ml_toolkitLibrary "ml_toolkit"
normalize(src, lookback)
Parameters:
src (float)
lookback (int)
standardize(src, lookback)
Parameters:
src (float)
lookback (int)
sigmoid(x)
Parameters:
x (float)
relu(x)
Parameters:
x (float)
tanh(x)
Parameters:
x (float)
logistic_regression(features, weights, bias)
Parameters:
features (array)
weights (array)
bias (float)
linear_regression(features, weights, bias)
Parameters:
features (array)
weights (array)
bias (float)
ensemble_vote(predictions, weights)
Parameters:
predictions (array)
weights (array)
Statistics
ema_stoploss_libLibrary "ema_stoploss_lib"
This library derives stop-loss levels from a dynamic list of EMA lengths. It computes each EMA internally (so dynamic lengths are allowed), keeps strict side filtering (long: only EMAs below the source; short: only EMAs above), sorts by distance to the source, and returns the n-th nearest value plus the original index of that EMA length.
get_stop_loss(index)
Initializes (once) a default length list:
21, 50, 100, 200, 250, 500, 750, 1000.
Returns:
sl_buy / sl_sell: selected EMA values
nearest_buy_idx / nearest_sell_idx: 0-based indices in the original lensArr
Parameters & Notes
Index (input in the example; default 2) is 0-based:
0 = nearest, 1 = second nearest, 2 = third, etc.
If there aren’t enough EMAs on the requested side, the value becomes na (plot will skip that bar).
Strict filtering means no fallback to the opposite side.
Performance:
EMA updates are O(n) per bar (n = number of lengths).
Sorting is O(k²) (k = candidates on the chosen side) — negligible for small lists.
ema_stoplossLibrary "ema_stoploss"
What it does
A small library that builds stop-loss levels from dynamically computed EMAs. It finds EMAs strictly on the desired side of price (long: below; short: above), sorts them by distance to price, and returns the n-th nearest as your stop.
How it works
sortEMAsByDistanceStrictDyn(signal, lensArr, src, ascending)
Computes each EMA internally with the alpha formula (alpha = 2/(len+1)), so you can pass a dynamic array of lengths.
Strict side filter:
signal = 1 → only EMAs < src (below)
signal = -1 → only EMAs > src (above)
Sorts candidates by distance to src (default: nearest → farthest) and returns two arrays: EMA values and their lengths.
get_stop_loss(index) (exported)
Builds a default length array: 21, 50, 100, 200, 250, 500, 750, 1000.
Long side uses low to find the index-th nearest lower EMA.
Short side uses high to find the index-th nearest upper EMA.
Returns .
Plots
Stop-Loss Long (green): the selected lower EMA (based on low).
Stop-Loss Short (red): the selected upper EMA (based on high).
Input
Index (default 2): 0-based.
0 = nearest, 1 = second nearest, 2 = third, etc.
If there aren’t enough EMAs on the required side, the function returns na (no plot).
Why internal EMA calc?
ta.ema() doesn’t accept a series length; by updating each EMA with its alpha step every bar, the library supports arbitrary dynamic length arrays and stays bar-consistent.
Customize
Edit the list in get_stop_loss() to use your own EMA lengths.
Change ascending in sortEMAsByDistanceStrictDyn if you prefer farthest → nearest.
Use a different src if needed (e.g., close, hlc3, etc.).
The example intentionally uses low for long stops and high for short stops.
Notes
Strict side filtering: EMAs on the wrong side are ignored (no fallback).
If no EMA qualifies on a side, you’ll get na for that side.
Complexity is O(n²) for sorting, which is negligible for small EMA lists.
Adaptive FoS LibraryThis library provides Adaptive Functions that I use in my scripts. For calculations, I use the max_bars_back function with a fixed length of 200 bars to prevent errors when a script tries to access data beyond its available history. This is a key difference from most other adaptive libraries — if you don’t need it, you don’t have to use it.
Some of the adaptive length functions are normalized. In addition to the adaptive length functions, this library includes various methods for calculating moving averages, normalized differences between fast and slow MA's, as well as several normalized oscillators.
utilitiesLibrary for commonly used utilities, for visualizing rolling returns, correlations and sharpe
ATR by Session Library [1CG]Library "ATRxSession"
This library shows you how big the bars usually are during a trading session. It looks only at the times you choose (like New York or London hours), measures the “true range” of every bar in that session, then finds the average for that session. It keeps the last N sessions and gives you their overall average, so you can quickly see how much the market typically moves per bar during your chosen session.
Call getSessionAtr(timezone, session, sessionCount) from your script, and it will return a single number: the average per-bar volatility during the chosen session, based on the last N completed sessions. This makes it easy to plug session-specific volatility into your own indicators or strategies.
getSessionAtr(_timezone, _session, _sessionCount)
getSessionAtr - Computes a session-aware ATR over completed sessions.
Parameters:
_timezone (string) : (string) - Timezone string to evaluate session timing.
_session (string) : (string) - Session time range string (e.g., "0930-1600").
_sessionCount (int) : (int) - Number of past completed sessions to include in the rolling average.
Returns: (float) - The average ATR across the last N completed sessions, or na if not enough data.
TimeSeriesBenchmarkMeasuresLibrary "TimeSeriesBenchmarkMeasures"
Time Series Benchmark Metrics. \
Provides a comprehensive set of functions for benchmarking time series data, allowing you to evaluate the accuracy, stability, and risk characteristics of various models or strategies. The functions cover a wide range of statistical measures, including accuracy metrics (MAE, MSE, RMSE, NRMSE, MAPE, SMAPE), autocorrelation analysis (ACF, ADF), and risk measures (Theils Inequality, Sharpness, Resolution, Coverage, and Pinball).
___
Reference:
- github.com .
- medium.com .
- www.salesforce.com .
- towardsdatascience.com .
- github.com .
mae(actual, forecasts)
In statistics, mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement.
Parameters:
actual (array) : List of actual values.
forecasts (array) : List of forecasts values.
Returns: - Mean Absolute Error (MAE).
___
Reference:
- en.wikipedia.org .
- The Orange Book of Machine Learning - Carl McBride Ellis .
mse(actual, forecasts)
The Mean Squared Error (MSE) is a measure of the quality of an estimator. As it is derived from the square of Euclidean distance, it is always a positive value that decreases as the error approaches zero.
Parameters:
actual (array) : List of actual values.
forecasts (array) : List of forecasts values.
Returns: - Mean Squared Error (MSE).
___
Reference:
- en.wikipedia.org .
rmse(targets, forecasts, order, offset)
Calculates the Root Mean Squared Error (RMSE) between target observations and forecasts. RMSE is a standard measure of the differences between values predicted by a model and the values actually observed.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.
offset (int) : Forecast offset related to target, `default=0`.
Returns: - RMSE value.
nmrse(targets, forecasts, order, offset)
Normalised Root Mean Squared Error.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.
offset (int) : Forecast offset related to target, `default=0`.
Returns: - NRMSE value.
rmse_interval(targets, forecasts)
Root Mean Squared Error for a set of interval windows. Computes RMSE by converting interval forecasts (with min/max bounds) into point forecasts using the mean of the interval bounds, then compares against actual target values.
Parameters:
targets (array) : List of target observations.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - RMSE value for the combined interval list.
mape(targets, forecasts)
Mean Average Percentual Error.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
Returns: - MAPE value.
smape(targets, forecasts, mode)
Symmetric Mean Average Percentual Error. Calculates the Mean Absolute Percentage Error (MAPE) between actual targets and forecasts. MAPE is a common metric for evaluating forecast accuracy, expressed as a percentage, lower values indicate a better forecast accuracy.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
mode (int) : Type of method: default=0:`sum(abs(Fi-Ti)) / sum(Fi+Ti)` , 1:`mean(abs(Fi-Ti) / ((Fi + Ti) / 2))` , 2:`mean(abs(Fi-Ti) / (abs(Fi) + abs(Ti))) * 100`
Returns: - SMAPE value.
mape_interval(targets, forecasts)
Mean Average Percentual Error for a set of interval windows.
Parameters:
targets (array) : List of target observations.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - MAPE value for the combined interval list.
acf(data, k)
Autocorrelation Function (ACF) for a time series at a specified lag.
Parameters:
data (array) : Sample data of the observations.
k (int) : The lag period for which to calculate the autocorrelation. Must be a non-negative integer.
Returns: - The autocorrelation value at the specified lag, ranging from -1 to 1.
___
The autocorrelation function measures the linear dependence between observations in a time series
at different time lags. It quantifies how well the series correlates with itself at different
time intervals, which is useful for identifying patterns, seasonality, and the appropriate
lag structure for time series models.
ACF values close to 1 indicate strong positive correlation, values close to -1 indicate
strong negative correlation, and values near 0 indicate no linear correlation.
___
Reference:
- statisticsbyjim.com
acf_multiple(data, k)
Autocorrelation function (ACF) for a time series at a set of specified lags.
Parameters:
data (array) : Sample data of the observations.
k (array) : List of lag periods for which to calculate the autocorrelation. Must be a non-negative integer.
Returns: - List of ACF values for provided lags.
___
The autocorrelation function measures the linear dependence between observations in a time series
at different time lags. It quantifies how well the series correlates with itself at different
time intervals, which is useful for identifying patterns, seasonality, and the appropriate
lag structure for time series models.
ACF values close to 1 indicate strong positive correlation, values close to -1 indicate
strong negative correlation, and values near 0 indicate no linear correlation.
___
Reference:
- statisticsbyjim.com
adfuller(data, n_lag, conf)
: Augmented Dickey-Fuller test for stationarity.
Parameters:
data (array) : Data series.
n_lag (int) : Maximum lag.
conf (string) : Confidence Probability level used to test for critical value, (`90%`, `95%`, `99%`).
Returns: - `adf` The test statistic.
- `crit` Critical value for the test statistic at the 10 % levels.
- `nobs` Number of observations used for the ADF regression and calculation of the critical values.
___
The Augmented Dickey-Fuller test is used to determine whether a time series is stationary
or contains a unit root (non-stationary). The null hypothesis is that the series has a unit root
(is non-stationary), while the alternative hypothesis is that the series is stationary.
A stationary time series has statistical properties that do not change over time, making it
suitable for many time series forecasting models. If the test statistic is less than the
critical value, we reject the null hypothesis and conclude the series is stationary.
___
Reference:
- www.jstor.org
- en.wikipedia.org
theils_inequality(targets, forecasts)
Calculates Theil's Inequality Coefficient, a measure of forecast accuracy that quantifies the relative difference between actual and predicted values.
Parameters:
targets (array) : List of target observations.
forecasts (array) : Matrix with list of forecasts, ordered column wise.
Returns: - Theil's Inequality Coefficient value, value closer to 0 is better.
___
Theil's Inequality Coefficient is calculated as: `sqrt(Sum((y_i - f_i)^2)) / (sqrt(Sum(y_i^2)) + sqrt(Sum(f_i^2)))`
where `y_i` represents actual values and `f_i` represents forecast values.
This metric ranges from 0 to infinity, with 0 indicating perfect forecast accuracy.
___
Reference:
- en.wikipedia.org
sharpness(forecasts)
The average width of the forecast intervals across all observations, representing the sharpness or precision of the predictive intervals.
Parameters:
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - Sharpness The sharpness level, which is the average width of all prediction intervals across the forecast horizon.
___
Sharpness is an important metric for evaluating forecast quality. It measures how narrow or wide the
prediction intervals are. Higher sharpness (narrower intervals) indicates greater precision in the
forecast intervals, while lower sharpness (wider intervals) suggests less precision.
The sharpness metric is calculated as the mean of the interval widths across all observations, where
each interval width is the difference between the upper and lower bounds of the prediction interval.
Note: This function assumes that the forecasts matrix has at least 2 columns, with the first column
representing the lower bounds and the second column representing the upper bounds of prediction intervals.
___
Reference:
- Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. otexts.com
resolution(forecasts)
Calculates the resolution of forecast intervals, measuring the average absolute difference between individual forecast interval widths and the overall sharpness measure.
Parameters:
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - The average absolute difference between individual forecast interval widths and the overall sharpness measure, representing the resolution of the forecasts.
___
Resolution is a key metric for evaluating forecast quality that measures the consistency of prediction
interval widths. It quantifies how much the individual forecast intervals vary from the average interval
width (sharpness). High resolution indicates that the forecast intervals are relatively consistent
across observations, while low resolution suggests significant variation in interval widths.
The resolution is calculated as the mean absolute deviation of individual interval widths from the
overall sharpness value. This provides insight into the uniformity of the forecast uncertainty
estimates across the forecast horizon.
Note: This function requires the forecasts matrix to have at least 2 columns (min, max) representing
the lower and upper bounds of prediction intervals.
___
Reference:
- (sites.stat.washington.edu)
- (www.jstor.org)
coverage(targets, forecasts)
Calculates the coverage probability, which is the percentage of target values that fall within the corresponding forecasted prediction intervals.
Parameters:
targets (array) : List of target values.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - Percent of target values that fall within their corresponding forecast intervals, expressed as a decimal value between 0 and 1 (or 0% and 100%).
___
Coverage probability is a crucial metric for evaluating the reliability of prediction intervals.
It measures how well the forecast intervals capture the actual observed values. An ideal forecast
should have a coverage probability close to the nominal confidence level (e.g., 90%, 95%, or 99%).
For example, if a 95% prediction interval is used, we expect approximately 95% of the actual
target values to fall within those intervals. If the coverage is significantly lower than the
nominal level, the intervals may be too narrow; if it's significantly higher, the intervals may
be too wide.
Note: This function requires the targets array and forecasts matrix to have the same number of
observations, and the forecasts matrix must have at least 2 columns (min, max) representing
the lower and upper bounds of prediction intervals.
___
Reference:
- (www.jstor.org)
pinball(tau, target, forecast)
Pinball loss function, measures the asymmetric loss for quantile forecasts.
Parameters:
tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.
target (float) : The actual observed value to compare against.
forecast (float) : The forecasted value.
Returns: - The Pinball loss value, which quantifies the distance between the forecast and target relative to the specified quantile level.
___
The Pinball loss function is specifically designed for evaluating quantile forecasts. It is
asymmetric, meaning it penalizes underestimates and overestimates differently depending on the
quantile level being evaluated.
For a given quantile τ, the loss function is defined as:
- If target >= forecast: (target - forecast) * τ
- If target < forecast: (forecast - target) * (1 - τ)
This loss function is commonly used in quantile regression and probabilistic forecasting
to evaluate how well forecasts capture specific quantiles of the target distribution.
___
Reference:
- (www.otexts.com)
pinball_mean(tau, targets, forecasts)
Calculates the mean pinball loss for quantile regression.
Parameters:
tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.
targets (array) : The actual observed values to compare against.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - The mean pinball loss value across all observations.
___
The pinball_mean() function computes the average Pinball loss across multiple observations,
making it suitable for evaluating overall forecast performance in quantile regression tasks.
This function leverages the asymmetric Pinball loss function to evaluate how well forecasts
capture specific quantiles of the target distribution. The choice of which column from the
forecasts matrix to use depends on the quantile level:
- For τ ≤ 0.5: Uses the first column (min) of forecasts
- For τ > 0.5: Uses the second column (max) of forecasts
This loss function is commonly used in quantile regression and probabilistic forecasting
to evaluate how well forecasts capture specific quantiles of the target distribution.
___
Reference:
- (www.otexts.com)
Correlation HeatMap Matrix Data [TradingFinder]🔵 Introduction
Correlation is a statistical measure that shows the degree and direction of a linear relationship between two assets.
Its value ranges from -1 to +1 : +1 means perfect positive correlation, 0 means no linear relationship, and -1 means perfect negative correlation.
In financial markets, correlation is used for portfolio diversification, risk management, pairs trading, intermarket analysis, and identifying divergences.
Correlation HeatMap Matrix Data TradingFinder is a Pine Script v6 library that calculates and returns raw correlation matrix data between up to 20 symbols. It only provides the data – it does not draw or render the heatmap – making it ideal for use in other scripts that handle visualization or further analysis. The library uses ta.correlation for fast and accurate calculations.
It also includes two helper functions for visual styling :
CorrelationColor(corr) : takes the correlation value as input and generates a smooth gradient color, ranging from strong negative to strong positive correlation.
CorrelationTextColor(corr) : takes the correlation value as input and returns a text color that ensures optimal contrast over the background color.
Library
"Correlation_HeatMap_Matrix_Data_TradingFinder"
CorrelationColor(corr)
Parameters:
corr (float)
CorrelationTextColor(corr)
Parameters:
corr (float)
Data_Matrix(Corr_Period, Sym_1, Sym_2, Sym_3, Sym_4, Sym_5, Sym_6, Sym_7, Sym_8, Sym_9, Sym_10, Sym_11, Sym_12, Sym_13, Sym_14, Sym_15, Sym_16, Sym_17, Sym_18, Sym_19, Sym_20)
Parameters:
Corr_Period (int)
Sym_1 (string)
Sym_2 (string)
Sym_3 (string)
Sym_4 (string)
Sym_5 (string)
Sym_6 (string)
Sym_7 (string)
Sym_8 (string)
Sym_9 (string)
Sym_10 (string)
Sym_11 (string)
Sym_12 (string)
Sym_13 (string)
Sym_14 (string)
Sym_15 (string)
Sym_16 (string)
Sym_17 (string)
Sym_18 (string)
Sym_19 (string)
Sym_20 (string)
🔵 How to use
Import the library into your Pine Script using the import keyword and its full namespace.
Decide how many symbols you want to include in your correlation matrix (up to 20). Each symbol must be provided as a string, for example FX:EURUSD .
Choose the correlation period (Corr\_Period) in bars. This is the lookback window used for the calculation, such as 20, 50, or 100 bars.
Call Data_Matrix(Corr_Period, Sym_1, ..., Sym_20) with your selected parameters. The function will return an array containing the correlation values for every symbol pair (upper triangle of the matrix plus diagonal).
For example :
var string Sym_1 = '' , var string Sym_2 = '' , var string Sym_3 = '' , var string Sym_4 = '' , var string Sym_5 = '' , var string Sym_6 = '' , var string Sym_7 = '' , var string Sym_8 = '' , var string Sym_9 = '' , var string Sym_10 = ''
var string Sym_11 = '', var string Sym_12 = '', var string Sym_13 = '', var string Sym_14 = '', var string Sym_15 = '', var string Sym_16 = '', var string Sym_17 = '', var string Sym_18 = '', var string Sym_19 = '', var string Sym_20 = ''
switch Market
'Forex' => Sym_1 := 'EURUSD' , Sym_2 := 'GBPUSD' , Sym_3 := 'USDJPY' , Sym_4 := 'USDCHF' , Sym_5 := 'USDCAD' , Sym_6 := 'AUDUSD' , Sym_7 := 'NZDUSD' , Sym_8 := 'EURJPY' , Sym_9 := 'EURGBP' , Sym_10 := 'GBPJPY'
,Sym_11 := 'AUDJPY', Sym_12 := 'EURCHF', Sym_13 := 'EURCAD', Sym_14 := 'GBPCAD', Sym_15 := 'CADJPY', Sym_16 := 'CHFJPY', Sym_17 := 'NZDJPY', Sym_18 := 'AUDNZD', Sym_19 := 'USDSEK' , Sym_20 := 'USDNOK'
'Stock' => Sym_1 := 'NVDA' , Sym_2 := 'AAPL' , Sym_3 := 'GOOGL' , Sym_4 := 'GOOG' , Sym_5 := 'META' , Sym_6 := 'MSFT' , Sym_7 := 'AMZN' , Sym_8 := 'AVGO' , Sym_9 := 'TSLA' , Sym_10 := 'BRK.B'
,Sym_11 := 'UNH' , Sym_12 := 'V' , Sym_13 := 'JPM' , Sym_14 := 'WMT' , Sym_15 := 'LLY' , Sym_16 := 'ORCL', Sym_17 := 'HD' , Sym_18 := 'JNJ' , Sym_19 := 'MA' , Sym_20 := 'COST'
'Crypto' => Sym_1 := 'BTCUSD' , Sym_2 := 'ETHUSD' , Sym_3 := 'BNBUSD' , Sym_4 := 'XRPUSD' , Sym_5 := 'SOLUSD' , Sym_6 := 'ADAUSD' , Sym_7 := 'DOGEUSD' , Sym_8 := 'AVAXUSD' , Sym_9 := 'DOTUSD' , Sym_10 := 'TRXUSD'
,Sym_11 := 'LTCUSD' , Sym_12 := 'LINKUSD', Sym_13 := 'UNIUSD', Sym_14 := 'ATOMUSD', Sym_15 := 'ICPUSD', Sym_16 := 'ARBUSD', Sym_17 := 'APTUSD', Sym_18 := 'FILUSD', Sym_19 := 'OPUSD' , Sym_20 := 'USDT.D'
'Custom' => Sym_1 := Sym_1_C , Sym_2 := Sym_2_C , Sym_3 := Sym_3_C , Sym_4 := Sym_4_C , Sym_5 := Sym_5_C , Sym_6 := Sym_6_C , Sym_7 := Sym_7_C , Sym_8 := Sym_8_C , Sym_9 := Sym_9_C , Sym_10 := Sym_10_C
,Sym_11 := Sym_11_C, Sym_12 := Sym_12_C, Sym_13 := Sym_13_C, Sym_14 := Sym_14_C, Sym_15 := Sym_15_C, Sym_16 := Sym_16_C, Sym_17 := Sym_17_C, Sym_18 := Sym_18_C, Sym_19 := Sym_19_C , Sym_20 := Sym_20_C
= Corr.Data_Matrix(Corr_period, Sym_1 ,Sym_2 ,Sym_3 ,Sym_4 ,Sym_5 ,Sym_6 ,Sym_7 ,Sym_8 ,Sym_9 ,Sym_10,Sym_11,Sym_12,Sym_13,Sym_14,Sym_15,Sym_16,Sym_17,Sym_18,Sym_19,Sym_20)
Loop through or index into this array to retrieve each correlation value for your custom layout or logic.
Pass each correlation value to CorrelationColor() to get the corresponding gradient background color, which reflects the correlation’s strength and direction (negative to positive).
For example :
Corr.CorrelationColor(SYM_3_10)
Pass the same correlation value to CorrelationTextColor() to get the correct text color for readability against that background.
For example :
Corr.CorrelationTextColor(SYM_1_1)
Use these colors in a table or label to render your own heatmap or any other visualization you need.
FunctionADFLibrary "FunctionADF"
Augmented Dickey-Fuller test (ADF), The ADF test is a statistical method used to assess whether a time series is stationary – meaning its statistical properties (like mean and variance) do not change over time. A time series with a unit root is considered non-stationary and often exhibits non-mean-reverting behavior, which is a key concept in technical analysis.
Reference:
-
- rtmath.net
- en.wikipedia.org
adftest(data, n_lag, conf)
: Augmented Dickey-Fuller test for stationarity.
Parameters:
data (array) : Data series.
n_lag (int) : Maximum lag.
conf (string) : Confidence Probability level used to test for critical value, (`90%`, `95%`, `99%`).
Returns: `adf` The test statistic. \
`crit` Critical value for the test statistic at the 10 % levels. \
`nobs` Number of observations used for the ADF regression and calculation of the critical values.
AnnualizedReturnCalculatorLibrary "AnnualizedReturnCalculator"
TODO: add library description here
calculateAnnualizedReturn(isStartTime, enableLog)
Parameters:
isStartTime (bool) : 开始时间的BOOL值变量（用于标记策略开始时间）
enableLog (bool) : 是否输出日志
Returns:
返回持仓基准年化收益率、资金基准年化收益率、总收益、平均资金占用
LiliALHUNTERSystem_v2📚 **Library: LiliALHUNTERSystem_v2**
This library provides a powerful target management system for Pine Script developers.
It includes advanced calculators for EMA, RMA, and Supertrend, and introduces a central `createTargets()` function to dynamically render target lines and labels based on long/short trade logic.
🛠️ **Main Features:**
– Dynamic horizontal & vertical target lines
– Dual target configuration (Target 1 & Target 2)
– Directional logic via `isLong1`, `isLong2`
– Integrated Supertrend validation
– Visual dashboard and label display
– Works seamlessly with custom indicators
🎯 **Purpose:**
The `LiliALHUNTERSystem_v2` Library enables Pine coders to manage and visualize targets consistently across all trading strategies and indicators. It simplifies target logic while maintaining visual clarity and modular usage.
⚠️ **Disclaimer:**
This script is intended for educational and analytical purposes only. It does not constitute financial advice.
Library "LiliALHUNTERSystem_v2"
ema_calc(len, source)
Parameters:
len (simple int)
source (float)
rma_calc(len, source)
Parameters:
len (simple int)
source (float)
supertrend_calc(length, factor)
Parameters:
length (simple int)
factor (float)
createTargets(config, state, source1A, source1B, source2A, source2B)
Parameters:
config (TargetConfig)
state (TargetState)
source1A (float)
source1B (float)
source2A (float)
source2B (float)
showDashboard(state, dashLoc, textSize)
Parameters:
state (TargetState)
dashLoc (string)
textSize (string)
TargetConfig
Fields:
enableTarget1 (series bool)
enableTarget2 (series bool)
isLong1 (series bool)
isLong2 (series bool)
target1Condition (series string)
target2Condition (series string)
target1Color (series color)
target2Color (series color)
target1Style (series string)
target2Style (series string)
distTarget1 (series float)
distTarget2 (series float)
distOptions1 (series string)
distOptions2 (series string)
showLabels (series bool)
showDash (series bool)
TargetState
Fields:
target1LineV (series line)
target1LineH (series line)
target2LineV (series line)
target2LineH (series line)
target1Lbl (series label)
target2Lbl (series label)
target1Active (series bool)
target2Active (series bool)
target1Value (series float)
target2Value (series float)
countTargets1 (series int)
countTgReached1 (series int)
countTargets2 (series int)
countTgReached2 (series int)
FastMetrixLibrary "FastMetrix"
This is a library I've been tweaking and working with for a while and I find it useful to get valuable technical analysis metrics faster (why its called FastMetrix). A lot of is personal to my trading style, so sorry if it does not have everything you want. The way I get my variables from library to script is by copying the return function into my new script.
TODO: Volatility and short term price analysis functions
slope(source, smoothing)
Parameters:
source (float)
smoothing (int)
integral(topfunction, bottomfunction, start, end)
Parameters:
topfunction (float)
bottomfunction (float)
start (int)
end (int)
deviation(x, y)
Parameters:
x (float)
y (float)
getema(len)
TODO: return important exponential long term moving averages and derivatives/variables
Parameters:
len (simple int)
getsma(len)
TODO: return requested sma
Parameters:
len (int)
kc(mult, len)
TODO: Return Keltner Channels variables and calculations
Parameters:
mult (simple float)
len (simple int)
bollinger(len, mult)
TODO: returns bollinger bands with optimal settings
Parameters:
len (int)
mult (simple float)
volatility(atrlen, smoothing)
TODO: Returns volatility indicators based on atr
Parameters:
atrlen (simple int)
smoothing (int)
premarketfib()
countinday(xcondition)
Parameters:
xcondition (bool)
countinsession(condition, n)
Parameters:
condition (bool)
n (int)
LMAsLibrary "LMAs"
Credits
Thank you to @QuantraSystems for dynamic calculations.
Introduction
This lightweight library offers dynamic implementations of popular moving averages that adapt their length automatically as new bars are added to the chart.
Each function is built on a dynamic length formula:
len = math.min(maxLength, bar_index + 1)
This approach ensures that calculations begin as early as the first bar, allowing for smoother initialization and more consistent behavior across all timeframes. It’s especially useful in custom scripts that run from bar 0 or when historical data is limited.
Usage
You can use this library as a drop-in replacement for standard moving averages. It provides more flexibility and stability in live or backtesting environments where fixed-length indicators may delay or fail to initialize properly.
Why Use This?
• Works from the very first bar
• Avoids na values during early bars
• Great for real-time indicators, strategies, and bar-replay
• Clean and efficient code with dynamic behavior
How to Use
Import the library into your script and call any of the included functions just like you would with their native counterparts.
Summary
A lightweight Pine Script™ library offering dynamic moving averages that work seamlessly from the very first bar. Ideal for strategies and indicators requiring robust initialization and adaptive behavior.
SMA(sourceData, maxLength)
Dynamic SMA
Parameters:
sourceData (float)
maxLength (int)
EMA(src, length)
Dynamic EMA
Parameters:
src (float)
length (int)
DEMA(src, length)
Dynamic DEMA
Parameters:
src (float)
length (int)
TEMA(src, length)
Dynamic TEMA
Parameters:
src (float)
length (int)
WMA(src, length)
Dynamic WMA
Parameters:
src (float)
length (int)
HMA(src, length)
Dynamic HMA
Parameters:
src (float)
length (int)
VWMA(src, volsrc, length)
Dynamic VWMA
Parameters:
src (float)
volsrc (float)
length (int)
SMMA(src, length)
Dynamic SMMA
Parameters:
src (float)
length (int)
LSMA(src, length, offset)
Dynamic LSMA
Parameters:
src (float)
length (int)
offset (int)
RMA(src, length)
Dynamic RMA
Parameters:
src (float)
length (int)
ALMA(src, length, offset_sigma, sigma)
Dynamic ALMA
Parameters:
src (float)
length (int)
offset_sigma (float)
sigma (float)
ZLSMA(src, length)
Dynamic ZLSMA
Parameters:
src (float)
length (int)
FRAMA(src, length)
Parameters:
src (float)
length (int)
KAMA(src, length)
Dynamic KAMA
Parameters:
src (float)
length (int)
JMA(src, length, phase)
Dynamic JMA
Parameters:
src (float)
length (int)
phase (float)
T3(src, length, volumeFactor)
Dynamic T3
Parameters:
src (float)
length (int)
volumeFactor (float)
TAIndicatorsThis library offers a comprehensive suite of enhanced technical indicator functions, building upon TradingView's built-in indicators. The primary advantage of this library is its expanded flexibility, allowing you to select from a wider range of moving average types for calculations and smoothing across various indicators.
The core difference between these functions and TradingView's standard ones is the ability to specify different moving average types beyond the default. While a standard ta.rsi() is fixed, the rsi() in this library, for example, can be smoothed by an 'SMMA (RMA)', 'WMA', 'VWMA', or others, giving you greater control over your analysis.
█ FEATURES
This library provides enhanced versions of the following popular indicators:
Moving Average (ma): A versatile MA function that includes optional secondary smoothing and Bollinger Bands.
RSI (rsi): Calculate RSI with an optional smoothed signal line using various MA types, plus built-in divergence detection.
MACD (macd): A MACD function where you can define the MA type for both the main calculation and the signal line.
ATR (atr): An ATR function that allows for different smoothing types.
VWAP (vwap): A comprehensive anchored VWAP with multiple configurable bands.
ADX (adx): A standard ADX calculation.
Cumulative Volume Delta (cvd): Provides CVD data based on a lower timeframe.
Bollinger Bands (bb): Create Bollinger Bands with a customizable MA type for the basis line.
Keltner Channels (kc): Keltner Channels with selectable MA types and band styles.
On-Balance Volume (obv): An OBV indicator with an optional smoothed signal line using various MA types.
... and more to come! This library will be actively maintained, with new useful indicator functions added over time.
█ HOW TO USE
To use this library in your scripts, import it using its publishing link. You can then call the functions directly.
For example, to calculate a Weighted Moving Average (WMA) and then smooth it with a Simple Moving Average (SMA) :
import ActiveQuants/TAIndicators/1 as tai
// Calculate a 20-period WMA of the close
// Then, smooth the result with a 10-period SMA
= tai.ma("WMA", close, 20, "SMA", 10)
plot(myWma, color = color.blue)
plot(smoothedWma, color = color.orange)
█ Why Choose This Library?
If you're looking for more control and customization than what's offered by the standard built-in functions, this library is for you. By allowing for a variety of smoothing methods across multiple indicators, it enables a more nuanced and personalized approach to technical analysis. Fine-tune your indicators to better fit your trading style and strategies.
StatMetricsLibrary "StatMetrics"
A utility library for common statistical indicators and ratios used in technical analysis.
Includes Z-Score, correlation, PLF, SRI, Sharpe, Sortino, Omega ratios, and normalization tools.
zscore(src, len)
Calculates the Z-score of a series
Parameters:
src (float) : The input price or series (e.g., close)
len (simple int) : The lookback period for mean and standard deviation
Returns: Z-score: number of standard deviations the input is from the mean
corr(x, y, len)
Computes Pearson correlation coefficient between two series
Parameters:
x (float) : First series
y (float) : Second series
len (simple int) : Lookback period
Returns: Correlation coefficient between -1 and 1
plf(src, longLen, shortLen, smoothLen)
Calculates the Price Lag Factor (PLF) as the difference between long and short Z-scores, normalized and smoothed
Parameters:
src (float) : Source series (e.g., close)
longLen (simple int) : Long Z-score period
shortLen (simple int) : Short Z-score period
smoothLen (simple int) : Hull MA smoothing length
Returns: Smoothed and normalized PLF oscillator
sri(signal, len)
Computes the Statistical Reliability Index (SRI) based on trend persistence
Parameters:
signal (float) : A price or signal series (e.g., smoothed PLF)
len (simple int) : Lookback period for smoothing and deviation
Returns: Normalized trend reliability score
sharpe(src, len)
Calculates the Sharpe Ratio over a period
Parameters:
src (float) : Price series (e.g., close)
len (simple int) : Lookback period
Returns: Sharpe ratio value
sortino(src, len)
Calculates the Sortino Ratio over a period, using only downside volatility
Parameters:
src (float) : Price series
len (simple int) : Lookback period
Returns: Sortino ratio value
omega(src, len)
Calculates the Omega Ratio as the ratio of upside to downside return area
Parameters:
src (float) : Price series
len (simple int) : Lookback period
Returns: Omega ratio value
beta(asset, benchmark, len)
Calculates beta coefficient of asset vs benchmark using rolling covariance
Parameters:
asset (float) : Series of the asset (e.g., close)
benchmark (float) : Series of the benchmark (e.g., SPX close)
len (simple int) : Lookback window
Returns: Beta value (slope of linear regression)
alpha(asset, benchmark, len)
Calculates rolling alpha of an asset relative to a benchmark
Parameters:
asset (float) : Series of the asset (e.g., close)
benchmark (float) : Series of the benchmark (e.g., SPX close)
len (simple int) : Lookback window
Returns: Alpha value (excess return not explained by Beta exposure)
skew(x, len)
Computes skewness of a return series
Parameters:
x (float) : Input series (e.g., returns)
len (simple int) : Lookback period
Returns: Skewness value
kurtosis(x, len)
Computes kurtosis of a return series
Parameters:
x (float) : Input series (e.g., returns)
len (simple int) : Lookback period
Returns: Kurtosis value
cv(x, len)
Calculates Coefficient of Variation
Parameters:
x (float) : Input series (e.g., returns or prices)
len (simple int) : Lookback period
Returns: CV value
autocorr(x, len)
Calculates autocorrelation with 1-lag
Parameters:
x (float) : Series to test
len (simple int) : Lookback window
Returns: Autocorrelation at lag 1
stderr(x, len)
Calculates rolling standard error of a series
Parameters:
x (float) : Input series
len (simple int) : Lookback window
Returns: Standard error (std dev / sqrt(n))
info_ratio(asset, benchmark, len)
Calculates the Information Ratio
Parameters:
asset (float) : Asset price series
benchmark (float) : Benchmark price series
len (simple int) : Lookback period
Returns: Information ratio (alpha / tracking error)
tracking_error(asset, benchmark, len)
Measures deviation from benchmark (Tracking Error)
Parameters:
asset (float) : Asset return series
benchmark (float) : Benchmark return series
len (simple int) : Lookback window
Returns: Tracking error value
max_drawdown(x, len)
Computes maximum drawdown over a rolling window
Parameters:
x (float) : Price series
len (simple int) : Lookback window
Returns: Rolling max drawdown percentage (as a negative value)
zscore_signal(z, ob, os)
Converts Z-score into a 3-level signal
Parameters:
z (float) : Z-score series
ob (float) : Overbought threshold
os (float) : Oversold threshold
Returns: -1, 0, or 1 depending on signal state
r_squared(x, y, len)
Calculates rolling R-squared (coefficient of determination)
Parameters:
x (float) : Asset returns
y (float) : Benchmark returns
len (simple int) : Lookback window
Returns: R-squared value (0 to 1)
entropy(x, len)
Approximates Shannon entropy using log returns
Parameters:
x (float) : Price series
len (simple int) : Lookback period
Returns: Approximate entropy
zreversal(z)
Detects Z-score reversals to the mean
Parameters:
z (float) : Z-score series
Returns: +1 on upward reversal, -1 on downward
momentum_rank(x, len)
Calculates relative momentum strength
Parameters:
x (float) : Price series
len (simple int) : Lookback window
Returns: Proportion of lookback where current price is higher
normalize(x, len)
Normalizes a series to a 0–1 range over a period
Parameters:
x (float) : The input series
len (simple int) : Lookback period
Returns: Normalized value between 0 and 1
composite_score(score1, score2, score3)
Combines multiple normalized scores into a composite score
Parameters:
score1 (float)
score2 (float)
score3 (float)
Returns: Average composite score
MonthlyPnLTableLibrary "MonthlyPnLTable"
monthlyPnL(currentClose, initialOpenPrice, monthsToDisplay)
Parameters:
currentClose (float)
initialOpenPrice (float)
monthsToDisplay (int)
displayPnLTable(pnls, pnlMonths, pnlYears, textSizeOption, labelColor)
Parameters:
pnls (array)
pnlMonths (array)
pnlYears (array)
textSizeOption (string)
labelColor (color)
Crypto_in_details_MAlibCrypto_in_details_MaLib — Advanced Moving Average Library for Pine Script
Overview:
Crypto_in_details_MaLib is a comprehensive, performance-optimized Moving Average (MA) library designed specifically for Pine Script v6 users seeking advanced technical analysis tools. Developed by Crypto_in_details, this library consolidates the most popular and sophisticated MA calculation methods — including classical, weighted, exponential, and Hull variants — into one seamless package.
Key Features:
Implements a wide range of Moving Averages: SMA, EMA, WMA, RMA, VWMA, HMA, TEMA, EHMA, THMA.
Designed for precision and flexibility — suitable for diverse trading strategies and indicator development.
Fully typed functions compatible with Pine Script v6 standards.
Simplifies your scripting workflow by providing ready-to-use MA functions via clean and easy-to-import methods.
Well-documented and maintained by an experienced Pine Script developer.
Why Use Crypto_in_details_MaLib?
Gain access to advanced MA calculations that enhance trend analysis, smoothing, and signal accuracy.
Save time on coding complex moving averages from scratch.
Easily extend or combine with your own strategies or indicators for improved performance.
Rely on a tested and community-driven solution backed by a prolific Pine Script author.
Ideal for:
Traders and developers building custom indicators or strategies requiring versatile MA techniques.
Anyone looking to improve their Pine Script efficiency and code maintainability.
Pine Script enthusiasts wanting a professional-grade MA toolkit.
VolumeFlowOscillatorLibVolume Flow Oscillator Library
Overview
The Volume Flow Oscillator library provides a comprehensive framework for analyzing directional volume flow in financial markets. It creates a multi-band oscillator system that transforms price and volume data into a spectrum of sensitivity bands, revealing the underlying buying and selling pressure.
Technical Approach
The library combines price direction with trading volume to generate an oscillator that fluctuates around a zero line, with positive values indicating buying pressure and negative values showing selling pressure. Using sophisticated ALMA (Arnaud Legoux Moving Average) smoothing techniques with asymmetric sensitivity, the library creates seven distinct bands that help identify various intensity levels of volume flow.
Key Features
Multi-band oscillator system with seven sensitivity levels
Directional volume flow analysis combining price movement and volume
Zero-line oscillation showing the balance between buying and selling pressure
Asymmetric ALMA smoothing for different sensitivity on positive/negative bands
Customizable lookback periods and multipliers for fine-tuning
Color-coded visualization for intuitive chart reading
Applications
This library offers developers a versatile foundation for creating volume-based indicators that go beyond simple volume measurement to reveal the directional force behind market movements. Ideal for confirming price trends, detecting divergences, identifying volume climaxes, and assessing overall market strength.
TradeTrackerLibrary "TradeTracker"
Simple Library for tracking trades
method track(this)
tracks trade when called on every bar
Namespace types: Trade
Parameters:
this (Trade) : Trade object
Returns: current Trade object
Trade
Has the constituents to track trades generated by any method.
Fields:
id (series int)
direction (series int) : Trade direction. Positive values for long and negative values for short trades
initialEntry (series float) : Initial entry price. This value will not change even if the entry is changed in the lifecycle of the trade
entry (series float) : Updated entry price. Allows variations to initial calculated entry. Useful in cases of trailing entry.
initialStop (series float) : Initial stop. Similar to initial entry, this is the first calculated stop for the lifecycle of trade.
stop (series float) : Trailing Stop. If there is no trailing, the value will be same as that of initial trade
targets (array) : array of target values.
startBar (series int) : bar index of starting bar. Set by default when object is created. No need to alter this after that.
endBar (series int) : bar index of last bar in trade. Set by tracker on each execution
startTime (series int) : time of the start bar. Set by default when object is created. No need to alter this after that.
endTime (series int) : time of the ending bar. Updated by tracking method.
status (series int) : Integer parameter to track the status of the trade
retest (series bool) : Boolean parameter to notify if there was retest of the entry price
tafirstlibGeneral Purpose: Starts by stating it's a collection of utility functions for technical analysis.
Core Functionality Areas: Mentions key categories like:
Extrema detection (isMin, isMax, etc.)
Condition checking over time (isMachedInRange, isContinuous, etc.)
Rate of change analysis (isSlowDown)
Moving average calculation (getMA)
Advanced Features: Highlights the more complex functions:
Visualization helpers (getColorNew)
Moving Regression (mr) for smoothing/trend
Cycle analysis (bpDom)
Overall Goal: Concludes by stating the library's aim – simplifying development and enabling complex analysis.
Library "tafirstlib"
TODO: add library description here
isSlowDown(data)
isSlowDown
Parameters:
data (float) : array of numbers
Returns: boolean
isMin(data, maeLength)
isMin
Parameters:
data (float) : array of numbers
maeLength (int) : number
Returns: boolean
isMax(data, maeLength)
isMax
Parameters:
data (float) : array of numbers
maeLength (int) : number
Returns: boolean
isMinStopped(data, maeLength)
isMinStopped
Parameters:
data (float) : array of numbers
maeLength (int) : number
Returns: boolean
isMaxStopped(data, maeLength)
isMaxStopped
Parameters:
data (float) : array of numbers
maeLength (int) : number
Returns: boolean
isLongMinStopped(data, maeLength, distance)
isLongMinStopped
Parameters:
data (float) : array of numbers
maeLength (int) : number
distance (int) : number
Returns: boolean
isLongMaxStopped(data, maeLength, distance)
isLongMaxStopped
Parameters:
data (float) : array of numbers
maeLength (int) : number
distance (int) : number
Returns: boolean
isMachedInRangeSkipCurrent(data, findRange, findValue)
isMachedInRangeSkipCurrent
Parameters:
data (bool) : array of numbers
findRange (int) : number
findValue (bool) : number
Returns: boolean
isMachedInRange(data, findRange, findValue)
isMachedInRange
Parameters:
data (bool) : array of numbers
findRange (int) : number
findValue (bool) : number
Returns: boolean
isMachedColorInRange(data, findRange, findValue)
isMachedColorInRange isMachedColorInRange(series color data, int findRange, color findValue)
Parameters:
data (color) : series of color
findRange (int) : int
findValue (color) : color
Returns: boolean
countMachedInRange(data, findRange, findValue)
countMachedInRange
Parameters:
data (bool) : array of numbers
findRange (int) : number
findValue (bool) : number
Returns: number
getColor(data)
getColor
Parameters:
data (float) : array of numbers
Returns: color
getColorNew(data)
getColorNew
Parameters:
data (float) : array of numbers
Returns: color
isColorBetter(color_data)
isColorBetter
Parameters:
color_data (color) : array of colors
Returns: boolean
isColorWorst(color_data)
isColorWorst
Parameters:
color_data (color) : array of colors
Returns: boolean
isColorBetter2(color_data)
isColorBetter2
Parameters:
color_data (color) : array of colors
Returns: boolean
isColorWorst2(color_data)
isColorWorst2
Parameters:
color_data (color) : array of colors
Returns: boolean
isDecreased2Bar(data)
isDecreased2Bar
Parameters:
data (float) : array of numbers
Returns: boolean
isContinuousAdvance(targetSeries, range2Find, howManyException)
isContinuousAdvance
Parameters:
targetSeries (bool) : array of booleans
range2Find (int) : number
howManyException (int) : number
Returns: boolean
isContinuous(targetSeries, range2Find, truefalse)
isContinuous
Parameters:
targetSeries (bool) : array of booleans
range2Find (int) : number
truefalse (bool) : boolean
Returns: boolean
isContinuousNotNow(targetSeries, range2Find, truefalse)
isContinuousNotNow
Parameters:
targetSeries (bool) : array of booleans
range2Find (int) : number
truefalse (bool) : boolean
Returns: boolean
isContinuousTwoFactors(targetSeries, range2Find, truefalse)
isContinuousTwoFactors
Parameters:
targetSeries (bool) : array of booleans
range2Find (int) : number
truefalse (bool) : boolean
Returns: boolean
findEventInRange(startDataBarIndex, neededDataBarIndex, currentBarIndex)
findEventInRange
Parameters:
startDataBarIndex (int) : number
neededDataBarIndex (int) : number
currentBarIndex (int) : number
Returns: boolean
findMin(firstdata, secondata, thirddata, forthdata)
findMin
Parameters:
firstdata (float) : number
secondata (float) : number
thirddata (float) : number
forthdata (float) : number
Returns: number
findMax(firstdata, secondata, thirddata, forthdata)
findMax
Parameters:
firstdata (float) : number
secondata (float) : number
thirddata (float) : number
forthdata (float) : number
Returns: number
getMA(src, length, mav)
getMA
Parameters:
src (float) : number
length (simple int) : number
mav (string) : string
Returns: number
mr(mrb_src, mrb_window, mrb_degree)
Parameters:
mrb_src (float)
mrb_window (int)
mrb_degree (int)
bpDom(maeLength, bpw, mult)
Parameters:
maeLength (int)
bpw (float)
mult (float)
FunctionSurvivalEstimationLibrary "FunctionSurvivalEstimation"
The Survival Estimation function, also known as Kaplan-Meier estimation or product-limit method, is a statistical technique used to estimate the survival probability of an individual over time. It's commonly used in medical research and epidemiology to analyze the survival rates of patients with different treatments, diseases, or risk factors.
What does it do?
The Survival Estimation function takes into account censored observations (i.e., individuals who are still alive at a certain point) and calculates the probability that an individual will survive beyond a specific time period. It's particularly useful when dealing with right-censoring, where some subjects are lost to follow-up or have not experienced the event of interest by the end of the study.
Interpretation
The Survival Estimation function provides a plot of the estimated survival probability over time, which can be used to:
1. Compare survival rates between different groups (e.g., treatment arms)
2. Identify patterns in the data that may indicate differences in mortality or disease progression
3. Make predictions about future outcomes based on historical data
4. In a trading context it may be used to ascertain the survival ratios of trading under specific conditions.
Reference:
www.global-developments.org
"Beyond GDP" ~ www.aeaweb.org
en.wikipedia.org
www.kdnuggets.com
survival_probability(alive_at_age, initial_alive)
Kaplan-Meier Survival Estimator.
Parameters:
alive_at_age (int) : The number of subjects still alive at a age.
initial_alive (int) : The Total number of initial subjects.
Returns: The probability that a subject lives longer than a certain age.
utility(c, l)
Captures the utility value from consumption and leisure.
Parameters:
c (float) : Consumption.
l (float) : Leisure.
Returns: Utility value from consumption and leisure.
welfare_utility(age, b, u, s)
Calculate the welfare utility value based age, basic needs and social interaction.
Parameters:
age (int) : Age of the subject.
b (float) : Value representing basic needs (food, shelter..).
u (float) : Value representing overall well-being and happiness.
s (float) : Value representing social interaction and connection with others.
Returns: Welfare utility value.
expected_lifetime_welfare(beta, consumption, leisure, alive_data, expectation)
Calculates the expected lifetime welfare of an individual based on their consumption, leisure, and survival probability over time.
Parameters:
beta (float) : Discount factor.
consumption (array) : List of consumption values at each step of the subjects life.
leisure (array) : List of leisure values at each step of the subjects life.
alive_data (array) : List of subjects alive at each age, the first element is the total or initial number of subjects.
expectation (float) : Optional, `defaut=1.0`. Expectation or weight given to this calculation.
Returns: Expected lifetime welfare value.
NR_VersatilitiesLibrary "NR_Versatilities"
Versatilities (aka, Versatile Utilities) includes:
- Seventeen Price Variants returned as a tuple,
- Eight Smoothing functions rolled into one,
- Pick any Past Value from any series with offset,
- Or just the previous value from any series.
pastVal(src, len)
Fetches past value from src that came len distance ago
Parameters:
src (float) : source series
len (int) : lookback distance - (optional) default is 1
Returns: latest src if len <= 0, else src
previous(src)
Fetches past value from src that came len distance ago
Parameters:
src (float) : source series
Returns: previous value in the series if found, else current value
price_variants()
Computes Several different averages using current and previous OHLC values
Returns: Seventeen Uncommon Average Price Combinations
dynamic_MA(matyp, masrc, malen, lsmaoff, almasgm, almaoff, almaflr)
Dynamically computes Eight different MAs on-demand individually, or an average of all taken together
Parameters:
matyp (string) : pick one of these MAs - ALMA, EMA, HMA, LSMA, RMA, SMA, SWMA, WMA, ALL
masrc (float) : source series to compute MA
malen (simple int) : lookback distance for MA
lsmaoff (simple int) : optional LSMA offset - default is 0
almasgm (simple float) : optional ALMA sigma - default is 5
almaoff (simple float) : optional ALMA offset - default is 0.5
almaflr (simple bool) : optional ALMA floor flag - default is false
Returns: MA series for chosen type or, an average of all of them, if chosen so
