Join data and union of 2 hystorical markets
How to create a union from two contiguous Tradingview tickers (series)
Francesco Marzolo March 18, 2021
Go to the older ticker of the two, for example CME: SP1! and open it on Tradingview.
On the graph thus created, add this script.
In the indicator settings select the same ticker as the chart in Symbol1
while in Symbol2 the ticker from which to retrieve the most recent data, for example: SPX500
The operation this script does is examine each bar of the two tickers, where there is a value for the second it holds this one, where it does not exist in second ticker it keeps the value of the first one. This new series is called Merge. So now in the chart there will be 4 series:
- that of the original chart without script
- the same series loaded via script (Symbol1)
- series 2 of "new" data (Symbol2)
- the Merge series that "prefers" the Symbol2 data if present, otherwise it shows Symbol1
So now you have to change the visibility of the 4 series to see the differences:
- turn off the visibility of the chart indicator
- turn off the Symbol1 series in the script properties (old data only)
- switch off the Symbol2 series as well (only new data)
- switch on the Merge series (new data if existing, old if not present in the new ticker)
在腳本中搜尋"文华财经tick价格"
Compare (RSI) MACDHere I've created an indicator which can be used together with my "Compare (RSI) Ticker 3x" Indicator.
It makes it much easier to see the movements between the "RSI Ticker 1" and "RSI Ticker 2/3".
- The white line is the "MACD" of Ticker 1, which is the difference between the "RSI Ticker 1" and "RSI Ticker 2/3".
- The purple line is the "Signal" line, an EMA of the "MACD". (Length is adjustable)
- The "0-line" is the "RSI Ticker 2/3" line, when Ticker 2 is chosen, this will be blue coloured, when Ticker 3 is chosen it will be red.
Because 2 MACD in 1 indicator is way too messy, you only can choose the comparison against Ticker 2 OR Ticker 3.
- In "Settings" > "Inputs" you can enable/disable the second or third Ticker
(If Ticker 2 is enabled, Ticker 3 is disabled and vice versa)
- The second Ticker has multiple choices
- The third you can type any Ticker you want, for example CRYPTOCAP:BNB, BINANCE:ETHUSDT, NASDAQ_DLY:NDX or whatever,
just start typing and you'll see the possibilities (You also can choose between "Cryptocurrencies", "Index", "Forex", ...)
- When the "MACD" crosses the "0-line", arrows will appear, white ones for "MACD", purple ones for the "Signal" line.
- The "Histogram" makes it easier to see the difference between "MACD" and "Signal" line.
- The source of this indicator is adjustable
- When the second chosen Ticker is the same as the first Ticker, of course you will be seeing lines
(because there is no difference between the 2 Tickers, the EMA is visible though)
If you use both "Compare (RSI) Ticker 3x" AND "Compare (RSI) MACD", of course be aware that you have the same Ticker 2 or 3 in each indicator!
LibWghtLibrary "LibWght"
This is a library of mathematical and statistical functions
designed for quantitative analysis in Pine Script. Its core
principle is the integration of a custom weighting series
(e.g., volume) into a wide array of standard technical
analysis calculations.
Key Capabilities:
1. **Universal Weighting:** All exported functions accept a `weight`
parameter. This allows standard calculations (like moving
averages, RSI, and standard deviation) to be influenced by an
external data series, such as volume or tick count.
2. **Weighted Averages and Indicators:** Includes a comprehensive
collection of weighted functions:
- **Moving Averages:** `wSma`, `wEma`, `wWma`, `wRma` (Wilder's),
`wHma` (Hull), and `wLSma` (Least Squares / Linear Regression).
- **Oscillators & Ranges:** `wRsi`, `wAtr` (Average True Range),
`wTr` (True Range), and `wR` (High-Low Range).
3. **Volatility Decomposition:** Provides functions to decompose
total variance into distinct components for market analysis.
- **Two-Way Decomposition (`wTotVar`):** Separates variance into
**between-bar** (directional) and **within-bar** (noise)
components.
- **Three-Way Decomposition (`wLRTotVar`):** Decomposes variance
relative to a linear regression into **Trend** (explained by
the LR slope), **Residual** (mean-reversion around the
LR line), and **Within-Bar** (noise) components.
- **Local Volatility (`wLRLocTotStdDev`):** Measures the total
"noise" (within-bar + residual) around the trend line.
4. **Weighted Statistics and Regression:** Provides a robust
function for Weighted Linear Regression (`wLinReg`) and a
full suite of related statistical measures:
- **Between-Bar Stats:** `wBtwVar`, `wBtwStdDev`, `wBtwStdErr`.
- **Residual Stats:** `wResVar`, `wResStdDev`, `wResStdErr`.
5. **Fallback Mechanism:** All functions are designed for reliability.
If the total weight over the lookback period is zero (e.g., in
a no-volume period), the algorithms automatically fall back to
their unweighted, uniform-weight equivalents (e.g., `wSma`
becomes a standard `ta.sma`), preventing errors and ensuring
continuous calculation.
---
**DISCLAIMER**
This library is provided "AS IS" and for informational and
educational purposes only. It does not constitute financial,
investment, or trading advice.
The author assumes no liability for any errors, inaccuracies,
or omissions in the code. Using this library to build
trading indicators or strategies is entirely at your own risk.
As a developer using this library, you are solely responsible
for the rigorous testing, validation, and performance of any
scripts you create based on these functions. The author shall
not be held liable for any financial losses incurred directly
or indirectly from the use of this library or any scripts
derived from it.
wSma(source, weight, length)
Weighted Simple Moving Average (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
the arithmetic mean if Σweight = 0.
wEma(source, weight, length)
Weighted EMA (exponential kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Exponential-kernel weighted mean; falls
back to classic EMA if Σweight = 0.
wWma(source, weight, length)
Weighted WMA (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
classic WMA if Σweight = 0.
wRma(source, weight, length)
Weighted RMA (Wilder kernel, α = 1/len).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Wilder-kernel weighted mean; falls back to
classic RMA if Σweight = 0.
wHma(source, weight, length)
Weighted HMA (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
classic HMA if Σweight = 0.
wRsi(source, weight, length)
Weighted Relative Strength Index.
Parameters:
source (float) : series float Price series.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Weighted RSI; uniform if Σw = 0.
wAtr(tr, weight, length)
Weighted ATR (Average True Range).
Implemented as WRMA on *true range*.
Parameters:
tr (float) : series float True Range series.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Weighted ATR; uniform weights if Σw = 0.
wTr(tr, weight, length)
Weighted True Range over a window.
Parameters:
tr (float) : series float True Range series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Weighted mean of TR; uniform if Σw = 0.
wR(r, weight, length)
Weighted High-Low Range over a window.
Parameters:
r (float) : series float High-Low per bar.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Weighted mean of range; uniform if Σw = 0.
wBtwVar(source, weight, length, biased)
Weighted Between Variance (biased/unbiased).
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns:
variance series float The calculated between-bar variance (σ²btw), either biased or unbiased.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wBtwStdDev(source, weight, length, biased)
Weighted Between Standard Deviation.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σbtw uniform if Σw = 0.
wBtwStdErr(source, weight, length, biased)
Weighted Between Standard Error.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²btw / N_eff) uniform if Σw = 0.
wTotVar(mu, sigma, weight, length, biased)
Weighted Total Variance (= between-group + within-group).
Useful when each bar represents an aggregate with its own
mean* and pre-estimated σ (e.g., second-level ranges inside a
1-minute bar). Assumes the *weight* series applies to both the
group means and their σ estimates.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns:
varBtw series float The between-bar variance component (σ²btw).
varWtn series float The within-bar variance component (σ²wtn).
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wTotStdDev(mu, sigma, weight, length, biased)
Weighted Total Standard Deviation.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σtot.
wTotStdErr(mu, sigma, weight, length, biased)
Weighted Total Standard Error.
SE = √( total variance / N_eff ) with the same effective sample
size logic as `wster()`.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²tot / N_eff).
wLinReg(source, weight, length)
Weighted Linear Regression.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
Returns:
mid series float The estimated value of the regression line at the most recent bar.
slope series float The slope of the regression line.
intercept series float The intercept of the regression line.
wResVar(source, weight, midLine, slope, length, biased)
Weighted Residual Variance.
linear regression – optionally biased (population) or
unbiased (sample).
Parameters:
source (float) : series float Data series.
weight (float) : series float Weighting series (volume, etc.).
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population variance (σ²_P), denominator ≈ N_eff.
false → sample variance (σ²_S), denominator ≈ N_eff - 2.
(Adjusts for 2 degrees of freedom lost to the regression).
Returns:
variance series float The calculated residual variance (σ²res), either biased or unbiased.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wResStdDev(source, weight, midLine, slope, length, biased)
Weighted Residual Standard Deviation.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σres; uniform if Σw = 0.
wResStdErr(source, weight, midLine, slope, length, biased)
Weighted Residual Standard Error.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²res / N_eff); uniform if Σw = 0.
wLRTotVar(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Variance **around the
window’s weighted mean μ**.
σ²_tot = E_w ⟶ *within-group variance*
+ Var_w ⟶ *residual variance*
+ Var_w ⟶ *trend variance*
where each bar i in the look-back window contributes
m_i = *mean* (e.g. 1-sec HL2)
σ_i = *sigma* (pre-estimated intrabar σ)
w_i = *weight* (volume, ticks, …)
ŷ_i = b₀ + b₁·x (value of the weighted LR line)
r_i = m_i − ŷ_i (orthogonal residual)
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns:
varRes series float The residual variance component (σ²res).
varWtn series float The within-bar variance component (σ²wtn).
varTrd series float The trend variance component (σ²trd), explained by the linear regression.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wLRTotStdDev(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Standard Deviation.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √(σ²tot).
wLRTotStdErr(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Standard Error.
SE = √( σ²_tot / N_eff ) with N_eff = Σw² / Σw² (like in wster()).
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √((σ²res, σ²wtn, σ²trd) / N_eff).
wLRLocTotStdDev(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Local Total Standard Deviation.
Measures the total "noise" (within-bar + residual) around the trend.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √(σ²wtn + σ²res).
wLRLocTotStdErr(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Local Total Standard Error.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √((σ²wtn + σ²res) / N_eff).
wLSma(source, weight, length)
Weighted Least Square Moving Average.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
Returns: series float Least square weighted mean. Falls back
to unweighted regression if Σw = 0.
Serenity Model VIPI — by yuu_iuHere’s a concise, practical English guide for Serenity Model VIPI (Author: yuu_iu). It covers what it is, how to set it up for daily trading, how to tune it, and how we guarantee non-repainting.
Serenity Model VIPI — User Guide (Daily Close, Non‑Repainting)
Credits
- Author: yuu_iu
- Producer: yuu_iu
- Platform: TradingView (Pine Script v5)
1) What it is
Serenity Model VIPI is a multi‑module, context‑aware trading model that fuses signals from:
- Entry modules: VCP, Flow, Momentum, Mean Reversion, Breakout
- Exit/risk modules: Contrarian, Breakout Sell, Volume Delta Sell, Peak Detector, Overbought Exit, Profit‑Take
- Context/memory: Learns per Ticker/Sector/Market Regime and adjusts weights/aggression
- Learning engine: Runs short “fake trades” to learn safely before scaling real trades
It produces a weighted, context‑adjusted score and a final decision: BUY, SELL, TAKE_PROFIT, or WAIT.
2) How it works (high level)
- Each module computes a score per bar.
- A fusion layer combines module scores using accuracy and base weights, then adjusts by:
- Market regime (Bull/Bear/Sideways) and optional higher‑timeframe (HTF) bias
- Risk control neuron
- Context memory (ticker/sector/regime)
- Optional LLM mode can override marginal cases if context supports it.
- Final decision is taken at bar close only (no intrabar repaint).
3) Non‑repainting guarantee (Daily)
- Close‑only execution: All key actions use barstate.isconfirmed, so signals/entries/exits only finalize after the daily candle closes.
- No lookahead on HTF data: request.security() reads prior‑bar values (series ) for HTF close/EMA/RSI.
- Alerts at bar close: Alerts are fired once per bar close to prevent mid‑bar changes.
What this means: Once the daily bar closes, the decision and alert won’t be repainted.
4) Setup (TradingView)
- Paste the Pine v5 code into Pine Editor, click Add to chart.
- Timeframe: 1D (Daily).
- Optional: enable a date window for training/backtest
- Enable Custom Date Filter: ON
- Set Start Date / End Date
- Create alert (non‑repainting)
- Condition: AI TRADE Signal
- Options: Once Per Bar Close
- Webhook (optional): Paste your URL into “System Webhook URL (for AI events)”
- Watch the UI
- On‑chart markers: AI BUY / AI SELL / AI TAKE PROFIT
- Right‑side table: Trades, Win Rate, Avg Profit, module accuracies, memory source, HTF trend, etc.
- “AI Thoughts” label: brief reasoning and debug lines.
5) Daily trading workflow
- The model evaluates at daily close and may:
- Enter long (BUY) when buy votes + total score exceed thresholds, after context/risk checks
- Exit via trailing stop, hard stop, TAKE_PROFIT, or SELL decision
- Learning mode:
- Triggers short “fake trades” every N bars (default 3) and measures outcome after 5 bars
- Improves module accuracies and adjusts aggression once stable (min fake win% threshold)
- Memory application:
- When you change tickers, the model tries to apply Ticker or Sector memory for the current market regime to pre‑bias module weights/aggression.
6) Tuning (what to adjust and why)
Core controls
- Base Aggression Level (default 1.0): Higher = more trades and stronger decisions; start conservative on Daily (1.0–1.2).
- Learning Speed Multiplier (default 3): Faster adaptation after fake/real trades; too high can overreact.
- Min Fake Win Rate to Exit Learning (%) (default 10–20%): Raises the bar before trusting more real trades.
- Fake Trade Every N Bars (default 3): Frequency of learning attempts.
- Learning Threshold Win Rate (default 0.4): Governs when the learner should keep learning.
- Hard Stop Loss (%) (default 5–8%): Global emergency stop.
Multi‑Timeframe (MTF)
- Enable Multi‑Timeframe Confirmation: ON (recommended for Daily)
- HTF Trend Source: HOSE:VNINDEX for VN equities (or CURRENT_SYMBOL if you prefer)
- HTF Timeframe: D or 240 (for a strong bias)
- MTF Weight Adjustment: 0.2–0.4 (0.3 default is balanced)
Module toggles and base weights
- In strong uptrends: increase VCP, Momentum, Breakout (0.2–0.3 typical)
- In sideways low‑vol regimes: raise MeanRev (0.2–0.3)
- For exits/defense: Contrarian, Peak, Overbought Exit, Profit‑Take (0.1–0.2 each)
- Keep Flow on as a volume‑quality filter (≈0.2)
Memory and control
- Enable Shared Memory Across Tickers: ON to share learning
- Enable Sector‑Based Knowledge Transfer: ON to inherit sector tendencies
- Manual Reset Learning: Use sparingly to reset module accuracies if regime changes drastically
Risk management
- Hard Stop Loss (%): 5–8% typical on Daily
- Trailing Stop: ATR‑ and volatility‑adaptive; tightens faster in Bear/High‑Vol regimes
- Max hold bars: Shorter in Bear or Sideways High‑Vol to cut risk
Alerts and webhook
- Use AI TRADE Signal with Once Per Bar Close
- Webhook payload is JSON, including event type, symbol, time, win rates, equity, aggression, etc.
7) Recommended Daily preset (VN equities)
- MTF: Enable, Source: HOSE:VNINDEX, TF: D, Weight Adj: 0.3
- Aggression: 1.1
- Learning Speed: 3
- Min Fake Win Rate to Exit Learning: 15%
- Hard SL: 6%
- Base Weights:
- VCP 0.25, Momentum 0.25, Breakout 0.15, Flow 0.20
- MeanRev 0.20 (raise in sideways)
- Contrarian/Peak/Overbought/Profit‑Take: 0.10–0.20
- Leave other defaults as is, then fine‑tune by symbol/sector.
8) Reading the UI
- Table highlights: Real Trades, Win Rate, Avg Profit, Fake Actions/Win%, VCP Acc, Aggression, Equity, Score, Status (LEARNING/TRADING/REFLECTION), Last Real, Consec Loss, Best/Worst Trade, Pattern Score, Memory Source, Current Sector, AI Health, HTF Trend, Scheduler, Memory Loaded, Fake Active.
- Shapes: AI BUY (below bar), AI SELL/TAKE PROFIT (above bar)
- “AI Thoughts”: module contributions, context notes, debug lines
9) Troubleshooting
- No trades?
- Ensure timeframe is 1D and the date filter covers the chart range
- Check Scheduler Cooldown (3 bars default) and that barstate.isconfirmed (only at close)
- If MTF is ON and HTF is bearish, buy bias is reduced; relax MTF Weight Adjustment or module weights
- Too many/too few trades?
- Lower/raise Base Aggression Level
- Adjust base weights on key modules (raise entry modules to be more active; raise exit/defense modules to be more selective)
- Learning doesn’t end?
- Increase Min Fake Win Rate to Exit Learning only after it’s consistently stable; otherwise lower it or reduce Fake Trade Every N Bars
10) Important notes
- The strategy is non‑repainting at bar close by design (confirmed bars + HTF series + close‑only alerts).
- Backtest fills may differ from live fills due to slippage and broker rules; this is normal for all TradingView strategies.
- Always validate settings across multiple symbols and regimes before going live.
If you want, I can bundle this guide into a README section in your Pine code and add a small on‑chart signature (Author/Producer: yuu_iu) in the top‑right corner.
ICT First Presented FVG with Volume Imbalance [1st P. FVG + VI]The indicator identifies and highlights the first presented Fair Value Gap (FVG) occurringthe morning (09:30–10:00) and afternoon (13:30–14:00) session's first 30 minutes. It includes an optional feature to extend FVG zones when a volume imbalance (V.I.) is detected, providing additional context for areas of potential price inefficiency. This powerful combination helps traders identify significant market structure gaps that often act as support/resistance zones and potential price targets.
What is an FVG?
A Fair Value Gap, often abbreviated as FVG, is a price range on a chart where there is an inefficiency or imbalance in trading. This typically happens when price moves rapidly in one direction, leaving a gap between the wicks or bodies of three consecutive candles. For example, in a bullish move, if the low of the third candle is higher than the high of the first candle, the space between them is the FVG.
What is a Volume Imbalance?
A volume imbalance is a smaller, more precise inefficiency within price action, often visible as a "crack" or thin area in the price delivery. It represents a spot where the volume traded was not balanced between buyers and sellers, often seen as a thin wick or a gap between candle bodies.
FVG + Volume Imbalance:
When you have a fair value gap that contains a volume imbalance, it becomes a more significant area of interest. ICT teaches that you should not ignore a volume imbalance if it’s part of an FVG. In fact, you should use the volume imbalance in conjunction with the FVG to define your trading range more accurately
📊 Volume Imbalance Integration
Toggle Option: Enable/disable volume imbalance detection based on preference
Extended Boundaries: When enabled, FVG boundaries expand to include volume imbalance zones
Accurate Gap Sizing: Total gap calculation includes volume imbalance extensions
Multi-Scenario Support: Handles volume imbalances at start, end, or both sides of FVG formations
📈 Multiple Display Modes
Current Day: Shows only today's FVGs for clean chart analysis
Current Week: Displays all weekly FVGs for broader context
Forward Extension: Extends FVG boxes and CE, Upper/Lower Quadrant lines into the future
📊 Visualization
Bullish FVGs appear in semi-transparent blue or purple zones (depending on session).
Bearish FVGs appear in red or orange zones.
Optional dotted lines mark the CE (midpoint) of each FVG for additional reference.
Quadrant Division: Additional 25%/75% lines for large FVGs (configurable minimum gap size)
🎯 Smart Filtering
First Presentation Only: Only displays the initial FVG in each session, avoiding clutter
Minimum Gap Size: Configurable tick-based thresholds for AM and PM sessions
Core FVG Validation: Ensures only valid Fair Value Gaps are displayed
⚙️ Configuration Options
Display Settings
Show Mode: Current Day or Current Week view
Forward Extension: 1-500 bars projection
Day Labels: Toggle weekday labels in weekly mode
Text Color: Customizable label colors
Volume Imbalance Settings
Include Volume Imbalance: Master toggle for enhanced boundary calculation
Automatic Detection: Identifies imbalance scenarios without additional input
Session-Specific Settings
AM Session (09:30-10:00):
Enable/disable AM FVG detection
Customizable bullish/bearish colors
CE line visibility and coloring
Minimum gap size in ticks
PM Session (13:30-14:00):
Enable/disable PM FVG detection
Customizable bullish/bearish colors
CE line visibility and coloring
Minimum gap size in ticks
Quadrant Settings
Enable/Disable: Toggle quadrant line display
Minimum Gap: Tick threshold for quadrant activation
Line Style: Dotted, dashed, or solid
Color: Customizable quadrant line color
How It Works
FVG Boundary Calculation
Traditional FVG: High to Low (bullish) or Low to High (bearish)
Enhanced FVG: Extended boundaries to include volume imbalance zones when enabled
Total Gap Size: Calculated including any volume imbalance extensions
Volume Imbalance Detection
The indicator identifies volume imbalances by detecting bars where:
Bullish Imbalance: Current bar's body is completely above previous bar's body
Bearish Imbalance: Current bar's body is completely below previous bar's body
⚠️ Disclaimer
This script is a technical visualization tool only.
It does not provide financial advice, signals, or predictions. Always perform independent analysis and manage risk appropriately before making trading decisions.
Quantum Flux Universal Strategy Summary in one paragraph
Quantum Flux Universal is a regime switching strategy for stocks, ETFs, index futures, major FX pairs, and liquid crypto on intraday and swing timeframes. It helps you act only when the normalized core signal and its guide agree on direction. It is original because the engine fuses three adaptive drivers into the smoothing gains itself. Directional intensity is measured with binary entropy, path efficiency shapes trend quality, and a volatility squash preserves contrast. Add it to a clean chart, watch the polarity lane and background, and trade from positive or negative alignment. For conservative workflows use on bar close in the alert settings when you add alerts in a later version.
Scope and intent
• Markets. Large cap equities and ETFs. Index futures. Major FX pairs. Liquid crypto
• Timeframes. One minute to daily
• Default demo used in the publication. QQQ on one hour
• Purpose. Provide a robust and portable way to detect when momentum and confirmation align, while dampening chop and preserving turns
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique concept or fusion. The novelty sits in the gain map. Instead of gating separate indicators, the model mixes three drivers into the adaptive gains that power two one pole filters. Directional entropy measures how one sided recent movement has been. Kaufman style path efficiency scores how direct the path has been. A volatility squash stabilizes step size. The drivers are blended into the gains with visible inputs for strength, windows, and clamps.
• What failure mode it addresses. False starts in chop and whipsaw after fast spikes. Efficiency and the squash reduce over reaction in noise.
• Testability. Every component has an input. You can lengthen or shorten each window and change the normalization mode. The polarity plot and background provide a direct readout of state.
• Portable yardstick. The core is normalized with three options. Z score, percent rank mapped to a symmetric range, and MAD based Z score. Clamp bounds define the effective unit so context transfers across symbols.
Method overview in plain language
The strategy computes two smoothed tracks from the chart price source. The fast track and the slow track use gains that are not fixed. Each gain is modulated by three drivers. A driver for directional intensity, a driver for path efficiency, and a driver for volatility. The difference between the fast and the slow tracks forms the raw flux. A small phase assist reduces lag by subtracting a portion of the delayed value. The flux is then normalized. A guide line is an EMA of a small lead on the flux. When the flux and its guide are both above zero, the polarity is positive. When both are below zero, the polarity is negative. Polarity changes create the trade direction.
Base measures
• Return basis. The step is the change in the chosen price source. Its absolute value feeds the volatility estimate. Mean absolute step over the window gives a stable scale.
• Efficiency basis. The ratio of net move to the sum of absolute step over the window gives a value between zero and one. High values mean trend quality. Low values mean chop.
• Intensity basis. The fraction of up moves over the window plugs into binary entropy. Intensity is one minus entropy, which maps to zero in uncertainty and one in very one sided moves.
Components
• Directional Intensity. Measures how one sided recent bars have been. Smoothed with RMA. More intensity increases the gain and makes the fast and slow tracks react sooner.
• Path Efficiency. Measures the straightness of the price path. A gamma input shapes the curve so you can make trend quality count more or less. Higher efficiency lifts the gain in clean trends.
• Volatility Squash. Normalizes the absolute step with Z score then pushes it through an arctangent squash. This caps the effect of spikes so they do not dominate the response.
• Normalizer. Three modes. Z score for familiar units, percent rank for a robust monotone map to a symmetric range, and MAD based Z for outlier resistance.
• Guide Line. EMA of the flux with a small lead term that counteracts lag without heavy overshoot.
Fusion rule
• Weighted sum of the three drivers with fixed weights visible in the code comments. Intensity has fifty percent weight. Efficiency thirty percent. Volatility twenty percent.
• The blend power input scales the driver mix. Zero means fixed spans. One means full driver control.
• Minimum and maximum gain clamps bound the adaptive gain. This protects stability in quiet or violent regimes.
Signal rule
• Long suggestion appears when flux and guide are both above zero. That sets polarity to plus one.
• Short suggestion appears when flux and guide are both below zero. That sets polarity to minus one.
• When polarity flips from plus to minus, the strategy closes any long and enters a short.
• When flux crosses above the guide, the strategy closes any short.
What you will see on the chart
• White polarity plot around the zero line
• A dotted reference line at zero named Zen
• Green background tint for positive polarity and red background tint for negative polarity
• Strategy long and short markers placed by the TradingView engine at entry and at close conditions
• No table in this version to keep the visual clean and portable
Inputs with guidance
Setup
• Price source. Default ohlc4. Stable for noisy symbols.
• Fast span. Typical range 6 to 24. Raising it slows the fast track and can reduce churn. Lowering it makes entries more reactive.
• Slow span. Typical range 20 to 60. Raising it lengthens the baseline horizon. Lowering it brings the slow track closer to price.
Logic
• Guide span. Typical range 4 to 12. A small guide smooths without eating turns.
• Blend power. Typical range 0.25 to 0.85. Raising it lets the drivers modulate gains more. Lowering it pushes behavior toward fixed EMA style smoothing.
• Vol window. Typical range 20 to 80. Larger values calm the volatility driver. Smaller values adapt faster in intraday work.
• Efficiency window. Typical range 10 to 60. Larger values focus on smoother trends. Smaller values react faster but accept more noise.
• Efficiency gamma. Typical range 0.8 to 2.0. Above one increases contrast between clean trends and chop. Below one flattens the curve.
• Min alpha multiplier. Typical range 0.30 to 0.80. Lower values increase smoothing when the mix is weak.
• Max alpha multiplier. Typical range 1.2 to 3.0. Higher values shorten smoothing when the mix is strong.
• Normalization window. Typical range 100 to 300. Larger values reduce drift in the baseline.
• Normalization mode. Z score, percent rank, or MAD Z. Use MAD Z for outlier heavy symbols.
• Clamp level. Typical range 2.0 to 4.0. Lower clamps reduce the influence of extreme runs.
Filters
• Efficiency filter is implicit in the gain map. Raising efficiency gamma and the efficiency window increases the preference for clean trends.
• Micro versus macro relation is handled by the fast and slow spans. Increase separation for swing, reduce for scalping.
• Location filter is not included in v1.0. If you need distance gates from a reference such as VWAP or a moving mean, add them before publication of a new version.
Alerts
• This version does not include alertcondition lines to keep the core minimal. If you prefer alerts, add names Long Polarity Up, Short Polarity Down, Exit Short on Flux Cross Up in a later version and select on bar close for conservative workflows.
Strategy has been currently adapted for the QQQ asset with 30/60min timeframe.
For other assets may require new optimization
Properties visible in this publication
• Initial capital 25000
• Base currency Default
• Default order size method percent of equity with value 5
• Pyramiding 1
• Commission 0.05 percent
• Slippage 10 ticks
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Honest limitations and failure modes
• Past results do not guarantee future outcomes
• Economic releases, circuit breakers, and thin books can break the assumptions behind intensity and efficiency
• Gap heavy symbols may benefit from the MAD Z normalization
• Very quiet regimes can reduce signal contrast. Use longer windows or higher guide span to stabilize context
• Session time is the exchange time of the chart
• If both stop and target can be hit in one bar, tie handling would matter. This strategy has no fixed stops or targets. It uses polarity flips for exits. If you add stops later, declare the preference
Open source reuse and credits
• None beyond public domain building blocks and Pine built ins such as EMA, SMA, standard deviation, RMA, and percent rank
• Method and fusion are original in construction and disclosure
Legal
Education and research only. Not investment advice. You are responsible for your decisions. Test on historical data and in simulation before any live use. Use realistic costs.
Strategy add on block
Strategy notice
Orders are simulated by the TradingView engine on standard candles. No request.security() calls are used.
Entries and exits
• Entry logic. Enter long when both the normalized flux and its guide line are above zero. Enter short when both are below zero
• Exit logic. When polarity flips from plus to minus, close any long and open a short. When the flux crosses above the guide line, close any short
• Risk model. No initial stop or target in v1.0. The model is a regime flipper. You can add a stop or trail in later versions if needed
• Tie handling. Not applicable in this version because there are no fixed stops or targets
Position sizing
• Percent of equity in the Properties panel. Five percent is the default for examples. Risk per trade should not exceed five to ten percent of equity. One to two percent is a common choice
Properties used on the published chart
• Initial capital 25000
• Base currency Default
• Default order size percent of equity with value 5
• Pyramiding 1
• Commission 0.05 percent
• Slippage 10 ticks
• Process orders on close ON
• Bar magnifier ON
• Recalculate after order is filled OFF
• Calc on every tick OFF
Dataset and sample size
• Test window Jan 2, 2014 to Oct 16, 2025 on QQQ one hour
• Trade count in sample 324 on the example chart
Release notes template for future updates
Version 1.1.
• Add alertcondition lines for long, short, and exit short
• Add optional table with component readouts
• Add optional stop model with a distance unit expressed as ATR or a percent of price
Notes. Backward compatibility Yes. Inputs migrated Yes.
Untouched ExtremesWhat it is
Untouched Extremes plots horizontal levels at green-candle highs and red-candle lows. Each level is considered “untouched” (clean liquidity) until price revisits it; on the first valid touch the line auto-deletes, keeping only live targets on your chart.
How it works (logic)
Bar close event
If close > open, the script draws a line at that bar’s high and extends it to the right.
If close < open, it draws a line at that bar’s low and extends it to the right.
(Optional) Perfect/almost-dojis can be classified as green or red via settings.
Touch & removal
A green-high line is removed when any later bar’s high ≥ level (optionally within a tick tolerance).
A red-low line is removed when any later bar’s low ≤ level (optionally within a tick tolerance).
You can delay deletion by N bars to make the touch visible before the line disappears.
Housekeeping
Maximum active lines per side and line styling are user-configurable.
Why it’s useful
Untouched highs/lows often coincide with resting liquidity and incomplete price probes. Tracking them helps:
Define targets and magnets price may seek.
Frame mean-reversion rotations after a failed push.
Keep the chart clean: only levels that have not been traded are displayed.
How to use it (trading idea)
Confirmation rule: Treat the line as a level/zone. Price can pierce it; wait for a clear reversal candle pattern (e.g., pin bar, engulfing, strong momentum shift) at or immediately after the touch.
Directional play:
If a bullish reversal pattern forms at/around a red-low line, the working assumption is that price will move toward the first untouched upper line (nearest green-high line above). Many traders use that as the primary target.
Conversely, if a bearish reversal pattern forms at/around a green-high line, expect rotation toward the first untouched lower line.
Risk management: Stops typically go just beyond the level or beyond the pattern’s wick. Consider a fixed R:R (e.g., 1:2) and partials at intermediate levels.
Settings
Doji handling: Choose how to classify close ≈ open bars (Green / Red / Ignore). A small equality margin (ticks) helps with rounding on some symbols.
Touch tolerance (ticks): Counts near-misses as touches if desired.
Deletion delay (bars): Wait N bars after creation before a line becomes eligible for deletion.
Max lines per side / width / colors: Keep the view readable.
Tips
Works on any symbol/timeframe; lower TFs produce more levels—adjust Max lines accordingly.
Combining with a trend filter (e.g., EMA-200), ATR distance, or volume clues can improve selectivity.
If spreads or wicks are noisy, increase tolerance slightly and/or use deletion delay to visualize touches.
Note: This tool provides structure and potential targets, not signals by itself. Always require your reversal pattern as confirmation and manage risk appropriately.
Range TableThe Range Table indicator calculates and displays the Daily Average True Range (ATR), the current day's True Range (TR), and two customizable ATR percentage values in a clean table format. It provides values in ticks, points, and USD, helping traders set stop-loss buffers based on market volatility.
**Features:**
- Displays the Daily ATR (14-period) and current day's True Range (TR) with its percentage of the Daily ATR.
- Includes two customizable ATR percentages (default: 75% and 10%, with the second disabled by default).
- Shows values in ticks, points, and USD based on the symbol's tick size and point value.
- Customizable table position, background color, text color, and font size.
- Toggle visibility for the table and percentage rows via input settings.
**How to Use:**
1. Add the indicator to your chart.
2. Adjust the table position, colors, and font size in the input settings.
3. Enable or disable the 75% and 10% ATR rows or customize their percentages.
4. Use the displayed values to set stop-loss or take-profit levels based on volatility.
**Ideal For:**
- Day traders and swing traders looking to set volatility-based stop-losses.
- Users analyzing tick, point, and USD-based risk metrics.
**Notes:**
- Ensure your chart is set to a timeframe that aligns with the daily ATR calculations.
- USD values are approximate if `syminfo.pointvalue` is unavailable.
Developed by FlyingSeaHorse.
Tuga SupertrendDescription
This strategy uses the Supertrend indicator enhanced with commission and slippage filters to capture trends on the daily chart. It’s designed to work on any asset but is especially effective in markets with consistent movements.
Use the date inputs to set the backtest period (default: from January 1, 2018, through today, June 30, 2025).
The default input values are optimized for the daily chart. For other timeframes, adjust the parameters to suit the asset you’re testing.
Release Notes
June 30, 2025
• Updated default backtest period to end on June 30, 2025.
• Default commission adjusted to 0.1 %.
• Slippage set to 3 ticks.
• Default slippage set to 3 ticks.
• Simplified the strategy name to “Tuga Supertrend”.
Default Parameters
Parameter Default Value
Supertrend Period 10
Multiplier (Factor) 3
Commission 0.1 %
Slippage 3 ticks
Start Date January 1, 2018
End Date June 30, 2025
[Mustang Algo] Channel Strategy# Mustang Algo Channel Strategy - Universal Market Sentiment Oscillator
## 🎯 ORIGINAL CONCEPT
This strategy employs a unique market sentiment oscillator that works on ALL financial assets. It uses Bitcoin supply dynamics combined with stablecoin market capitalization as a macro sentiment indicator to generate universal timing signals across stocks, forex, commodities, indices, and cryptocurrencies.
## 🌐 UNIVERSAL APPLICATION
- **Any Asset Class:** Stocks, Forex, Commodities, Indices, Crypto, Bonds
- **Market-Wide Timing:** BTC/Stablecoin ratio serves as a global risk sentiment gauge
- **Cross-Market Signals:** Trade any instrument using macro liquidity conditions
- **Ecosystem Approach:** One oscillator for all financial markets
## 🧮 METHODOLOGY
**Core Calculation:** BTC Supply / (Combined Stablecoin Market Cap / BTC Price)
- **Data Sources:** DAI + USDT + USDC market capitalizations
- **Signal Generation:** RSI(14) applied to the ratio, double-smoothed with WMA
- **Timing Logic:** Crossover signals filtered by overbought/oversold zones
- **Multi-Timeframe:** Configurable timeframe analysis (default: Daily)
## 📈 TRADING STRATEGY
**LONG Entries:** Bullish crossover when market sentiment is oversold (<48)
**SHORT Entries:** Bearish crossover when market sentiment is overbought (>55)
**Universal Timing:** These macro signals apply to trading any financial instrument
## ⚙️ FLEXIBLE RISK MANAGEMENT
**Three SL/TP Calculation Modes:**
- **Percentage Mode:** Traditional % based (4% SL, 12% TP default)
- **Ticks Mode:** Precise tick-based calculation (50/150 ticks default)
- **Pips Mode:** Forex-style pip calculation (50/150 pips default)
**Realistic Parameters:**
- Commission: 0.1% (adjustable for different asset classes)
- Slippage: 2 ticks
- Position sizing: 10% of equity (conservative)
- No pyramiding (single position management)
## 📊 KEY ADVANTAGES
✅ **Universal Application:** One strategy for all asset classes
✅ **Macro Foundation:** Based on global liquidity and risk sentiment
✅ **False Signal Filtering:** Overbought/oversold zones reduce noise
✅ **Flexible Risk Management:** Multiple SL/TP calculation methods
✅ **No Lookahead Bias:** Clean backtesting with realistic results
✅ **Cross-Market Correlation:** Captures broad market risk cycles
## 🎛️ CONFIGURATION GUIDE
1. **Asset Selection:** Apply to stocks, forex, commodities, indices, crypto
2. **Timeframe Setup:** Daily recommended for swing trading
3. **Sentiment Bounds:** Adjust 48/55 levels based on market volatility
4. **Risk Management:** Choose appropriate SL/TP mode for your asset class
5. **Direction Filter:** Select Long Only, Short Only, or Both
## 📋 BACKTESTING STANDARDS
**Compliant with TradingView Guidelines:**
- ✅ Realistic commission structure (0.1% default)
- ✅ Appropriate slippage modeling (2 ticks)
- ✅ Conservative position sizing (10% equity)
- ✅ Sustainable risk ratios (1:3 SL/TP)
- ✅ No lookahead bias (proper historical simulation)
- ✅ Sufficient sample size potential (100+ trades possible)
## 🔬 ORIGINAL RESEARCH
This strategy introduces a revolutionary approach to financial markets by treating the BTC/Stablecoin ratio as a global risk sentiment gauge. Unlike traditional indicators that analyze individual asset price action, this oscillator captures macro liquidity flows that affect ALL financial markets - from stocks to forex to commodities.
## 🎯 MARKET APPLICATIONS
**Stocks & Indices:** Risk-on/risk-off sentiment timing
**Forex:** Global liquidity flow analysis for major pairs
**Commodities:** Risk appetite for inflation hedges
**Bonds:** Flight-to-safety vs. risk-seeking behavior
**Crypto:** Native application with direct correlation
## ⚠️ RISK DISCLOSURE
- Designed for intermediate to long-term trading across all timeframes
- Market sentiment can remain extreme longer than expected
- Always use appropriate position sizing for your specific asset class
- Adjust commission and slippage settings for different markets
- Past performance does not guarantee future results
## 🚀 INNOVATION SUMMARY
**What makes this strategy unique:**
- First to use BTC/Stablecoin ratio as universal market sentiment indicator
- Applies macro-economic principles to technical analysis across all assets
- Single oscillator provides timing signals for entire financial ecosystem
- Bridges traditional finance with digital asset insights
- Combines fundamental liquidity analysis with technical precision
WebhookGeneratorLibrary "WebhookGenerator"
Generates Json objects for webhook messages.
GenerateOT(license_id, symbol, action, order_type, trade_type, size, price, tp, sl, risk, trailPrice, trailOffset)
CreateOrderTicket: Establishes a order ticket.
Parameters:
license_id (string) : Provide your license index
symbol (string) : Symbol on which to execute the trade
action (string) : Execution method of the trade : "MRKT" or "PENDING"
order_type (string) : Direction type of the order: "BUY" or "SELL"
trade_type (string) : Is it a "SPREAD" trade or a "SINGLE" symbol execution?
size (float) : Size of the trade, in units
price (float) : If the order is pending you must specify the execution price
tp (float) : (Optional) Take profit of the order
sl (float) : (Optional) Stop loss of the order
risk (float) : Percent to risk for the trade, if size not specified
trailPrice (float) : (Optional) Price at which trailing stop is starting
trailOffset (float) : (Optional) Amount to trail by
Returns: Return Order string
Asset Allocation CalculatorOverview
This script is a tool that automatically calculates asset allocation for your investment portfolio. Users can set the weight of multiple assets and monitor the portfolio value in real time based on price fluctuations.
Key Features
Supports input of asset allocation percentages
Dynamic allocation calculation based on real-time price data
Automatically calculates allocated amounts for each asset based on the total investment amount
User-friendly interface with intuitive visual feedback
Settings
Total Capital : Enter the total capital, including the value of assets.
Quantity rounding : Using the rounding function may cause the target allocation to exceed 100%.
Tickers, Weight, Holdings :
To retrieve accurate asset prices, specify both the exchange and the ticker.
If you want to include cash in your portfolio, use $.
Ensure that the total allocation sums to 100%.
Refer to the pre-filled example for the correct format.
Table Settings : You can adjust the table's position, height, font size, and background color.
How to Use
By buying or selling the quantity shown in the Buy column, you can continuously maintain your target allocation.
Hold - Current holdings
Buy - Quantity to buy or sell to reach the target allocation
Target - Quantity aimed for after buying or selling
Caution
It can only calculate for a single currency, so do not mix multiple currency markets.
자산 배분 계산기
소개
이 스크립트는 투자 포트폴리오의 자산 배분을 자동으로 계산해주는 도구입니다. 사용자는 여러 자산의 비중을 설정할 수 있으며, 가격 변동에 따라 포트폴리오 가치를 실시간으로 모니터링할 수 있습니다.
주요 기능
자산 배분 비율 입력 지원
실시간 가격 데이터를 기반으로 한 동적 배분 계산
총 투자 금액을 기준으로 각 자산에 할당된 금액 자동 계산
직관적인 시각적 피드백을 제공하는 사용자 친화적인 인터페이스
설정
Total Capital : 자산 가치를 포함한 총 자본금을 입력하세요.
Quantity rounding : 반올림 기능을 사용하면 목표 비중이 100%를 초과할 수 있습니다.
Tickers, Weight, Holdings :
정확한 자산 가격을 불러오기 위해 거래소와 티커를 함께 입력하세요.
포트폴리오에 현금을 포함하려면 '$'를 사용하세요.
비중 합계가 반드시 100%가 되도록 설정하세요.
예제 형식을 참고하여 올바르게 입력하세요.
한국(원화) 시장을 위한 입력 예시입니다.
KRX:360750, 17.5, 100
KRX:310960, 17.5, 120
KRX:148070, 25, 20
KRX:305080, 25, 10
KRX:139320, 10, 150
UPBIT:BTCKRW, 5, 0.002
$,0,5000000
Table Settings : 테이블의 위치, 높이, 글자 크기 및 배경색을 조정할 수 있습니다.
사용 방법
Buy 열에 표시된 수량만큼 매수 또는 매도하면 목표 비중을 지속적으로 유지할 수 있습니다.
Hold - 현재 보유 수량
Buy - 목표 비중을 맞추기 위해 매수 또는 매도해야 하는 수량
Target - 매수, 매도 후 목표로 하는 수량
주의
한 가지 통화로만 계산할 수 있으니 여러 통화 시장을 혼용하지 마세요.
HTF RangeThis Pine Script indicator, HTF Range , is a tool designed to help traders visualize predefined ranges (highs and lows) and analyze price action within those levels. It's particularly useful for identifying key levels and trends for a set of pre-configured assets, such as cryptocurrencies, stocks, and forex pairs.
Key Features:
1. Predefined Symbol Ranges:
Stores a list of assets (tickers) with corresponding high, low, and trend information in an array.
Automatically matches the current symbol on the chart (syminfo.ticker) to fetch and display relevant range data:
High Range: The upper price level.
Low Range: The lower price level.
Trend: Indicates whether the trend is "up" or "down."
Example tickers: BTCUSDT, ETHUSDT, GBPUSD, NVDA, and more.
2. Range Visualizations:
Extremeties: Draws dashed horizontal lines for the high and low levels.
Half-Level: Marks the midpoint of the range with a dashed yellow line.
Upper and Lower Quarters: Highlights upper and lower portions of the range using shaded boxes with customizable extensions:
3. Configurable Inputs:
Enable/Disable Levels: Toggles for extremeties, half-levels, and quarter-levels.
Table Info: Option to display a table summarizing the range data (symbol, high, low, and trend).
4. Dynamic Calculations:
Automatically calculates the difference between the high and low (diff) for precise range subdivisions.
Dynamically adjusts visuals based on the trend (up or down) for better relevance to the market condition.
5. Table Display:
Provides a detailed summary of the asset's range and trend in the top-right corner of the chart:
Symbol ticker.
High and low levels.
Overall trend direction.
Use Case:
This indicator is ideal for traders who:
Trade multiple assets and want a quick overview of key price ranges.
Analyze price movements relative to predefined support and resistance zones.
Use range-based strategies for trend following, breakout trading, or reversals.
Hinton Map█ HINTON MAP
This script displays a Hinton Map visualization of market data for user-defined tickers and timeframes. It uses color gradients to represent the magnitude and direction of price change, RSI, and a combination of both.
This is one example. You can modify and try other values as you wish, but do keep the incoming values between -1 and 1.
In the Example Usage:
Users can input up to 5 symbols and 5 timeframes. For each ticker/timeframe combination:
The box size represents the relative magnitude of the 2-bar percentage change.
The box fill color represents the direction and magnitude of the 2-bar percentage change.
The box border color and thickness represent the RSI deviation from 50.
The inner box color represents a combination of price change magnitude and RSI deviation from 50.
Hovering over each box displays a tooltip with the ticker, timeframe, percentage change, and RSI.
Inputs:
• Unit Size (bars):
The size of each Hinton unit in bars.
Type: int
Default Value: 10
• Border Width:
The base width of the inner box border.
Type: int
Default Value: 3
• Negative Hue (0-360):
The hue value for negative price changes (0-360).
Type: float
Default Value: 100
• Positive Hue (0-360):
The hue value for positive price changes (0-360).
Type: float
Default Value: 180
• Ticker 1-5:
The tickers to display on the Hinton map.
Type: string
Default Value: AAPL
• Timeframes (comma separated):
The timeframes to display on the Hinton map (comma-separated).
Type: string
Default Value: 1, 5, 60, 1D, 1W
(Fun Note: My Home town is named `Hinton`)
Master Candle Breakout V1 Master Candle Breakout V1 - Indicator Description
The Master Candle Breakout V1 indicator is a powerful price action-based tool designed to help traders identify and capitalize on breakout opportunities from consolidation phases. This indicator is particularly useful for identifying master candles, which are large candles that encompass the range of subsequent candles, creating a key level of support or resistance. Once the price breaks above or below the range of the master candle, the indicator provides clear buy or sell signals, allowing traders to ride the momentum of the breakout.
Key Features:
Master Candle Detection: The indicator identifies master candles based on a user-defined period, marking them on the chart as critical breakout points.
Buy and Sell Signals: When the price breaks above the master candle's high, a buy signal is plotted. Similarly, when the price breaks below the master candle's low, a sell signal is generated. These signals are displayed on the chart with customizable shapes (diamonds, arrows, circles, crosses) and colors for easy visualization.
Stop-Loss Level Display: For risk management, the indicator calculates and plots a stop-loss level based on user-defined ticks above or below the master candle's high or low. The stop-loss value is shown as a label next to the signal, helping traders manage risk effectively.
Customizable Colors and Shapes: Users can fully customize the appearance of the signals, including the color of the buy/sell diamonds, the stop-loss label text color, and the type of shape used for the signals.
Versatile Application: The Master Candle Breakout V1 can be applied to any timeframe and market, from forex and stocks to commodities and cryptocurrencies, making it a highly versatile tool for traders of all types.
How to Use:
Master Candle Period: Define how many candles should follow the master candle for confirmation.
Stop Loss Ticks: Set the number of ticks above or below the master candle to define your stop-loss level.
Entry Signals: Once the price closes outside the high or low of the master candle, enter the trade accordingly (buy on breakouts above the high, sell on breakouts below the low).
Risk Management: Use the stop-loss level provided by the indicator to minimize losses and protect your capital.
This indicator is perfect for traders who prefer a simple, price-action-based strategy and want to avoid the clutter of traditional indicators. By focusing on the core principle of breakouts, Master Candle Breakout V1 helps traders quickly identify consolidation zones and potential breakout trades.
Cumulative Volume Delta Strategy | Flux Charts💎 GENERAL OVERVIEW
Introducing the Cumulative Volume Delta Strategy (CVDS) Indicator, an advanced tool designed to enhance trading strategies by identifying potential trend reversals through volume dynamics. This script features integrated order block detection, Fair Value Gaps (FVGs), and a dynamic take-profit (TP) and stop-loss (SL) system. For an in-depth understanding of the strategy, refer to the "HOW DOES IT WORK?" section below.
Features of the new Cumulative Volume Delta Strategy (CVDS) Indicator :
Cumulative Volume Delta-based Strategy
Order Block and Fair Value Gap (FVG) Entry Methods
Dynamic TP/SL System
Customizable Risk Management Settings
Alerts for Buy, Sell, TP, and SL Signals
📌 HOW DOES IT WORK ?
The CVDS indicator operates by tracking the net volume difference between buyers and sellers to identify divergences that could indicate potential trend reversals. A cumulative volume delta (CVD) calculation is employed to measure the intensity of these divergences in relation to price movements. The net volume sum is reset every trading day (can be changed from the settings using the anchor period option), and divergences are detected when the cumulative volume crosses the 0-line over or under.
Once a significant divergence is detected, the indicator identifies breakout points, confirmed by either Fair Value Gaps (FVGs) or Order Blocks (OBs). Depending on your chosen entry mode, the indicator will trigger a buy or sell entry when the confirmation signal aligns with the breakout direction. Alerts for Buy, Sell, Take-Profit, and Stop-Loss are available.
Note that the indicator cannot run on 1-minute and 1-second charts, as it needs to get data from a lower timeframe. 1-minutes & 1-second timeframes are the minimum timeframes in their ranges respectively.
🚩 UNIQUENESS
What sets this indicator apart is the combination of volume divergence analysis with advanced price action tools like Fair Value Gaps (FVGs) and Order Blocks (OBs). The ability to choose between these methods, along with a dynamic TP/SL system that adapts based on volatility, provides flexibility for traders in any market condition. The backtesting dashboard provides metrics about the performance of the indicator. You can use it to tune the settings for best use in the current ticker. The CVD-based strategy ensures that trades are initiated only when meaningful divergences between volume and price occur, filtering out noise and increasing the likelihood of profitable trades.
⚙️ SETTINGS
1. General Configuration
Anchor Period: Time anchor period used in CVD calculation. This is essentially the period that the volume delta sum will be reset. Lower timeframes may result in more entries at the cost of less reliable results.
Entry Mode: Choose between FVGs or OBs to trigger your entries based on the confirmation signals.
Retracement Requirement: Enable to confirm the entry after a retracement toward the FVG or OB.
2. Fair Value Gaps
FVG Sensitivity: Modify the sensitivity of FVG detection, allowing for more or fewer gaps to be considered valid.
3. Order Blocks (OB)
Swing Length: Define the swing length to identify OB formations. Shorter lengths find smaller OBs, while longer lengths detect larger structures.
4. TP / SL
TP / SL Method:
a) Dynamic: The TP / SL zones will be auto-determined by the algorithm based on the Average True Range (ATR) of the current ticker.
b) Fixed : You can adjust the exact TP / SL ratios from the settings below.
Dynamic Risk: The risk you're willing to take if "Dynamic" TP / SL Method is selected. Higher risk usually means a better winrate at the cost of losing more if the strategy fails. This setting is has a crucial effect on the performance of the indicator, as different tickers may have different volatility so the indicator may have increased performance when this setting is correctly adjusted.
Crypto Heatmap [Pinescriptlabs]🌟 Crypto Heatmap is a visual tool that enables quick and efficient visualization of price behavior and percentage changes of various cryptocurrencies.
📊 It generates a heatmap to show variations in daily closing prices, helping traders quickly identify assets with the most movement.
📈 Percentage Change Calculation: It calculates the difference between the current price and the previous day's price, updating with each ticker.
✨ It uses a dynamic approach that adjusts colors based on market movements, making it easier to detect trading opportunities.
👀 You will notice for a moment that some cells disappear; this is because the table updates with each ticker to show real-time changes.
Español:
🌟 Crypto Heatmap es una herramienta visual que permite una rápida y eficiente visualización del comportamiento de precios y cambios porcentuales de varias criptomonedas.
📊 Genera un mapa de calor para mostrar las variaciones en los precios de cierre diario, ayudando a los traders a identificar rápidamente los activos con mayor movimiento.
📈 Cálculo del cambio porcentual: Calcula la diferencia entre el precio actual y el del día anterior, actualizándose en cada ticker.
✨ Utiliza un enfoque dinámico que ajusta los colores según los movimientos del mercado, facilitando la detección de oportunidades de trading.
Aquí tienes la traducción al español:
👀 **Observarás por un momento que algunas celdas desaparecen; esto es porque la tabla se actualiza en cada ticker para mostrar el cambio en tiempo real.**
Short Interest Tracker [SS]This is a simple indicator that is designed to provide you with a synopsis of short interest on the daily, weekly and monthly timeframes.
How it works:
It pulls FINRA ticker data on short volume for whichever ticker you are on. It works with all tickers provided they are listed on FINRA (which is all tickers).
It will not work with futures, for futures, you would want to use a COT-based indicator, but for indices and equities, this indicator will provide you with the short volume information.
What it shows:
It breaks short volume down into current short volume, the 14-period SMA of short volume over the day, week and month, it also provides you with a short volume to SMA ratio. This is Short Volume divided by the SMA. Anything below 1 is good, it means short interest is low. Anything above 1 is not good, it means that short volume is above the SMA.
It also will show you the weekly, daily and monthly short volume change.
And last but not least, it will tell you whether short interest is falling, rising or steady. How it does this is by tracking whether the SMA is increasing, decreasing or stagnant.
Customization:
You can customize the SMA length and the assessment of whether short volume is increasing or decreasing. The default SMA length is 14 and the default assessment of rising/falling short volume is 4. This means, short volume has to rise or fall over a 4-period timeframe for it to register. So on the week, if it displays short volume increasing, it means that, over the past 4 weeks, the sma has steadily risen. Inverse if it decreases. If you want it to be more sensitive, you can reduce it to 2 or 3. If you want it to be more strict, you can increase it to 5 or 6.
NOTE:
If the volume information for a ticker is not available, it will return a runtime error indicating as such.
And that's the indicator!
I wanted something similar to COT data for equities and indices, so this was my attempt to bridge that gap.
Hope you enjoy and find it useful! Leave your suggestions below.
Take care everyone!
ETHE Premium SmoothedThis script visualizes the "premium" or "deflection" between the price of Ethereum in a fund (ETHE) and the price of Ethereum itself. It's used to detect when the ETHE fund is trading at a significant premium or discount compared to the actual value of Ethereum it represents.
Components:
Two-Pole Smoothing Function: This function acts as a filter to smoothen data, specifically the calculated deflection. Using a combination of exponential math and trigonometry, the function reduces the noise from the raw deflection data, providing a clearer view of the trend.
ETH Per Share: A constant that represents the amount of Ethereum backing each share of ETHE.
Tickers: The script fetches data for two tickers:
ETHE ticker from OTC markets.
Ethereum's ticker from Coinbase.
Deflection Calculation: This represents the difference between the price of one share of ETHE and its actual value in Ethereum. This percentage gives an idea of how much more or less the ETHE is trading compared to its intrinsic Ethereum value.
Smoothing: The raw deflection data is then passed through the Two-Pole Smoothing function to produce the "smoothed" deflection curve.
Visuals:
A horizontal dashed red line at 0%, indicating the point where ETHE trades exactly at its intrinsic Ethereum value.
A plot of the smoothed deflection, with its color changing based on whether the value is above or below zero (green for above, red for below).
Usage:
Traders can use this script to identify potential buy or sell opportunities. For instance, if ETHE is trading at a significant discount (a negative deflection value), it might be an attractive buying opportunity, assuming the discrepancy will eventually correct itself. Conversely, if ETHE is trading at a significant premium (a positive deflection value), it might indicate a potential overvaluation.
Euclidean Distance Predictive Candles [SS]Finally releasing this, its been in the works for the past 2 weeks and has undergone many iterations.
I am not sure if I am 100% happy with it yet, but I guess its best to release and get feedback to make improvements.
So this is the Euclidean distance predictive candle indicator and what it does is exactly what it sounds like, it uses Euclidean distance to identify similar candles and then plot the candles and range that immediately proceeded like candles.
While this is using a general machine learning/data science approach (Euclidean distance), I do not employ the KNN (Nearest Neighbors) algo into this. The reason being is it simply offered no predictive advantage than isolating for the last case. I tried it, I didn't like it, the results were not improve and, at times, acutally hindered so I ditched it. Perhaps it was my approach but using some other KNN indicators, I just don't really find them all that more advantageous to simply relying on the Law of Large Numbers and collecting more data rather than less data (which we will get into later in this explanation).
So using this indicator:
There is a lot of customizability here. And the reason is, not all settings are going to work the same for all tickers. To help you narrow down your parameters, I have included various backtest results that show you how the model is performing. You see in the AMZN chart above, with the current settings, it is performing optimally, with a cumulative range pass of 99% (meaning that, of all the cases, the indicator accurately predicted the next day high OR low range 99% of the time), and the ability to predict the candle slightly over 52%.
The recommended settings, from me, are as follows:
So these are generally my recommended settings.
Euclidian Tolerance: This will determine the parameters to look for similar candles. In general, the lower the tolerance, the greater the precision. I recommend keeping it between 0.5, for tickers with larger prices (like ES1! futures or NQ1!) or 0.05 for tickers with lower TPs, like SPY or QQQ.
If the ED Tolerance is too extreme that the indicator cannot find identical setups, it will alert you:
But in general, the more precise you can get it, the better.
Anchor Type: You will see the option to anchor by "Predicted Open" or by "Previous Close". I suggest sticking with anchoring by predicted open. All this means is, it is going to anchor your range, candle, high and low targets by the predicted open price. Anchoring by previous close will anchor by the close of yesterday. Both work okay, but in general the results from anchoring to predicted open have higher pass rates and more accurately depict the candle.
Euclidean Distance Measurement Type: You can choose to measure by candle body or from high to low wicks. I haven't played around with measuring from high to low wicks all that much, because candle body tends to do the job. But remember, ED is a neutral measurement. Which means, its not going to distinguish between a red or green candle, just the formation of the candle. Thus, I tend to recommend, pragmatically, not to necessarily rely on the candle being red or green, but one the formation of the candle (where are the wicks going, are there more bearish wicks or bullish wicks) etc. Examples will follow.
Range Prediction Type: You can filter the range prediction type by last instance (in which, it will pull the previous identical candle and plot the next candle that followed it, adjusted for the current ranges) or "Average of All Cases". So this is where we need to talk a little bit about the law of large numbers.
In general, in statistics, when you have a huge amount of random data, the law of large numbers stipulates that, within this randomness should be repeated events. This is why sometimes chart patterns work, sometimes they don't. When we filter by the average of all cases, we are relying on the law of large numbers. In general, if you are getting good Backtest readings from Last Instance, then you don't need to use this function. But it provides an alternative insight into potential candle formations next day. Its not a bad idea to compare between the two and look for similarities and differences.
So now that we have covered the boring details, let's get into how to use the indicator and some examples.
So the indicator is plotting the range and candle for the next day. As such, we are not looking at the current candle being plotted, but we are looking at the previous candle (see image below for example):
The green arrow shows the prediction for Friday, along with the corresponding result. The purple arrow shows the prediction for Monday which we have yet to realize.
So remember when you are using this, you need to look at the previous candle, and not the candle that it is currently plotting with realtime data, because it is plotting for the next candle.
If you are plotting by last instance, the indicator will tell you which day it is pulling its data from if you have opted to toggle on the demographic data:
You can see the green arrow pointing to the date where it is pulling from. This data serves as the example candle with the candle proceeding this date being the anchored candle (or the predicted candle).
Price Targets and Probability:
In the chart, you can see the green arrow pointing to the green portion of the table. In this table, it will give you the current TPs. These represent the current time target price, which means, the TPs shown here are for Friday. On Monday, the table will update with the TPs for Monday, etc. If you want to view the TPs in advance, you can view them from the actual candle itself.
Below the TPs, you see a bullish 7:6. It means, in a total of 13 cases, the next candle was bullish 7 times and bearish 6 times. Where do we see the number of cases? In the demographic table as well:
Auxiliary functions
Because you are using the previous candle, if you want to avoid confusion, you can have the indicator plot the price targets over the predicted candle, to anchor your attention so to speak. Simply select "Label" in the "Show Price Targets" section, which will look like this:
You can also ask the indicator to plot the demographic data of Higher High, Low, etc. information. What this does is simply looks at all the cases and plots how many times higher highs, lows, lower lows, highs etc. were made:
This will just count all of the cases identified and plot the number of times higher highs, lows, etc. were made.
Concluding Remarks
This is a kind of complex indicator and I can appreciate it may take some getting used to.
I will try to post a tutorial video at some point next week for it, so stay tuned for that.
But this isn't designed to make your life more complicated, just to help give you insights into potential outcomes for the next day or hour or 5 minute (it can be used on all timeframes).
If you find it helpful, great! If not, that's okay, too :-).
Please be aware, this is not my forte of indicators. I am not a data scientist or programmer. My background is in Epi and we don't use these types of data science approaches, so if you have any suggestions or critiques, feel free to share them below.
Otherwise, I hope you enjoy!
Take care everyone and safe trades!
Multi-Asset Performance [Spaghetti] - By LeviathanThis indicator visualizes the cumulative percentage changes or returns of 30 symbols over a given period and offers a unique set of tools and data analytics for deeper insight into the performance of different assets.
Multi Asset Performance indicator (also called “Spaghetti”) makes it easy to monitor the changes in Price, Open Interest, and On Balance Volume across multiple assets simultaneously, distinguish assets that are overperforming or underperforming, observe the relative strength of different assets or currencies, use it as a tool for identifying mean reversion opportunities and even for constructing pairs trading strategies, detect "risk-on" or "risk-off" periods, evaluate statistical relationships between assets through metrics like correlation and beta, construct hedging strategies, trade rotations and much more.
Start by selecting a time period (e.g., 1 DAY) to set the interval for when data is reset. This will provide insight into how price, open interest, and on-balance volume change over your chosen period. In the settings, asset selection is fully customizable, allowing you to create three groups of up to 30 tickers each. These tickers can be displayed in a variety of styles and colors. Additional script settings offer a range of options, including smoothing values with a Simple Moving Average (SMA), highlighting the top or bottom performers, plotting the group mean, applying heatmap/gradient coloring, generating a table with calculations like beta, correlation, and RSI, creating a profile to show asset distribution around the mean, and much more.
One of the most important script tools is the screener table, which can display:
🔸 Percentage Change (Represents the return or the percentage increase or decrease in Price/OI/OBV over the current selected period)
🔸 Beta (Represents the sensitivity or responsiveness of asset's returns to the returns of a benchmark/mean. A beta of 1 means the asset moves in tandem with the market. A beta greater than 1 indicates the asset is more volatile than the market, while a beta less than 1 indicates the asset is less volatile. For example, a beta of 1.5 means the asset typically moves 150% as much as the benchmark. If the benchmark goes up 1%, the asset is expected to go up 1.5%, and vice versa.)
🔸 Correlation (Describes the strength and direction of a linear relationship between the asset and the mean. Correlation coefficients range from -1 to +1. A correlation of +1 means that two variables are perfectly positively correlated; as one goes up, the other will go up in exact proportion. A correlation of -1 means they are perfectly negatively correlated; as one goes up, the other will go down in exact proportion. A correlation of 0 means that there is no linear relationship between the variables. For example, a correlation of 0.5 between Asset A and Asset B would suggest that when Asset A moves, Asset B tends to move in the same direction, but not perfectly in tandem.)
🔸 RSI (Measures the speed and change of price movements and is used to identify overbought or oversold conditions of each asset. The RSI ranges from 0 to 100 and is typically used with a time period of 14. Generally, an RSI above 70 indicates that an asset may be overbought, while RSI below 30 signals that an asset may be oversold.)
⚙️ Settings Overview:
◽️ Period
Periodic inputs (e.g. daily, monthly, etc.) determine when the values are reset to zero and begin accumulating again until the period is over. This visualizes the net change in the data over each period. The input "Visible Range" is auto-adjustable as it starts the accumulation at the leftmost bar on your chart, displaying the net change in your chart's visible range. There's also the "Timestamp" option, which allows you to select a specific point in time from where the values are accumulated. The timestamp anchor can be dragged to a desired bar via Tradingview's interactive option. Timestamp is particularly useful when looking for outperformers/underperformers after a market-wide move. The input positioned next to the period selection determines the timeframe on which the data is based. It's best to leave it at default (Chart Timeframe) unless you want to check the higher timeframe structure of the data.
◽️ Data
The first input in this section determines the data that will be displayed. You can choose between Price, OI, and OBV. The second input lets you select which one out of the three asset groups should be displayed. The symbols in the asset group can be modified in the bottom section of the indicator settings.
◽️ Appearance
You can choose to plot the data in the form of lines, circles, areas, and columns. The colors can be selected by choosing one of the six pre-prepared color palettes.
◽️ Labeling
This input allows you to show/hide the labels and select their appearance and size. You can choose between Label (colored pointed label), Label and Line (colored pointed label with a line that connects it to the plot), or Text Label (colored text).
◽️ Smoothing
If selected, this option will smooth the values using a Simple Moving Average (SMA) with a custom length. This is used to reduce noise and improve the visibility of plotted data.
◽️ Highlight
If selected, this option will highlight the top and bottom N (custom number) plots, while shading the others. This makes the symbols with extreme values stand out from the rest.
◽️ Group Mean
This input allows you to select the data that will be considered as the group mean. You can choose between Group Average (the average value of all assets in the group) or First Ticker (the value of the ticker that is positioned first on the group's list). The mean is then used in calculations such as correlation (as the second variable) and beta (as a benchmark). You can also choose to plot the mean by clicking on the checkbox.
◽️ Profile
If selected, the script will generate a vertical volume profile-like display with 10 zones/nodes, visualizing the distribution of assets below and above the mean. This makes it easy to see how many or what percentage of assets are outperforming or underperforming the mean.
◽️ Gradient
If selected, this option will color the plots with a gradient based on the proximity of the value to the upper extreme, zero, and lower extreme.
◽️ Table
This section includes several settings for the table's appearance and the data displayed in it. The "Reference Length" input determines the number of bars back that are used for calculating correlation and beta, while "RSI Length" determines the length used for calculating the Relative Strength Index. You can choose the data that should be displayed in the table by using the checkboxes.
◽️ Asset Groups
This section allows you to modify the symbols that have been selected to be a part of the 3 asset groups. If you want to change a symbol, you can simply click on the field and type the ticker of another one. You can also show/hide a specific asset by using the checkbox next to the field.
Options & Leveraged Shares Heatmap This is the leveraged share/option heatmap / screener.
Tradingview offers a few different tickers that have PTCR data on the daily timeframe. So I was able to pull those few tickers that display the PTCR data and format it into a heatmap.
I also had some room to add leveraged share data as well.
It is pretty self explanatory but I will go over it really briefly:
The timeframe is 1 D. This cannot be changed because this is the only timeframe available for the PTCR data.
It will pull the current day PTCR as well as the previous day PTCR and display the PTCR and change value.
The screening will be done according to the 1 day change.
You have the ability to select the option to sort by Max and Min or sort by heatmap:
Displaying max and min will show you the max positive and negative change among all the available tickers.
Max positive = bearish, as this indicates an uptick in Puts.
Max negative = bullish, as this indicates a decline in Puts.
If we flip over to the leveraged shares, it is the same:
To keep it consistent, the leveraged share ratio is displayed similar to PTCR. It is Sell to Buy ratio. The higher the ratio, the more selling and vice versa.
Thus, the same rules apply. Max positive = bearish and max negative = bullish.
If you want to display the heatmap, this is what it will look like:
The darker the blue, the higher the change in either a negative or positive direction. The same for the leveraged shares:
And that is the indicator.
Hopefully you find it helpful. I like to reference it at the end of each day to see how things are looking in terms of positioning for the following day.
Leave your comments/questions and suggestions below.
Safe trades!
Expected Move from RSI [SS]Publishing this experimental indicator.
What it does:
The indicator uses a user-defined lookback period on a user-defined timeframe to lookback at all instances of RSI. It breaks RSI down as follows:
RSI between
0 - 10
10 - 20
20 - 30
30 - 40
40 - 50
50 - 60
60 - 70
70 - 80
80 - 90
90 - 100
From there, it stores the ticker's move from open to high and open to low. It will then use this data to look at the current RSI based on the specified timeframe and plot the expected move based on the average move the ticker does with a similar RSI reading.
It will plot the expected range, with the high range being plotted in green and the low range being plotted in red.
It will also display an infographic that dictates the current RSI based on the selected time frame, the anticipated up move and the anticipated down move. This infographic will also tell you the strength of the relationship (correlation) RSI has with the ticker's high or low price:
From there the user can determine whether this RSI reading is traditionally bullish or bearish for the ticker. A greater down move indicates that the RSI traditionally elicits a bearish response. A greater up move indicates the inverse.
The user can also view a chart of a breakdown of the anticipated moves based on RSI. If the option to "Show Expected Move Table" is select in the settings menu, the following table will appear:
From here you can see the average up move and down move a ticker does based on its corresponding RSI reading.
NOTE: When using the table, please adjust your chart timeframe to the selected timeframe on the indicator. Thus, if you are looking at the 1 hour levels, please adjust your chart to the 1 hour timeframe to use the chart.
Additional Note: When using the table, an "NaN" means that there are no instances of the ticker being at that RSI level within the designated timeframe period. You can extend your lookback period to up to 500 candles to see if it finds additional instances of similar RSI. Otherwise, you can adjust the selected timeframe.
Uses:
The indicator can be used on all timeframes. It can help give you an idea as to whether the RSI indicates a bearish or bullish sentiment.
It can signal a potential reversal or continuation. It can also help you with determining target prices for day trades and scalp trades.
And that is the indicator. Its pretty straight forward. It is experimental and new, so feel free to play around with it and let me know your thoughts.
Safe trades everyone and thank you for reading!
