EEI Strategy — Greedy/Guarded v1.2Purpose
Day‑trading strategy (5‑min focus) that hunts “armed” setups (PRE) and confirms them (GO) with greedy-but‑guarded execution. It adapts to symbol type, trend strength, and how long it’s been since the last signal.
Core signals & regime
Trend/Regime: EMA‑200 (intraday bias), VWAP, and a non‑repainting HTF EMA (via request.security(...) ).
Momentum/Structure: Manual Wilder DMI/ADX, micro‑ribbon (EMA 8/21), Bollinger‑Keltner squeeze + “squeeze fire,” BOS (break of swing high/low), pullback to band.
Liquidity/Vol: RVOL vs SMA(volume) + a latch (keeps eligibility a few bars after the first spike).
Volatility: ATR + ATR EMA (expansion).
PRE / GO engine
Score (0–100) aggregates trend, momentum, RVOL, squeeze, OBV slope, ribbon, pullback, BOS, and an Opening‑Range (OR) proximity penalty.
PRE arms when the adjusted score ≥ threshold and basic hygiene passes (ATR%, cooldown, etc.).
GO confirms within a dynamic window (1–3 bars):
Wick‑break mode on hot momentum (trend‑day / high ADX+RVOL): stop orders above/below the PRE high/low with a tick buffer.
Close‑through mode otherwise: close must push through PRE high/low plus ATR buffer.
Chase guard: entry cannot be too far from PRE price (ATR‑based), with a tiny extra allowance when the 8/21 ribbon aligns.
Multiple PREs per squeeze (capped) + per‑entry cooldown.
Adaptive behavior
Presets (Conservative/Balanced/Aggressive/Turbo) shift score/ADX/RVOL/ATR gates, GO window, cooldown, and max chase.
Profiles / Auto by Symbol:
Mega Trend (e.g., AMD/NVDA/TSLA/AAPL): looser chase, ATR stop, chandelier trail.
Mid Guarded (e.g., TTD/COIN/SOFI): swing stop, EMA trail, moderate gates.
Small Safe (e.g., BTAI/BBAI class): tighter gates, more guardrails.
BBAI micro‑override: easier arming (lower score/ADX/RVOL), multi‑PRE=3, swing stop + EMA trail, lighter OR penalty.
Trend‑day detector: if ADX hot + RVOL strong + ATR expanding + distance from day‑open large → GO window = 1 and wick‑break mode.
Mid‑day relaxers: mild score bonus between 10:30–14:30 to keep signals flowing in quieter tape.
Auto‑Relaxer (no‑signal fallback): after N bars without PRE/GO, gradually lowers score/ADX/RVOL/ATR% gates and raises max chase so the engine doesn’t stall on sleepy symbols.
Auto‑Session fallback: if RTH session isn’t detected (some tickers/premarket), it falls back to daily boundaries so Opening Range and day‑open logic still work.
Risk & exits
Initial stop per side chosen by ATR, Swing, or OR (computed every bar; no conditional calls).
Scaled targets: TP1/TP2 (R‑based) + runner with optional Chandelier or EMA trailing.
BE logic: optional move to breakeven after TP1; trailing can start after TP1 if configured.
Opening Range (OR)
Computes day open, OR high/low over configurable minutes; applies a penalty when entries are too close to OR boundary (lighter for small caps/BBAI). Protects against boundary whips.
Alerts & visuals
Alertconditions: PRE Long/Short Armed, GO Long/Short + explicit alert() calls for once‑per‑bar automation.
Plots: EMA‑200, HTF EMA, BB/KC bands, OR lines, squeeze shading, and PRE markers.
Why it’s robust
Non‑repainting HTF technique, all series precomputed every bar, no function calls hidden in conditionals that could break history dependence, and consistent state handling (var + sentinels).
Tuning cheat‑sheet (fast wins)
More trades: lower scoreBase, adxHot, or rvolMinBase a notch; reduce cooldownBase; increase maxPREperSqueeze.
Fewer whips: increase closeBufferATR, wickBufferTicks, or atrMinPct; reduce maxChaseATRBase.
Trend capture: use trailType="Chandelier", smaller trailLen, slightly larger trailMult; set preset="Aggressive".
Choppy names: prefer stopMode="Swing", enable EMA trail, keep OR penalty on.
Indicatori e strategie
Smart Money Breakout Signals [GILDEX]Introducing the Smart Money Breakout Signals, a cutting-edge trading indicator designed to identify key structural shifts and breakout opportunities in the market. This tool leverages a blend of smart money concepts like Break of Structure (BOS) and Change of Character (CHoCH) to provide traders with actionable insights into market direction and potential entry or exit points.
Key Features:
✨ Market Structure Analysis: Automatically detects and labels BOS and CHoCH for trend confirmation and reversals.
🎨 Customizable Visualization: Tailor bullish and bearish colors for breakout lines and signals to suit your preferences.
📊 Dynamic Take-Profit Targets: Displays three tiered take-profit levels based on breakout volatility.
🔔 Real-Time Alerts: Stay ahead of the game with notifications for bullish and bearish breakouts.
📋 Performance Dashboard: Monitor signal statistics, including win rates and total signals, directly on your chart.
How to Use:
Add the Indicator: Add the script to your favourites ⭐ and customize settings like market structure horizon and confirmation type.
QLitCycle QuarterlyQLITCYCLE
QLitCycle is an intraday cycle visualization tool that divides each trading day into multiple segments, helping traders identify time-based patterns and recurring market behaviors. By splitting the day into distinct periods, this indicator allows for better analysis of intraday rhythms, cycle alignment, and time-specific market tendencies.
It can be applied to various markets and timeframes, but is most effective on intraday charts where precise time segmentation can reveal valuable insights.
Andean • Dot Watcher (Exact Math + Optional Alerts)Title: Andean • Dot Watcher (1m + 1000T Alerts)
Description:
The Andean • Dot Watcher is a precision trend-detection tool that plots Bull and Bear “dot” signals for both the 1-minute chart and the 1000-tick chart — all in one indicator. It’s designed for traders who want early confirmation from tick data while also monitoring a traditional time-based chart for added confluence.
Key Features:
Dual-Timeframe Signals – Plots and alerts for both 1m and 1000T chart conditions.
Bull Dots – Green markers indicating bullish dominance or trigger events.
Bear Dots – Red markers indicating bearish dominance or trigger events.
Customizable Dot Mode – Choose between continuous dominance, flip-only signals, or crossover conditions.
Real-Time Alerts – Built-in TradingView alerts for:
1m Bull / 1m Bear signals
1000T Bull / 1000T Bear signals
Alert Flexibility – Users can set alerts for either timeframe independently or combine them for confirmation setups.
Usage Tips:
For fastest reaction, combine 1000T dots with 1-minute dots as a confirmation filter.
If your TradingView plan does not include tick charts, you can still use the 1-minute signals without issue.
Works best when combined with your existing trade plan for entries, exits, and risk management.
Requirements:
1-minute chart signals work on any TradingView plan (including Basic).
1000T tick chart signals require a TradingView plan that supports tick charts.
Kaos CHoCH M15 – Confirm + BOS H4 Bias (no repinta)Marca choch en dirección del Bias de H4 para seguir con la tendencia.
EDWARDS SQUEEZE 3MINUTE DOWSqueeze Momentum Strategy with EMA780 Trend Filter, ATR-SL, PT, EMA5 Exit Filter, and 3:57 PM Close
Golden Launch Pad🔰 Golden Launch Pad
This indicator identifies high-probability bullish setups by analyzing the relationship between multiple moving averages (MAs). A “Golden Launch Pad” is formed when the following five conditions are met simultaneously:
📌 Launch Pad Criteria (all must be true):
MAs Are Tightly Grouped
The selected MAs must be close together, measured using the Z-score spread — the difference between the highest and lowest Z-scores of the MAs.
Z-scores are calculated relative to the average and standard deviation of price over a user-defined window.
This normalizes MA distance based on volatility, making the signal adaptive across different assets.
MAs Are Bullishly Stacked
The MAs must be in strict ascending order: MA1 > MA2 > MA3 > ... > MA(n).
This ensures the short-term trend leads the longer-term trend — a classic sign of bullish structure.
All MAs Have Positive Slope
Each MA must be rising, based on a lookback period that is a percentage of its length (e.g. 30% of the MA’s bars).
This confirms momentum and avoids signals during sideways or weakening trends.
Price Is Above the Fastest MA
The current close must be higher than the first (fastest) moving average.
This adds a momentum filter and reduces false positives.
Price Is Near the MA Cluster
The current price must be close to the average of all selected MAs.
Proximity is measured in standard deviations (e.g. within 1.0), ensuring the price hasn't already made a large move away from the setup zone.
⚙️ Customization Options:
Use 2 to 6 MAs for the stack
Choose from SMA, EMA, WMA, VWMA for each MA
Adjustable Z-score window and spread threshold
Dynamic slope lookback based on MA length
Volatility-adjusted price proximity filter
🧠 Use Case:
This indicator helps traders visually and systematically detect strong continuation setups — often appearing before breakouts or sustained uptrends. It works well on intraday, swing, and positional timeframes across all asset classes.
For best results, combine with volume, breakout structure, or multi-timeframe confirmation.
New RSI📌 New RSI
The New RSI is a modern, enhanced version of the classic RSI created in 1978 — redesigned for today’s fast-moving markets, where algorithmic trading and AI dominate price action.
This indicator combines:
Adaptive RSI: Adjusts its calculation length in real time based on market volatility, making it more responsive during high volatility and smoother during calm periods.
Dynamic Bands: Upper and lower bands calculated from historical RSI volatility, helping you spot overbought/oversold conditions with greater accuracy.
Trend & Regime Filters: EMA and ADX-based detection to confirm signals only in favorable market conditions.
Volume Confirmation: Signals appear only when high trading volume supports the move — green volume for bullish setups and red volume for bearish setups — filtering out weak and unreliable trades.
💡 How it works:
A LONG signal appears when RSI crosses above the lower band and the volume is high with a bullish candle.
A SHORT signal appears when RSI crosses below the upper band and the volume is high with a bearish candle.
Trend and higher timeframe filters (optional) can help improve precision and adapt to different trading styles.
✅ Best Use Cases:
Identify high-probability reversals or pullbacks with strong momentum confirmation.
Avoid false signals by trading only when volume validates the move.
Combine with your own support/resistance or price action strategy for even higher accuracy.
⚙️ Fully Customizable:
Adjustable RSI settings (length, volatility adaptation, smoothing)
Dynamic band sensitivity
Volume threshold multiplier
Higher timeframe RSI filter
Color-coded background for market regime visualization
This is not just another RSI — it’s a complete, next-gen momentum tool designed for traders who want accuracy, adaptability, and confirmation in every signal.
WA-%Chg with BackgroundDescription
The WA-%Chg with Background indicator measures the percentage change in a selected price source over a user-defined period. It allows traders to visually and quickly assess bullish and bearish momentum through dynamic color coding and background shading.
Percentage Change Calculation – Uses ta.roc to determine the rate of change over the chosen length.
Customizable Alerts – Set upper (HiAlert) and lower (LoAlert) thresholds to get notified when momentum crosses bullish or bearish trigger levels.
Dynamic Line Coloring – Blue when above the bullish threshold, red when below the bearish threshold, and gray when in neutral territory.
Background Highlighting – Light blue shading for bullish zones, light red shading for bearish zones.
User Customization – Modify calculation length, colors, and alert thresholds to suit your trading style.
This tool is useful for identifying breakout conditions, momentum shifts, and potential reversals at a glance. Traders can combine it with other indicators for confirmation.
Disclaimer
This indicator is provided for educational purposes only and should not be considered financial advice. Past performance of any indicator or strategy is not indicative of future results. Trading in financial markets involves significant risk, including the risk of losing capital. Always perform your own analysis and consult with a qualified financial advisor before making any investment decisions. The author assumes no liability for any losses incurred from the use of this tool.
Contracts Calculator by NQLOGIEST🧮 Contracts Calculator by NQLOGIEST
This tool helps futures traders quickly calculate how many micro contracts to trade based on their dollar risk and stop size. It supports the following micro instruments:
MNQ – Micro Nasdaq 100
MES – Micro S&P 500
MCL – Micro Crude Oil Futures
MGC – Micro Gold Futures
🔧 Features:
Dynamic Contract Calculation based on:
Selected instrument
Dollar risk amount
Stop size (in points)
Instrument-aware $/point logic:
MNQ: $2/pt
MES: $5/pt
MCL: $1/pt
MGC: $1/pt
Customizable Table Position: Pin the results table to any corner of your chart.
Clean and lightweight — no chart clutter.
📋 How to Use:
Select the instrument you're trading from the dropdown (NQ, ES, CL, or GC).
Set your risk amount in dollars.
Set your stop loss size in points.
The indicator will calculate how many micro contracts you can trade while staying within your risk tolerance.
4 EMA Multi-Length / Abbas4 EMA Multi Length indicator
in case you need to make 4 different EMA/s for your chart
for swinging you'll need 50/100/150/200
for scalping perhaps 9/20/50
this indicator allows you to combine up to 4 EMAS in one indicator instead of 4 separate ones.
U Table • LITEA compact, educational version of my workflow that combines trend, momentum, trend strength, and a clean trigger:
Trend: EMA Fast vs EMA Slow (auto-lengths by chart TF)
Momentum: RSI > 50 for longs / < 50 for shorts
Strength: ADX above a user-set threshold (fallback implementation; can be replaced by ta.adx() when available)
Trigger: price crosses the Bollinger basis (center line)
Signals
LONG: crossover(close, BB basis) while EMA Fast > EMA Slow, RSI > 50, ADX > threshold
SHORT: crossunder(close, BB basis) while EMA Fast < EMA Slow, RSI < 50, ADX > threshold
Visuals
EMA Fast / EMA Slow / BB basis
Markers “L” / “S” on triggers
Latest confirmed pivot high/low (broken line style)
Small diagnostics table (ADX, EMA relation, RSI, last pivots) on the last bar
Inputs
Pivot length: pivot confirmation window (default 5)
ADX threshold: minimum trend strength to allow signals (default 20)
Notes
Signals are intended to be evaluated on bar close. Intrabar values may change until the bar closes.
Pivot lines appear after confirmation; they do not repaint once confirmed.
No external data or security() calls are used.
This LITE build focuses on clarity and speed (few calculations, overlay-friendly). It can be used as a stand-alone study or as a scaffold for your own research and risk management.
Smart Money Breakout Moving Strength [GILDEX]🟠OVERVIEW
This script draws breakout detection zones called “Smart Money Breakout Channels” based on volatility-normalized price movement and visualizes them as dynamic boxes with volume overlays. It identifies temporary accumulation or distribution ranges using a custom normalized volatility metric and tracks when price breaks out of those zones—either upward or downward. Each channel represents a structured range where smart money may be active, helping traders anticipate key breakouts with added context from volume delta, up/down volume, and a visual gradient gauge for momentum bias.
🟠CONCEPTS
The script calculates normalized price volatility by measuring the standard deviation of price mapped to a scale using the highest and lowest prices over a set lookback period. When normalized volatility reaches a local low and flips upward, a boxed channel is drawn between the highest and lowest prices in that zone. These boxes persist until price breaks out, either with a strong candle close (configurable) or by touching the boundary. Volume analysis enhances interpretation by rendering delta bars inside the box, showing volume distribution during the channel. Additionally, a real-time visual “gauge” shows where volume delta sits within the channel range, helping users spot pressure imbalances.
Smart Money Breakout Moving Strength [GILDEX]🟠OVERVIEW
This script draws breakout detection zones called “Smart Money Breakout Channels” based on volatility-normalized price movement and visualizes them as dynamic boxes with volume overlays. It identifies temporary accumulation or distribution ranges using a custom normalized volatility metric and tracks when price breaks out of those zones—either upward or downward. Each channel represents a structured range where smart money may be active, helping traders anticipate key breakouts with added context from volume delta, up/down volume, and a visual gradient gauge for momentum bias.
🟠CONCEPTS
The script calculates normalized price volatility by measuring the standard deviation of price mapped to a scale using the highest and lowest prices over a set lookback period. When normalized volatility reaches a local low and flips upward, a boxed channel is drawn between the highest and lowest prices in that zone. These boxes persist until price breaks out, either with a strong candle close (configurable) or by touching the boundary. Volume analysis enhances interpretation by rendering delta bars inside the box, showing volume distribution during the channel. Additionally, a real-time visual “gauge” shows where volume delta sits within the channel range, helping users spot pressure imbalances.
Extended AM Range w/ Breakout Markerscreates a range from market starting till 10 am (half an hour into the market
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)
Indicador Millo SMA20-SMA200-AO-RSI M1This indicator is designed for scalping in 1-minute timeframes on crypto pairs, combining trend direction, momentum, and oscillator confirmation.
Logic:
Trend Filter:
Only BUY signals when price is above the SMA200.
Only SELL signals when price is below the SMA200.
Entry Trigger:
BUY: Price crosses above the SMA20.
SELL: Price crosses below the SMA20.
Confirmation Window:
After the price cross, the Awesome Oscillator (AO) must cross the zero line in the same direction within a maximum of N bars (configurable, default = 4).
RSI must be > 50 for BUY and < 50 for SELL at the moment AO confirms.
Cooldown:
A cooldown period (configurable, default = 10 bars) prevents multiple signals of the same type in a short time, reducing noise in sideways markets.
Features:
Works on any crypto pair and can be used in other markets.
Adjustable confirmation window, RSI threshold, and cooldown.
Alerts ready for BUY and SELL conditions.
Can be converted into a strategy for backtesting with TP/SL.
Suggested Use:
Pair: BTC/USDT M1 or similar high-liquidity asset.
Combine with manual support/resistance or higher timeframe trend analysis.
Recommended to confirm entries visually and with additional confluence before trading live.
EMA Cross by TejasFor all Free Sub users. Feel free to use it everywhere. Mostly ASTA students. Very Eaasy to use with signals.
1-Hour Full-Width Transparent Candles (v6 - Final Fixed v2)its a one hour indicator showing the 1 5 15 30 min candles inside the 1hour candle, its useful to see both indicators on the same chart
Trading Sessionsconst string TZ_TOOLTIP_TEXT = "The session's time zone, specified in either GMT notation (e.g., 'GMT-5') or as an IANA time zone database name (e.g., 'America/New_York')."
