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Indicators and strategies

eHarmonicpatternsExtendedLibrary "eHarmonicpatternsExtended" Library provides an alternative method to scan harmonic patterns. This is helpful in reducing iterations. Republishing as new library instead of existing eHarmonicpatterns because I need that copy for existing scripts. scan_xab(bcdRatio, err_min, err_max, patternArray) Checks if bcd ratio is in range of any harmonic pattern   Parameters:      bcdRatio : AB/XA ratio      err_min : minimum error threshold      err_max : maximum error threshold      patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten. scan_abc_axc(abcRatio, axcRatio, err_min, err_max, patternArray) Checks if abc or axc ratio is in range of any harmonic pattern   Parameters:      abcRatio : BC/AB ratio      axcRatio : XC/AX ratio      err_min : minimum error threshold      err_max : maximum error threshold      patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten. scan_bcd(bcdRatio, err_min, err_max, patternArray) Checks if bcd ratio is in range of any harmonic pattern   Parameters:      bcdRatio : CD/BC ratio      err_min : minimum error threshold      err_max : maximum error threshold      patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten. scan_xad_xcd(xadRatio, xcdRatio, err_min, err_max, patternArray) Checks if xad or xcd ratio is in range of any harmonic pattern   Parameters:      xadRatio : AD/XA ratio      xcdRatio : CD/XC ratio      err_min : minimum error threshold      err_max : maximum error threshold      patternArray : Array containing pattern check flags. Checks are made only if flags are true. Upon check flgs are overwritten. isHarmonicPattern(x, a, b, c, d, flags, errorPercent) Checks for harmonic patterns   Parameters:      x : X coordinate value      a : A coordinate value      b : B coordinate value      c : C coordinate value      d : D coordinate value      flags : flags to check patterns. Send empty array to enable all      errorPercent : Error threshold   Returns: Array of boolean values which says whether valid pattern exist and array of corresponding pattern names isHarmonicProjection(x, a, b, c, flags, errorPercent) Checks for harmonic pattern projection   Parameters:      x : X coordinate value      a : A coordinate value      b : B coordinate value      c : C coordinate value      flags : flags to check patterns. Send empty array to enable all      errorPercent : Error threshold   Returns: Array of boolean values which says whether valid pattern exist and array of corresponding pattern names. get_prz_range(x, a, b, c, patternArray, errorPercent, start_adj, end_adj) Provides PRZ range based on BCD and XAD ranges   Parameters:      x : X coordinate value      a : A coordinate value      b : B coordinate value      c : C coordinate value      patternArray : Pattern flags for which PRZ range needs to be calculated      errorPercent : Error threshold      start_adj : - Adjustments for entry levels      end_adj : - Adjustments for stop levels   Returns: Start and end of consolidated PRZ range get_prz_range_xad(x, a, b, c, patternArray, errorPercent, start_adj, end_adj) Provides PRZ range based on XAD range only   Parameters:      x : X coordinate value      a : A coordinate value      b : B coordinate value      c : C coordinate value      patternArray : Pattern flags for which PRZ range needs to be calculated      errorPercent : Error threshold      start_adj : - Adjustments for entry levels      end_adj : - Adjustments for stop levels   Returns: Start and end of consolidated PRZ range
Pine Script® library
by Trendoscope
14
MathProbabilityDistributionLibrary "MathProbabilityDistribution" Probability Distribution Functions. name(idx) Indexed names helper function.   Parameters:      idx : int, position in the range (0, 6).   Returns: string, distribution name. usage: .name(1) Notes: (0) => 'StdNormal' (1) => 'Normal' (2) => 'Skew Normal' (3) => 'Student T' (4) => 'Skew Student T' (5) => 'GED' (6) => 'Skew GED' zscore(position, mean, deviation) Z-score helper function for x calculation.   Parameters:      position : float, position.      mean : float, mean.      deviation : float, standard deviation.   Returns: float, z-score. usage: .zscore(1.5, 2.0, 1.0) std_normal(position) Standard Normal Distribution.   Parameters:      position : float, position.   Returns: float, probability density. usage: .std_normal(0.6) normal(position, mean, scale) Normal Distribution.   Parameters:      position : float, position in the distribution.      mean : float, mean of the distribution, default=0.0 for standard distribution.      scale : float, scale of the distribution, default=1.0 for standard distribution.   Returns: float, probability density. usage: .normal(0.6) skew_normal(position, skew, mean, scale) Skew Normal Distribution.   Parameters:      position : float, position in the distribution.      skew : float, skewness of the distribution.      mean : float, mean of the distribution, default=0.0 for standard distribution.      scale : float, scale of the distribution, default=1.0 for standard distribution.   Returns: float, probability density. usage: .skew_normal(0.8, -2.0) ged(position, shape, mean, scale) Generalized Error Distribution.   Parameters:      position : float, position.      shape : float, shape.      mean : float, mean, default=0.0 for standard distribution.      scale : float, scale, default=1.0 for standard distribution.   Returns: float, probability. usage: .ged(0.8, -2.0) skew_ged(position, shape, skew, mean, scale) Skew Generalized Error Distribution.   Parameters:      position : float, position.      shape : float, shape.      skew : float, skew.      mean : float, mean, default=0.0 for standard distribution.      scale : float, scale, default=1.0 for standard distribution.   Returns: float, probability. usage: .skew_ged(0.8, 2.0, 1.0) student_t(position, shape, mean, scale) Student-T Distribution.   Parameters:      position : float, position.      shape : float, shape.      mean : float, mean, default=0.0 for standard distribution.      scale : float, scale, default=1.0 for standard distribution.   Returns: float, probability. usage: .student_t(0.8, 2.0, 1.0) skew_student_t(position, shape, skew, mean, scale) Skew Student-T Distribution.   Parameters:      position : float, position.      shape : float, shape.      skew : float, skew.      mean : float, mean, default=0.0 for standard distribution.      scale : float, scale, default=1.0 for standard distribution.   Returns: float, probability. usage: .skew_student_t(0.8, 2.0, 1.0) select(distribution, position, mean, scale, shape, skew, log) Conditional Distribution.   Parameters:      distribution : string, distribution name.      position : float, position.      mean : float, mean, default=0.0 for standard distribution.      scale : float, scale, default=1.0 for standard distribution.      shape : float, shape.      skew : float, skew.      log : bool, if true apply log() to the result.   Returns: float, probability. usage: .select('StdNormal', __CYCLE4F__, log=true)
Pine Script® library
by RicardoSantos
6
Adaptive_LengthLibrary "Adaptive_Length" This library contains functions to calculate Adaptive dynamic length which can be used in Moving Averages and other indicators. Two Exponential Moving Averages (EMA) are plotted. Coloring in plot is derived from Chikou filter and Dynamic length of MA1 is adapted using Signal output from Chikou library. dynamic(para, adapt_Pct, minLength, maxLength) Adaptive dynamic length based on boolean parameter   Parameters:      para : Boolean parameter; if true then length would decrease and would increase if its false      adapt_Pct : Percentage adaption based on parameter      minLength : Minimum allowable length      maxLength : Maximum allowable length   Returns: Adaptive Dynamic Length based on Boolean Parameter auto_alpha(src, a) Adaptive length based on automatic alpha calculations from source input   Parameters:      src : Price source for alpha calculations      a : Input Alpha value   Returns: Adaptive Length calculated from input price Source and Alpha
Pine Script® library
by MightyZinger
1
MovingAveragesLibrary "MovingAverages" Contains utilities for generating moving average values including getting a moving average by name and a function for generating a Volume-Adjusted WMA. sma(_D, _len) Simple Moving Avereage   Parameters:      _D : The series to measure from.      _len : The number of bars to measure with. ema(_D, _len) Exponential Moving Avereage   Parameters:      _D : The series to measure from.      _len : The number of bars to measure with. rma(_D, _len) RSI Moving Avereage   Parameters:      _D : The series to measure from.      _len : The number of bars to measure with. wma(_D, _len) Weighted Moving Avereage   Parameters:      _D : The series to measure from.      _len : The number of bars to measure with. vwma(_D, _len) volume-weighted Moving Avereage   Parameters:      _D : The series to measure from. Default is 'close'.      _len : The number of bars to measure with. alma(_D, _len) Arnaud Legoux Moving Avereage   Parameters:      _D : The series to measure from. Default is 'close'.      _len : The number of bars to measure with. cma(_D, _len, C, compound) Coefficient Moving Avereage (CMA) is a variation of a moving average that can simulate SMA or WMA with the advantage of previous data.   Parameters:      _D : The series to measure from. Default is 'close'.      _len : The number of bars to measure with.      C : The coefficient to use when averaging. 0 behaves like SMA, 1 behaves like WMA.      compound : When true (default is false) will use a compounding method for weighting the average. dema(_D, _len) Double Exponential Moving Avereage   Parameters:      _D : The series to measure from. Default is 'close'.      _len : The number of bars to measure with. zlsma(_D, _len) Arnaud Legoux Moving Avereage   Parameters:      _D : The series to measure from. Default is 'close'.      _len : The number of bars to measure with. zlema(_D, _len) Arnaud Legoux Moving Avereage   Parameters:      _D : The series to measure from. Default is 'close'.      _len : The number of bars to measure with. get(type, len, src) Generates a moving average based upon a 'type'.   Parameters:      type : The type of moving average to generate. Values allowed are: SMA, EMA, WMA, VWMA and VAWMA.      len : The number of bars to measure with.      src : The series to measure from. Default is 'close'.   Returns: The moving average series requested.
Pine Script® library
by TanawutWattana
ChikouLibrary "Chikou" This library contains Chikou Filter function to enhances functionality of Chikou-Span from Ichimoku Cloud using a simple trend filter. Chikou is basically close value of ticker offset to close and it is a good for indicating if close value has crossed potential Support/Resistance zone from past. Chikou is usually used with 26 period. Chikou filter uses a lookback length calculated from provided lookback percentage and checks if trend was bullish or bearish within that lookback period. Bullish : Trend is bullish if Chikou span is above high values of all candles within defined lookback period. Bull color shows bullish trend . Bearish : Trend is bearish if Chikou span is below low values of all candles within defined lookback period. This is indicated by Bearish color. Reversal / Choppiness : Reversal color indicates that Chikou are swinging around candles within defined lookback period which is an indication of consolidation or trend reversal. chikou(src, len, perc, _high, _low, bull_col, bear_col, r_col) Chikou Filter for Ichimoku Cloud with Color and Signal Output   Parameters:      src : Price Source (better to use (OHLC4+high+low/3 instead of default close value)      len : Chikou Legth (displaced source value)      perc : Percentage lookback period for Chikou Filter with defined how much candels of total length should be considered for backward filteration      _high : Ticker High Value      _low : Ticker Low Value      bull_col : Color to be returned if source value is greater than all candels within provided lookback percentage.      bear_col : Color to be returned if source value is lower than all candels within provided lookback percentage.      r_col : Color to be returned if source value is swinging around candles within defined lookback period which is an indication of consolidation or trend reversal.   Returns: Color based on trend. 'bull_col' if trend is bullish, 'bear_col' if trend is bearish. 'r_col' if no prominent trend. Integer Signal is also returned as 1 for Bullish, -1 for Bearish and 0 for no prominent trend.
Pine Script® library
by MightyZinger
2
HA_CandlesLibrary "HA_Candles" Heikin Ashi Candles HA_Close() Heikin Ashi Modified Close   Returns: Heikin Ashi Modified Close HA_Open() Heikin Ashi Modified Open   Returns: Heikin Ashi Modified Open HA_High() Heikin Ashi Modified High   Returns: Heikin Ashi Modified High HA_Low() Heikin Ashi Modified Low   Returns: Heikin Ashi Modified Low HA_Delta(Heikin) Heikin Ashi Delta   Parameters:      Heikin : Ashi Close, Heikin Ashi Open   Returns: Heikin Ashi Delta
Pine Script® library
by smhoma
1
easytableLibrary "easytable" Create tables easily, with minimal code      ▦ FEATURES ▦                     █ Create tables █ JSON To Table █ Change Colors █ Array to Rows/Columns █ Pre-Styles █ Change Text Size █ Delete Rows/Columns █ Blink Cells                   indentify_table_id() Identifies all tables ID number in each cell(0,0). get_table_by_id(id_number) Get table object by ID number.   Parameters:      id_number : (int) ID number of the table to fetch.   Returns: table. change_cells_color(table_object, cells_color, start_column, end_column, start_row, end_row) Change cells background colors.   Parameters:      table_object : (table) table object to be changed.      cells_color : (color) Cells color.      start_column : (int) Start column.      end_column : (int) End column.      start_row : (int) Start Row.      end_row : (int) End Row to change.   Returns: Void. change_cells_text_color(table_object, text_color, start_column, end_column, start_row, end_row) Change cells text colors.   Parameters:      table_object : (table) table object to be changed.      text_color : (color) Text color.      start_column : (int) Start column.      end_column : (int) End column.      start_row : (int) Start Row.      end_row : (int) End Row.   Returns: Void. change_all_table_text_color(table_object, text_color, table_column_size, table_row_size) Change All table text color.   Parameters:      table_object : (table) table object to be changed.      text_color : (color) Text color.      table_column_size : (int) Size of the table columns.      table_row_size : (int) Size of the table rows.   Returns: Void. change_table_size(table_object, n_of_columns, n_of_rows, tbl_size) Change table size.   Parameters:      table_object : (table) table object to be changed.      n_of_columns : (int) Size of the table columns.      n_of_rows : (int) Size of the table rows.      tbl_size : (string) size of the table.   Returns: Void. change_cells_text_size(text_size, start_column, end_column, start_row, end_row, table_id) Change table cells text size .   Parameters:      text_size : (string) Text size.      start_column : (int) Start column.      end_column : (int)(optional) End column.      start_row : (int)(optional) Start Row.      end_row : (int)(optional) End Row.      table_id : (int)(optional) Number of the ID of the table.   Returns: Void. table_delete_row(table_object, table_column_size, start_row, end_row) Delete specified rows from table.   Parameters:      table_object : (table) table object to be changed.      table_column_size : (int) Table columns max size.      start_row : (int) Start row to delete.      end_row : (int)(optional) End row to delete (optional — Assumes start_row value).   Returns: Void. table_delete_column(table_object, table_row_size, start_column, end_column) Delete specified columns from table.   Parameters:      table_object : (table) table object to be changed.      table_row_size : (int) Table rows max size.      start_column : (int) Start column to delete.      end_column : (int)(optional) End column to delete (optional — Assumes start_column value).   Returns: Void. array_to_table_column_auto(column_to_insert, array_to_insert, table_id) Insert string array to table column without passing table object.   Parameters:      column_to_insert : (int) Column to be inserted.      array_to_insert : (string array) Start column to delete.      table_id : (int)(optional) Number of the ID of the table.   Returns: Void. array_to_table_row_auto(row_to_insert, array_to_insert, table_id) Insert string array to table row without passing table object.   Parameters:      row_to_insert : (int) Column to be inserted.      array_to_insert : (string array) Start column to delete.      table_id : (int)(optional) Number of the ID of the table.   Returns: Void. array_to_table_row(table_object, row_to_insert, array_to_insert) Insert string array to table row by passing table object.   Parameters:      table_object : (table) table object to be changed.      row_to_insert : (int) Row to be inserted.      array_to_insert : (string array) Start column to delete.   Returns: Void. array_to_table_column(table_object, column_to_insert, array_to_insert) Insert string array to table column by passing table object.   Parameters:      table_object : (table) table object to be changed.      column_to_insert : (int) Column to be inserted.      array_to_insert : (string array) Start column to delete.   Returns: Void. blink_cell(cell_column, cell_row, c_color, blink_interval_ms, table_id) Changes cell color at set intervals (blink).   Parameters:      cell_column : (int) Cell column position.      cell_row : (int) Cell row position.      c_color : (color) Color to blink.      blink_interval_ms : (int)(opt) Interval in milliseconds.      table_id : (int)(opt) Table ID number. change_table_style(table_object, number_of_columns, number_of_rows, color) Changes table pre-style by selecting a pre-style number.   Parameters:      table_object : (table) table object to be changed.      number_of_columns : (int) Table column size.      number_of_rows : (int) Table row size.      color : 1 (color) Color of .   Returns: Void. create_table_clean(n_of_columns, n_of_rows, position) Create a simple(blank) table without any styling.   Parameters:      n_of_columns : (int) Numbers of columns in the table.      n_of_rows : (int) Number of rows in the table.      position : (string) table position.   Returns: table object. create_table_with_style(n_of_columns, n_of_rows, style_number, position) Create table with a pre-set style.   Parameters:      n_of_columns : (int) Numbers of columns in the table.      n_of_rows : (int) Number of rows in the table.      style_number : (int) Style number.      position : (string) table position.   Returns: table object. json_to_table(raw_json) Create table based on input raw json string.   Parameters:      raw_json : (int) Raw json string.   Returns: table object. json_example() Example function that display a table based on a json example_create_table()
Pine Script® library
by MA_PT
8
jsonLibrary "json" Convert JSON strings to tradingview        ▦ FEATURES ▦                         █ Json to array  █ Get json key names  █ Get json key values █ Size of json                   get_json_keys_names(raw_json) Returns string array with all key names   Parameters:      raw_json : (string) Raw JSON string   Returns: (string array) Array with all key names get_values_by_id_name(raw_json, key_name) Returns string array with values of the input key name   Parameters:      raw_json : (string) Raw JSON string      key_name : (string) Name of the key to be fetched   Returns: (string array) Array with values of the input key name size_of_json_string(raw_json) Returns size of raw JSON string   Parameters:      raw_json : (string) Raw JSON string   Returns: Size of n_of_values, size of n_of_keys_names
Pine Script® library
by MA_PT
3
RVSILibrary "RVSI" This Library contains functions that calculate all types of " Relative Volume Strength Index (MZ RVSI ) " depending upon unique volume oscillator. Achieved RVSI value can be used for divergence detection in volume or to adapt dynamic length in Moving Averages or other functions. rvsi_tfs(vol_src, vol_Len, rvsiLen, _open, _close) Relative Volume Strength Index based on TFS Volume Oscillator   Parameters:      vol_src : Volume Source      vol_Len : Volume Legth for TFS Volume Oscillato      rvsiLen : Period of Relative Volume Strength Index      _open : Ticker Open Value      _close : Ticker Close Value   Returns: Relative Volume Strength Index value based on TFS Volume Oscillator rvsi_obv(vol_src, rvsiLen, _close) Relative Volume Strength Index based on On Balance Volume   Parameters:      vol_src : Volume Source to Calculate On Balance Volume      rvsiLen : Period of Relative Volume Strength Index      _close : Ticker Close Value   Returns: Relative Volume Strength Index value based on On Balance Volume rvsi_kvo(vol_src, FastX, SlowX, rvsiLen, _close) Relative Volume Strength Index based on Klinger Volume Oscillator   Parameters:      vol_src : Volume Source      FastX : Volume Fast Length      SlowX : Volume Slow Length      rvsiLen : Period of Relative Volume Strength Index      _close : Ticker Close Value   Returns: Relative Volume Strength Index value based on Klinger Volume Oscillator rvsi_vzo(vol_src, zLen, rvsiLen, _close) Relative Volume Strength Index based on Volume Zone Oscillator   Parameters:      vol_src : Volume Source      zLen : Volume Legth for Volume Zone Oscillator      rvsiLen : Period of Relative Volume Strength Index      _close : Ticker Close Value   Returns: Relative Volume Strength Index value based on Volume Zone Oscillator rvsi_cvo_obv(vol_src, ema1len, ema2len, rvsiLen) Relative Volume Strength Index based on Cumulative Volume Oscillator with On Balance Volume as Calculations Source   Parameters:      vol_src : Volume Source      ema1len : EMA Fast Length      ema2len : EMA Slow Length      rvsiLen : Period of Relative Volume Strength Index   Returns: Relative Volume Strength Index value based on Cumulative Volume Oscillator with On Balance Volume as Calculations Source rvsi_cvo_pvt(vol_src, FastX, SlowX, rvsiLen) Relative Volume Strength Index based on Cumulative Volume Oscillator with Price Volume Trend as Calculations Source   Parameters:      vol_src : Volume Source      FastX : EMA Fast Length      SlowX : EMA Slow Length      rvsiLen : Period of Relative Volume Strength Index   Returns: Relative Volume Strength Index value based on Cumulative Volume Oscillator with Price Volume Trend as Calculations Source rvsi_cvo_cvd(vol_src, FastX, SlowX, rvsiLen, _open, _close, _high, _low) Relative Volume Strength Index based on Cumulative Volume Oscillator with Cumulative Volume Delta as Calculations Source   Parameters:      vol_src : Volume Source      FastX : EMA Fast Length      SlowX : EMA Slow Length      rvsiLen : Period of Relative Volume Strength Index      _open : Ticker Open Value      _close : Ticker Close Value      _high : Ticker High Value      _low : Ticker Low Value   Returns: Relative Volume Strength Index value based on Cumulative Volume Oscillator with Cumulative Volume Delta as Calculations Source
Pine Script® library
by MightyZinger
2
eStrategyLibrary "eStrategy" Library contains methods which can help build custom strategy for continuous investment plans and also compare it with systematic buy and hold. sip(startYear, initialDeposit, depositFrequency, recurringDeposit, buyPrice) Depicts systematic buy and hold over period of time   Parameters:      startYear : Year on which SIP is started      initialDeposit : Initial one time investment at the start      depositFrequency : Frequency of recurring deposit - can be monthly or weekly      recurringDeposit : Recurring deposit amount      buyPrice : Indicatinve buy price. Use high to be conservative. low, close, open, hl2, hlc3, ohlc4, hlcc4 are other options.   Returns: totalInvestment - initial + recurring deposits totalQty - Quantity of units held for given instrument totalEquity - Present equity customStrategy(startYear, initialDeposit, depositFrequency, recurringDeposit, buyPrice, sellPrice, initialInvestmentPercent, recurringInvestmentPercent, signal, tradePercent) Allows users to define custom strategy and enhance systematic buy and hold by adding take profit and reloads   Parameters:      startYear : Year on which SIP is started      initialDeposit : Initial one time investment at the start      depositFrequency : Frequency of recurring deposit - can be monthly or weekly      recurringDeposit : Recurring deposit amount      buyPrice : Indicatinve buy price. Use high to be conservative. low, close, open, hl2, hlc3, ohlc4, hlcc4 are other options.      sellPrice : Indicatinve sell price. Use low to be conservative. high, close, open, hl2, hlc3, ohlc4, hlcc4 are other options.      initialInvestmentPercent : percent of amount to invest from the initial depost. Keep rest of them as cash      recurringInvestmentPercent : percent of amount to invest from recurring deposit. Keep rest of them as cash      signal : can be 1, -1 or 0. 1 means buy/reload. -1 means take profit and 0 means neither.      tradePercent : percent of amount to trade when signal is not 0. If taking profit, it will sell the percent from existing position. If reloading, it will buy with percent from cash reserve   Returns: totalInvestment - initial + recurring deposits totalQty - Quantity of units held for given instrument totalCash = Amount of cash held totalEquity - Overall equity = totalQty*close + totalCash
Pine Script® library
by Trendoscope
11
JohnEhlersFourierTransformLibrary "JohnEhlersFourierTransform" Fourier Transform for Traders By John Ehlers, slightly modified to allow to inspect other than the 8-50 frequency spectrum. reference: www.mesasoftware.com high_pass_filter(source) Detrended version of the data by High Pass Filtering with a 40 Period cutoff   Parameters:      source : float, data source.   Returns: float. transformed_dft(source, start_frequency, end_frequency) DFT by John Elhers.   Parameters:      source : float, data source.      start_frequency : int, lower bound of the frequency window, must be a positive number >= 0, window must be less than or 30.      end_frequency : int, upper bound of the frequency window, must be a positive number >= 0, window must be less than or 30.   Returns: tuple with float, float array. db_to_rgb(db, transparency) converts the frequency decibels to rgb.   Parameters:      db : float, decibels value.      transparency : float, transparency value.   Returns: color.
Pine Script® library
by RicardoSantos
24
windowing_taAll Signals Are the Sum of Sines. When looking at real-world signals, you usually view them as a price changing over time. This is referred to as the time domain. Fourier’s theorem states that any waveform in the time domain can be represented by the weighted sum of sines and cosines. For example, take two sine waves, where one is three times as fast as the other–or the frequency is 1/3 the first signal. When you add them, you can see you get a different signal. Although performing an FFT on a signal can provide great insight, it is important to know the limitations of the FFT and how to improve the signal clarity using windowing. When you use the FFT to measure the frequency component of a signal, you are basing the analysis on a finite set of data. The actual FFT transform assumes that it is a finite data set, a continuous spectrum that is one period of a periodic signal. For the FFT, both the time domain and the frequency domain are circular topologies, so the two endpoints of the time waveform are interpreted as though they were connected together. When the measured signal is periodic and an integer number of periods fill the acquisition time interval, the FFT turns out fine as it matches this assumption. However, many times, the measured signal isn’t an integer number of periods. Therefore, the finiteness of the measured signal may result in a truncated waveform with different characteristics from the original continuous-time signal, and the finiteness can introduce sharp transition changes into the measured signal. The sharp transitions are discontinuities. When the number of periods in the acquisition is not an integer, the endpoints are discontinuous. These artificial discontinuities show up in the FFT as high-frequency components not present in the original signal. These frequencies can be much higher than the Nyquist frequency and are aliased between 0 and half of your sampling rate. The spectrum you get by using a FFT, therefore, is not the actual spectrum of the original signal, but a smeared version. It appears as if energy at one frequency leaks into other frequencies. This phenomenon is known as spectral leakage, which causes the fine spectral lines to spread into wider signals. You can minimize the effects of performing an FFT over a noninteger number of cycles by using a technique called windowing. Windowing reduces the amplitude of the discontinuities at the boundaries of each finite sequence acquired by the digitizer. Windowing consists of multiplying the time record by a finite-length window with an amplitude that varies smoothly and gradually toward zero at the edges. This makes the endpoints of the waveform meet and, therefore, results in a continuous waveform without sharp transitions. This technique is also referred to as applying a window. Here is a windowing_ta library with J.F Ehlers Windowing functions proposed on Sep, 2021. Library "windowing_ta" hann() hamm() fir_sma() fir_triangle()
Pine Script® library
by blackcat1402
2