Grid Spot Trading Algorithm V2 - The Quant ScienceGrid Spot Trading Algorithm V2 is the last grid trading algorithm made by our developer team.
Grid Spot Trading Algorithm V2 is a fixed 10-level grid trading algorithm. The grid is divided into an accumulation area (red) and a selling area (green).
In the accumulation area, the algorithm will place new buy orders, selling the long positions on the top of the grid.
BUYING AND SELLING LOGIC
The algorithm places up to 5 limit orders on the accumulation section of the grid, each time the price cross through the middle grid. Each single order uses 20% of the equity.
Positions are closed at the top of the grid by default, with the algorithm closing all orders at the first sell level. The exit level can be adjusted using the user interface, from the first level up to the fifth level above.
CONFIGURING THE ALGORITHM
1) Add it to the chart: Add the script to the current chart that you want to analyze.
2) Select the top of the grid: Confirm a price level with the mouse on which to fix the top of the grid.
3) Select the bottom of the grid: Confirm a price level with the mouse on which to fix the bottom of the grid.
4) Wait for the automatic creation of the grid.
USING THE ALGORITHM
Once the grid configuration process is completed, the algorithm will generate automatic backtesting.
You can add a stop loss that destroys the grid by setting the destruction price and activating the feature from the user interface. When the stop loss is activated, you can view it on the chart.
Statistics
[MAD] Position starter & calculatorThe tool you're using is a financial instrument trading planner and analyzer.
Here is how to use it:
Trade Planning: You can plan your trade entries and exits, calculating potential profits, losses, and their ratio (P/L ratio).
You can define up to five target closing prices with varying volumes, which can be individually activated or deactivated (volume set to 0%).
Risk Management: There's a stop-loss function to calculate and limit potential losses.
Additionally, it includes a liquidation pre-calculation for adjustable leverages and position maintenance(subject to exchange variation).
Customization: You can customize the tool's appearance with five adjustable color schemes, light and dark.
-----------------
Initiation: This tool functions as an indicator.
To start, add it as an indicator.
Once added, you can close the indicator window.
Now wait, till you'll see a blue box at the bottom of the input window.
Parameter Input:
Enter your parameters (SL, box left, box right, TP1, TP2, TP3, TP4, TP5) in the direction of the desired trade.
Click from top to bottom for a short trade or bottom to top for a long trade.
Adjustment: If you want to move the box in the future, adjust the times in the indicator settings directly as click input is not yet platform-supported.
This tool functions as a ruler and doesn't offer alerts (for now).
Here is another examples of how to set up a Position-calculation but here for a short:
Have fun trading
Position_controlLibrary "Position_control"
This is a library for defining positions and working with them.
f_calculateLeverage(_Leverage, _maintenance, _value, _direction)
Calculate the leverage used in a trade.
@description This function calculates the leverage used in a trade, based on the value of the trade, the maintenance margin, and the direction of the trade.
Parameters:
_Leverage (float) : The leverage used in the trade, as a floating point number.
_maintenance (float) : The maintenance margin percentage, as a floating point number.
_value (float) : The value of the trade, as a floating point number.
_direction (string) : The direction of the trade, either "long" or "short".
Returns: The leverage used in the trade, as a floating point number.
f_calculate_PL(_Position, _max_TP, _Position_index, _show_profit, _i_decimals_contracts, _i_decimals_prercent)
Calculate the profit or loss for a given trade.
@description This function calculates the profit or loss for a given trade, based on the position type, maximum take profit, position index, and whether to show the profit as a percentage or a value.
Parameters:
_Position (t_Position_type ) : An array of position types for the trade.
_max_TP (int) : The maximum take profit for the trade, as an integer value.
_Position_index (int) : The index of the position in the array, as an integer value.
_show_profit (bool) : A boolean value indicating whether to show the profit as a percentage or a value.
_i_decimals_contracts (int)
_i_decimals_prercent (int)
Returns: The profit or loss for the trade, as a floating point number.
f_drawposition(_Position, _Parameters, _Position_index)
draws a position on the chart
@description via sending in a typo of Position this function is able to drawout Stoploss, Entrybox, Takeprofits and the required labels with information
Parameters:
_Position (t_Position_type ) : array of type t_Position_type containing the position information.
_Parameters (t_drawing_parameters)
_Position_index (int) : the index of the current position.
Returns: None but boxes / lines / labels on the chart itself
t_TP_Variant
Fields:
TP_Type (series__string)
TP_Parameter_1 (series__integer)
TP_Parameter_2 (series__integer)
TP_Parameter_3 (series__float)
TP_Parameter_4 (series__float)
t_TPs
Fields:
TP_Price (series__float)
TP_Lot (series__float)
TP_Variant (|t_TP_Variant|#OBJ)
TP_Active (series__bool)
t_SLs
Fields:
SL_Price (series__float)
SL_Lot (series__float)
SL_Active (series__bool)
t_Position_type
Fields:
Lot (series__float)
Leverage (series__float)
Maintenance (series__float)
Starttime (series__integer)
Entry_Start (series__float)
Stoptime (series__integer)
Entry_Stop (series__float)
Entryprice (series__float)
TPs (array__|t_TPs|#OBJ)
SLs (array__|t_SLs|#OBJ)
t_drawing_parameters
Fields:
ShowPos (series__bool)
ShowLIQ (series__bool)
A_Colors (array__color)
Prolong_lines (series__bool)
Str_fontsize (series__string)
Textshift (series__integer)
Decimals_contracts (series__integer)
Decimals_price (series__integer)
Decimals_percent (series__integer)
bartime (series__integer)
Metrics using Alternative Portfolio TheoryLibrary "APT_Metrics"
Portfolio metrics using alternative portfolio theory
metrics(init, cur, start, end, alpha)
Calculates APT metrics
Parameters:
init (float) : Starting Equity (strategy.initial)
cur (float)
start (int) : Start date (UNIX)
end (int) : End Date (UNIX)
alpha (float) : Confidence interval for DaR/CDaR. Defval = 0.05
Returns: Plots table with APT metrics
The metrics are shown in the bottom pane being applied to a buy-and-hold strategy.
PLEASE NOTE: This is the first draft of the library. Some calculations may be incorrect. If you spot any mistakes then please let me know and I will correct them as soon as possible. I am also open to suggestions on how to improve this.
At the moment this only works on the daily timeframe until I can find a way to universally calculate annualized volatility.
See inside Candles: Directionality %; Constituent Bars & GapsSee inside candles based on user-input LTF setting: get data on 'Directionality' of your candle; Gaps (total and Sum; UP and DOWN); Number of Bull or Bear constituent candles
//Features:
-DIRECTIONALITY: compare length of the 'zig-zag' random walk of lower time frame constituent candles, to the full height of the current candle. Resulting % I refer to as 'directionality'.
-GAPs: what i refer to as 'gaps' are also known as Volume imbalances: the gap between previous candles close and current candle's open (if there is one).
--Gaps total (up vs down gaps). Number of Up gaps printed above bar in green, down gaps printed below bar in red.
--Gaps Sum (total summed UP gap, total summed down gaps. Sum of Up gaps printed above bar in green, Sum of down gaps printed below bar in red.
-Candles Total: Numer of LTF up vs down candles within current timeframe candle. Number of up candles printed above bar in green, Number of down candles printed below bar in red.
//USAGE:
-Primary purpose in this was the Directionality aspect. Wanted to get a measure of how choppy vs how directional the internals of a candle were. Idea being that a candle with high % directionality (approaching 100) would imply trending conditions; while a candle which was large range and full bodies but had a low % directionality would imply the internals were back-and-forth and => rebalanced, potentially indicating price may not need to retrace back into it and rebalance further. All rather experimental, please treat it as such: have a play around with it.
-Number of gaps, Sums of up and down gaps, ratio of up and down constituent candles also intended to serve a similar purpose as the above.
-Set the input lower timeframe; this must obviously be lower then your current timeframe. You will significant differences in results depending on the ratio your timeframes (chart timeframe vs user-input timeframe).
//User Inputs:
-Lower timeframe input (setting child candle size within current chart parent candle).
-Choose function from the four listed above.
-typical formating options: Bull color/bear color txt for gaps functions.
-display % unit or not.
-display vertical or horizontal text.
-Set min / max directionality thresholds; and color code results.
-Toggle on/off 'hide results outside of threshold' to declutter the chart.
-choose label style.
//NOTES:
-Directionality thresholds can be set manually; Max and Min thresholds can be set to filter out 'non-extreme' readings.
-Note that directionality % can sometimes exceed 100%, in cases where price trends very strongly and gaps up continuously such that sum of constituent candles is less than total range of parent candle.
-Personally i like the idea of seeking bold, large-range, full bodied candles, with a lower than typical directionality %; indicating that a price move is both significant and it's already done it's rebalancing; I would see this as potentially favourable for continuation (obviously depending on context).
---- Showcase of the other functions beyond Directionality percentage ----
Candles Total (bull vs Bear). ES1! Hourly; ltf = 5min: Candles total: LTF up candles and LTF down candles making up the current HTF candle (constituent number of UP candles printed above in green, Down candles printed below in red):
Gaps SUM. SPX hourly, ltf = 5min. Sum of 'UP' gaps within candle printed above in green, sum of 'DOWN' gaps printed below in red:
Gaps TOTAL: SPX hourly, ltf = 1min. Simply the total of 'up' gaps vs 'down' gaps withing our candle; based on the user input constituent candles within:
SPX ES SpreadA very simple indicator to display the spread between ES and SPX. The table by default displays in the upper right corner of the chart. If you are on the chart for SPX, it will show the current price of ES, as well as the difference in points between the two. Similarly, if on the chart for ES, it will show the price for SPX as well as the difference in points between the two. The table does not appear at all if the chart symbol is anything other than ES or SPX. The specific symbols used can be defined by the user.
PSESS1 - Learn PineScript InputsThis is a script written exclusively for people who are trying to learn Pine Script.
PSESS stands for "Pine Script Educational Script Series" which is a series of scripts that helps Pine Script programmers in 2 ways:
1. Learn Pine Script at more depth by an interactive environment where they can immediately see the effects of any change in the pre-written code and also comparing different lines code having tiny differences so they can grasp the details.
2. Have this script open while coding in order to copy the line they find useful
Pine Script Library couldn't be used for this purpose since this script has educational aspect and needs to be executable individually.
This is Script 1 of PSESS and focuses on inputs in Pine Script.
The script is densly commented in order to make it understandable. here is the outline of the script:
1. Inputs that can be received through the indicator() function
2. 12 possible types of input
3. Input() function arguments: defval - title - tooltip - inline - group - confirm
4. The different display of tooltip when inputs are inline
5. Multiple price and time inputs (on single request or multiple requests)
6. What happens when title argument is not specified
7. References and key points from them
Expected VolatilityExpected Volatility
Hello and welcome to my first indicator! I'm publishing this indicator as free to use and modify because I think it's a great place to learn and I hope I can teach you something.
There are some terms which you need to understand before I begin explaining this indicator and what it does for you:
Daily Settlement - The price at which a market closes when the trading day closes (RTH or Regular Trading Hours close)
Standard Deviation - A measure in statistics that declares how far away a data point is from the mean when compared with all the data points before it to an extent
Now for the history behind this indicator:
Rule of 16. This goes back to the VIX, or S&P 500 volatility index. The idea behind the volatility index is to determine what magnitude of movement could be expected from the market the following day based on recent movement. The rule of 16 is an easier way to refer to the square root of the number of trading days in a year. There are 252 trading days in a year and the square root of 252 is approximately 15.87. We estimate it to be 16 because it's easier to talk about when it's easier to say and therefore easier to remember.
The relevance of this rule is that when the VIX is at 16, we can expect a market movement of 1% or so unless some special circumstances overrule this estimate. To get the expected market movement, we take 16 and divide by 16 and get 1, or 1%. If the VIX is trading at 24, we get 24/16 or 1.5 which is 1.5% movement. This indicator seeks to simplify the math and lay it out in a visual way to show the highest probability of range the market is expected to trade.
Thanks for taking the time to read my description, I hope you like my indicator.
Special thanks to my trading friends and coaches for helping me complete this indicator.
Statistical Arbitrage Right LegStatistical Arbitrage, this is the right leg strategy signal.
You should find "Statistical Arbitrage Left Leg (Symbol 1)" this strategy for the opposite leg (Left Leg),
In order to full hedge the position exposure risk, and profit on the spread convergence.
Statistical arbitrage is a quantitative trading strategy that seeks to exploit pricing discrepancies in financial markets based on statistical models and analysis. It involves using mathematical models and statistical techniques to identify and take advantage of mispricings between related financial assets.
In statistical arbitrage, traders use sophisticated algorithms to identify pairs or groups of financial assets that are expected to move in tandem based on historical correlations. They then look for deviations from these historical patterns in order to generate profitable trades. For example, a trader might identify a pair of stocks that have historically moved together, but are currently priced such that one is significantly cheaper than the other. The trader would then buy the cheaper stock and simultaneously sell the more expensive one, hoping to profit when the prices converge.
Statistical arbitrage is a popular trading strategy among hedge funds and other institutional investors, who have access to large amounts of data and the computational resources necessary to analyze it. However, it requires significant expertise in statistics, mathematics, and programming, as well as access to advanced analytical tools and data sources.
Statistical Arbitrage Left LegStatistical Arbitrage, this is the left leg strategy signal.
You should find "Statistical Arbitrage Right Leg (Symbol 1)" this strategy for the opposite leg (Right Leg),
In order to full hedge the position exposure risk, and profit on the spread convergence.
Statistical arbitrage is a quantitative trading strategy that seeks to exploit pricing discrepancies in financial markets based on statistical models and analysis. It involves using mathematical models and statistical techniques to identify and take advantage of mispricings between related financial assets.
In statistical arbitrage, traders use sophisticated algorithms to identify pairs or groups of financial assets that are expected to move in tandem based on historical correlations. They then look for deviations from these historical patterns in order to generate profitable trades. For example, a trader might identify a pair of stocks that have historically moved together, but are currently priced such that one is significantly cheaper than the other. The trader would then buy the cheaper stock and simultaneously sell the more expensive one, hoping to profit when the prices converge.
Statistical arbitrage is a popular trading strategy among hedge funds and other institutional investors, who have access to large amounts of data and the computational resources necessary to analyze it. However, it requires significant expertise in statistics, mathematics, and programming, as well as access to advanced analytical tools and data sources.
Manual PnL (Profit and Loss) % Tracker - spot long only
This is a manual profit and loss tracker. It takes the user's manual input of total cost and quantity, and then outputs a table on the bottom right of the chart showing the profit or loss %, average purchase price, gross profit or loss, and market value.
Instructions:
1. Double click the indicator title at the top left of the chart
2. Select the "Inputs" tab and click the empty field next to "Symbol" to enter the traded symbol+exchange. This entry MUST be the same as the chart you are on, for example BTCUSDT/BINANCE (indicator will not display otherwise)
3. Enter the Total Cost and Qty of shares/coins owned
4. Optional - change positive or negative colors
5. Optional - under the "Style" tab, change the color of the average price (AVG) line
Note that for the average price (AVG) line to be shown/hidden you must enable/disable "Indicator and financials labels" in the scales settings.
For crypto or other tickers that have prices in many decimal places I would suggest, for the sake of accuracy, adjusting the decimal places in the code so that for prices under $1 you will display more info.
For example let's say you purchase x number of crypto at a price of 0.031558 you should change the code displaying "0.00" on line 44 to "0.000000"
This will ensure that the output table and plotted line will calculate an average price with the same number of decimals.
Monte Carlo Price ProbabilitiesMonte Carlo simulations have been a popular tool in the world of finance, risk analysis, and decision making for decades. In this post, I will take you through the history of Monte Carlo simulations and explain how I implemented this powerful technique in Pine Script. This implementation can help traders and investors in various time frames to better understand the potential future price movements of financial instruments based on historical data.
History of Monte Carlo Simulations
The Monte Carlo method was named after the famous Monte Carlo Casino in Monaco, as the technique involves using random sampling to approximate solutions to mathematical problems. The method was first introduced by Stanislaw Ulam, a mathematician working on the Manhattan Project in the 1940s. Ulam realized that using random sampling could provide approximate solutions to complex problems that were otherwise difficult or impossible to solve analytically.
Over the years, Monte Carlo simulations have found applications in various fields, including physics, engineering, and finance. In the world of finance, the method has been used to model stock price movements, option pricing, portfolio optimization, and risk management.
Implementation in Pine
In my implementation of Monte Carlo simulations in Pine, I created a script that allows users to input several parameters such as the arbitrary price, number of simulations, number of steps into the future, and the start bar index. The start bar index is a crucial setting for running the script on lower time frames, as it helps to ensure that the script runs smoothly for a given symbol.
The script then calculates the log return of each bar and categorizes them into green (positive) or red (negative) moves. It uses these historical price movements to calculate the probabilities of future price movements for each step in the simulation.
The core of the Monte Carlo simulation lies in the `monte()` function, which generates random numbers to determine if the next price movement will be green or red, and then selects a move size based on its probability. The `sim()` function runs multiple simulations using the `monte()` function and stores the results in an array.
Finally, the script calculates the probability of the arbitrary price being reached in the future based on the results of the simulations. It also plots the probability on the chart, allowing users to visually assess the potential future price movements of the financial instrument.
Using the Monte Carlo Simulation
To use the Monte Carlo simulation in Pine, you need to input the desired parameters such as the arbitrary price, number of simulations, number of steps into the future, and the start bar index. For some symbols, you may need to set the start bar index to around 10k to ensure that the script runs smoothly.
Once you have input the parameters and run the script, you will see the probability of reaching the arbitrary price plotted on the chart. This can provide a valuable insight into the potential future price movements of the financial instrument based on historical data, helping you make more informed trading and investment decisions.
Conclusion
Monte Carlo simulations have a rich history and have proven to be a valuable tool in various fields, including finance. My implementation of Monte Carlo simulations in Pine allows traders and investors to better understand the potential future price movements of financial instruments in various time frames. By evaluating the probabilities of reaching specific price levels, users can make more informed decisions and better manage their risk.
Position TrackerEnter your purchase price & the quantity.
It'll display a line at that value, with a label indicating the current gain/loss
[MiV] Trading SessionHello, everyone!
Today I want to present my new script, which I hope will help not only me!
I'm sure that many people, like me, went through such a stage as "building their strategy". This is when you sit and test on the history how you would enter or exit a trade.
Recently I was doing the same thing and realized that my "tests" involve night time, when in reality I would be asleep and not trading! So I decided to create an indicator that would display my "working hours" so that the backtest I conduct would be as realistic as possible.
Also this indicator is able to display sessions of major exchanges and forex working hours, so it will be useful not only for cryptocurrency lovers.
In addition, we don't always trade every day and, for example, I don't trade on Sunday. That's why we added a feature that "turns off" the day and does not highlight it in color if you're not planning to trade on that day.
And finally, I added a notification of the beginning and end of the trading session. A small thing, but it may also be a useful feature for those who like to sit at the chart!
I will be glad to receive any comments and suggestions!
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Всем привет!
Хочу сегодня представить свой новый скрипт, который, надеюсь, поможет не только мне!
Уверен, что многие, как и я, проходили такой этап как "постройка своей стратегии". Это когда ты сидишь и тестируешь на истории то как бы ты входил или выходил из сделки.
Вот недавно я ровно также занимался этим и осознал, что мои "тесты" затрагивают и ночное время, когда в реальности я бы спал и не торговал! Поэтому я решил создать индикатор, который будет отображать мои "рабочие часы", чтобы бектест, который я провожу, был максимально реалистичным.
Также данный индикатор умеет отображать сессии крупных бирж и время работы форекса, так что полезным он будет не только для любителей криптовалюты.
Кроме того, мы же не всегда торгуем каждый день и например я не торгую в воскресенье. Поэтому добавлен функционал, который "выключает" день и не подсвечивает его цветом, если ты в этот день не планируешь торговать.
Ну и в заключении, добавил уведомление о начале и завершении торговой сессии. Мелочь, а тоже может быть полезной фичей для тех кто любит засесть за графиком!
Буду рад любым замечаниям и предложениям!
Gaussian Fisher Transform Price Reversals - FTRHello Traders !
Looking for better trading results ?
"This indicator shows you how to identify price reversals in a timely manner." John F. Ehlers
Introduction :
The Gaussian Fisher Transform Price Reversals indicator, dubbed FTR for short, is a stat based price reversal detection indicator inspired by and based on the work of the electrical engineer now private trader John F. Ehlers.
The Fisher Transform :
It is a common assumption that prices have a gaussian / normal probability density function(PDF), i.e. a sample of n close prices would be normally distributed if the probability of observing a price value say at any given standard deviation range is equal to that probability in the case of the normal distribution, e.g. 68% off all samples fell within one standard deviation around the mean, which is what we would expect if the data was normal.
However Price Action is not normally distributed and thus can not be conventionally interpreted in this way, Formally the Fisher Transform, transforms the distribution of bounded ranging price action (were price action takes values in a range from -1 to 1) into that of a normal distribution, alternatively it may be said the Fisher Transform changes the PDF of any waveform so that the transformed output has n approximately Gaussian PDF, It does so through the following equations. taken directly from the work of John F. Ehlers - Using The Fisher Transform
By substituting price data in the above formulas, bounded ranging price actions (over a given user defined period lookback - this determines the range price ranges in, see the Intermediate formula above) distribution is transformed to that in the normal case. This means when the input, the Intermediate ,(the Midpoint - see formula above) approaches either limit within the range the outputs are greatly amplified, this amplification accentuates /puts more weight on the larger deviations or limits within the range, conversely when price action is varying round the mean of the range the output is approximately equal to unity (the input is approximately equal to the input, the intermediate)
The inputs (Intermediates) are converted to normal outputs and the nonlinear Transfer of the Fisher Transform with varying senesitivity's (gammas) can be seen in the graph / image above. Although sensitivity adjustments are not currently available in this script (I forgot to add it) the outputs may be greatly amplified as gamma (the coefficient of the Fisher Transformation - see Fish equation) approaches 1. the purple line show this graphically, as a higher gamma leads to a greater amplification than in the standard case (the red line which is the standard fisher transformation, the black plot is the Fish with a gamma of 1, which is unity sensativity)
Reversal plots and Breakouts :
- Support lines are plotted with their corresponding Fish value when there is a crossover of the Fish and Fish SMA <= a given standard deviation of Fish
- Resistance lines are plotted with their corresponding Fish value when there is a crossunder of the Fish and Fish SMA >= a given standard deviation of Fish
- Reversals are these support and resistance line plots
Breakouts and Volume bars :
Breakouts cause the reversal lines to break (when the high/low is above the resistance/support), Breakouts are more "high quality" when they occur conditional on high volume, the highlighted bars represent volume standard deviations ranging from -3 to 3. When breakouts occure on high volume this may be a sign of the continutaion of the trend (reversals would signify the start of a new trend).
Hope you enjoy, Happy Trading !
(be sure to rocket the script if you liked it, this helps me know which of my scripts are the most useful)
BenfordsLawLibrary "BenfordsLaw"
Methods to deal with Benford's law which states that a distribution of first and higher order digits
of numerical strings has a characteristic pattern.
"Benford's law is an observation about the leading digits of the numbers found in real-world data sets.
Intuitively, one might expect that the leading digits of these numbers would be uniformly distributed so that
each of the digits from 1 to 9 is equally likely to appear. In fact, it is often the case that 1 occurs more
frequently than 2, 2 more frequently than 3, and so on. This observation is a simplified version of Benford's law.
More precisely, the law gives a prediction of the frequency of leading digits using base-10 logarithms that
predicts specific frequencies which decrease as the digits increase from 1 to 9." ~(2)
---
reference:
- 1: en.wikipedia.org
- 2: brilliant.org
- 4: github.com
cumsum_difference(a, b)
Calculate the cumulative sum difference of two arrays of same size.
Parameters:
a (float ) : `array` List of values.
b (float ) : `array` List of values.
Returns: List with CumSum Difference between arrays.
fractional_int(number)
Transform a floating number including its fractional part to integer form ex:. `1.2345 -> 12345`.
Parameters:
number (float) : `float` The number to transform.
Returns: Transformed number.
split_to_digits(number, reverse)
Transforms a integer number into a list of its digits.
Parameters:
number (int) : `int` Number to transform.
reverse (bool) : `bool` `default=true`, Reverse the order of the digits, if true, last will be first.
Returns: Transformed number digits list.
digit_in(number, digit)
Digit at index.
Parameters:
number (int) : `int` Number to parse.
digit (int) : `int` `default=0`, Index of digit.
Returns: Digit found at the index.
digits_from(data, dindex)
Process a list of `int` values and get the list of digits.
Parameters:
data (int ) : `array` List of numbers.
dindex (int) : `int` `default=0`, Index of digit.
Returns: List of digits at the index.
digit_counters(digits)
Score digits.
Parameters:
digits (int ) : `array` List of digits.
Returns: List of counters per digit (1-9).
digit_distribution(counters)
Calculates the frequency distribution based on counters provided.
Parameters:
counters (int ) : `array` List of counters, must have size(9).
Returns: Distribution of the frequency of the digits.
digit_p(digit)
Expected probability for digit according to Benford.
Parameters:
digit (int) : `int` Digit number reference in range `1 -> 9`.
Returns: Probability of digit according to Benford's law.
benfords_distribution()
Calculated Expected distribution per digit according to Benford's Law.
Returns: List with the expected distribution.
benfords_distribution_aprox()
Aproximate Expected distribution per digit according to Benford's Law.
Returns: List with the expected distribution.
test_benfords(digits, calculate_benfords)
Tests Benford's Law on provided list of digits.
Parameters:
digits (int ) : `array` List of digits.
calculate_benfords (bool)
Returns: Tuple with:
- Counters: Score of each digit.
- Sample distribution: Frequency for each digit.
- Expected distribution: Expected frequency according to Benford's.
- Cumulative Sum of difference:
to_table(digits, _text_color, _border_color, _frame_color)
Parameters:
digits (int )
_text_color (color)
_border_color (color)
_frame_color (color)
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
Trend Reversal Probability CalculatorThe "Trend Reversal Probability Calculator" is a TradingView indicator that calculates the probability of a trend reversal based on the crossover of multiple moving averages and the rate of change (ROC) of their slopes. This indicator is designed to help traders identify potential trend reversals by providing signals when the short-term moving averages start to slope in the opposite direction of the long-term moving average.
To use the indicator, simply add it to your TradingView chart and adjust the input parameters according to your preferences. The input parameters include the length of the moving averages, the ROC length (trend sensitivity), and the reversal sensitivity (signal percentage).
The indicator calculates the ROC of the moving averages and determines if the short-term moving averages are sloping in the opposite direction of the long-term moving average. The number of short-term moving averages that meet this condition is then counted, and the probability of a trend reversal is calculated based on the percentage of short-term moving averages that meet this condition.
When the probability of a trend reversal is high, a bullish or bearish signal is generated, depending on the direction of the reversal. The bullish signal is generated when the short-term moving averages start to slope upward, and the bearish signal is generated when the short-term moving averages start to slope downward.
Traders can use the "Trend Reversal Probability Calculator" to identify potential trend reversals and adjust their trading strategies accordingly. It is important to note that this indicator is not a guarantee of a trend reversal and should be used in conjunction with other technical analysis tools to make informed trading decisions.
relative performanceThis indicator is built to mesure the performance of a stock vs the index of choice. it is best use for the intraday session because it doesn't take gap into account when doing the calculation. This is how i made my math (using AAPL compared to SPY for simplicity)
(change AAPL / ATR AAPL) - (change SPY / ATR SPY) * beta factor * volume factor
change is calculated open to close for each candle instead of close to close. this is why gap does not affect the calculation
blue columns is an instant snap shot of the RP
red and green columns is the moving average of the blue columns
limit is the max value for the blue line when ploting them on the chart but doesn't affect the calculation
option:
indice: default with SPY but could use any stock
moving average choice: let you choose between EMA or SMA green and red columns
rolling average length : number of bar for the moving average
I made an auto adjust for the 5 min chart and the 2 min chart so you can swithc between both chart and have the same average (default value set to 6x 5min and 15x 2 min, giving you the average of the last 30min)
volume weighing let you choose if you want a volume factor or not. volume factor is only going to multiplie the result of the price move. it cannot move it from positive to negative.
this is the calculation
(volume AAPL / volume SMA AAPL) / (volume SPY / volume sma SPY)
meaning that a higher volume on the thicker compared to it's sma while having a lower volume on SPY will give you a big relative performance.
you can choose the number of bar in the average for the volume.
BETA factor work the same way that the volume factor does. you got to manualy enter your beta. default is set to 1.5
table
top line : blue square is you RP value (same has the blue columns bar) and your reference thicker
middle line : pourcentage move from the open (9:30 open) for your stock on the left and the reference on the right
bottom line : beta on the left and volume factor on the right
feel free to ask question or give modification idea!
Uptrend Downtrend Loopback Candle Identification LibThis library is for identifying uptrends and downtrends using a loopback candle analysis method. Which contains two functions:
uptrendLoopbackCandleIdentification() and downtrendLoopbackCandleIdentification() . These functions check if the current candle is part of an uptrend or downtrend, respectively, based on the specified lookback period.
The uptrendLoopbackCandleIdentification() takes two arguments: index , which is the index of the current bar, and lookbackPeriod , which is the number of previous candles to check for an uptrend. The function returns false if the index is less than the lookback period. Otherwise, it initializes a boolean variable isHigherHigh as true and loops through the previous candles. If any of the previous candles have a higher high than the current candle, isHigherHigh is set to false , and the loop breaks. Finally, the function returns the value of isHigherHigh .
The downtrendLoopbackCandleIdentification() takes the same arguments and returns false if the index is less than the lookback period. The function initializes a boolean variable isHigherLow as true and loops through the previous candles. If any of the previous candles have a higher low than the current candle, isHigherLow is set to false , and the loop breaks. The function returns the value of isHigherLow .
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.
MF Total Silver Market Capitalization by MigueFinanceThis is the Current Market Capitalization and Historical Chart of Silver
There might be discrepancies in the future on the current market capitalization of silver due to the number of silver ever mined which is always increasing.
So as to update it when necessary, one of the sites you can check to get the most up to date amount is: "https://companiesmarketcap.com/silver/marketcap/" and then edit the amount of tonnes on the settings of this indicator.
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
Goertzel Cycle Composite Wave
Goertzel Browser
Fourier Spectrometer of Price w/ Extrapolation Forecast
Fourier Extrapolator of 'Caterpillar' SSA of Price
Normalized, Variety, Fast Fourier Transform Explorer
Real-Fast Fourier Transform of Price Oscillator
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Fourier Extrapolator of Price
STD-Stepped Fast Cosine Transform Moving Average
Variety RSI of Fast Discrete Cosine Transform
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