars Restriction
This indicator requires a large amount of processing power to render on the chart. To reduce overhead, the setting "Number of Bars to Render" is set to 500 bars. You can adjust this to you liking.
█ Related Indicators and Libraries
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Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
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Variety RSI of Fast Discrete Cosine Transform
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