Game Theory Trading StrategyGame Theory Trading Strategy: Explanation and Working Logic
This Pine Script (version 5) code implements a trading strategy named "Game Theory Trading Strategy" in TradingView. Unlike the previous indicator, this is a full-fledged strategy with automated entry/exit rules, risk management, and backtesting capabilities. It uses Game Theory principles to analyze market behavior, focusing on herd behavior, institutional flows, liquidity traps, and Nash equilibrium to generate buy (long) and sell (short) signals. Below, I'll explain the strategy's purpose, working logic, key components, and usage tips in detail.
1. General Description
Purpose: The strategy identifies high-probability trading opportunities by combining Game Theory concepts (herd behavior, contrarian signals, Nash equilibrium) with technical analysis (RSI, volume, momentum). It aims to exploit market inefficiencies caused by retail herd behavior, institutional flows, and liquidity traps. The strategy is designed for automated trading with defined risk management (stop-loss/take-profit) and position sizing based on market conditions.
Key Features:
Herd Behavior Detection: Identifies retail panic buying/selling using RSI and volume spikes.
Liquidity Traps: Detects stop-loss hunting zones where price breaks recent highs/lows but reverses.
Institutional Flow Analysis: Tracks high-volume institutional activity via Accumulation/Distribution and volume spikes.
Nash Equilibrium: Uses statistical price bands to assess whether the market is in equilibrium or deviated (overbought/oversold).
Risk Management: Configurable stop-loss (SL) and take-profit (TP) percentages, dynamic position sizing based on Game Theory (minimax principle).
Visualization: Displays Nash bands, signals, background colors, and two tables (Game Theory status and backtest results).
Backtesting: Tracks performance metrics like win rate, profit factor, max drawdown, and Sharpe ratio.
Strategy Settings:
Initial capital: $10,000.
Pyramiding: Up to 3 positions.
Position size: 10% of equity (default_qty_value=10).
Configurable inputs for RSI, volume, liquidity, institutional flow, Nash equilibrium, and risk management.
Warning: This is a strategy, not just an indicator. It executes trades automatically in TradingView's Strategy Tester. Always backtest thoroughly and use proper risk management before live trading.
2. Working Logic (Step by Step)
The strategy processes each bar (candle) to generate signals, manage positions, and update performance metrics. Here's how it works:
a. Input Parameters
The inputs are grouped for clarity:
Herd Behavior (🐑):
RSI Period (14): For overbought/oversold detection.
Volume MA Period (20): To calculate average volume for spike detection.
Herd Threshold (2.0): Volume multiplier for detecting herd activity.
Liquidity Analysis (💧):
Liquidity Lookback (50): Bars to check for recent highs/lows.
Liquidity Sensitivity (1.5): Volume multiplier for trap detection.
Institutional Flow (🏦):
Institutional Volume Multiplier (2.5): For detecting large volume spikes.
Institutional MA Period (21): For Accumulation/Distribution smoothing.
Nash Equilibrium (⚖️):
Nash Period (100): For calculating price mean and standard deviation.
Nash Deviation (0.02): Multiplier for equilibrium bands.
Risk Management (🛡️):
Use Stop-Loss (true): Enables SL at 2% below/above entry price.
Use Take-Profit (true): Enables TP at 5% above/below entry price.
b. Herd Behavior Detection
RSI (14): Checks for extreme conditions:
Overbought: RSI > 70 (potential herd buying).
Oversold: RSI < 30 (potential herd selling).
Volume Spike: Volume > SMA(20) x 2.0 (herd_threshold).
Momentum: Price change over 10 bars (close - close ) compared to its SMA(20).
Herd Signals:
Herd Buying: RSI > 70 + volume spike + positive momentum = Retail buying frenzy (red background).
Herd Selling: RSI < 30 + volume spike + negative momentum = Retail selling panic (green background).
c. Liquidity Trap Detection
Recent Highs/Lows: Calculated over 50 bars (liquidity_lookback).
Psychological Levels: Nearest round numbers (e.g., $100, $110) as potential stop-loss zones.
Trap Conditions:
Up Trap: Price breaks recent high, closes below it, with a volume spike (volume > SMA x 1.5).
Down Trap: Price breaks recent low, closes above it, with a volume spike.
Visualization: Traps are marked with small red/green crosses above/below bars.
d. Institutional Flow Analysis
Volume Check: Volume > SMA(20) x 2.5 (inst_volume_mult) = Institutional activity.
Accumulation/Distribution (AD):
Formula: ((close - low) - (high - close)) / (high - low) * volume, cumulated over time.
Smoothed with SMA(21) (inst_ma_length).
Accumulation: AD > MA + high volume = Institutions buying.
Distribution: AD < MA + high volume = Institutions selling.
Smart Money Index: (close - open) / (high - low) * volume, smoothed with SMA(20). Positive = Smart money buying.
e. Nash Equilibrium
Calculation:
Price mean: SMA(100) (nash_period).
Standard deviation: stdev(100).
Upper Nash: Mean + StdDev x 0.02 (nash_deviation).
Lower Nash: Mean - StdDev x 0.02.
Conditions:
Near Equilibrium: Price between upper and lower Nash bands (stable market).
Above Nash: Price > upper band (overbought, sell potential).
Below Nash: Price < lower band (oversold, buy potential).
Visualization: Orange line (mean), red/green lines (upper/lower bands).
f. Game Theory Signals
The strategy generates three types of signals, combined into long/short triggers:
Contrarian Signals:
Buy: Herd selling + (accumulation or down trap) = Go against retail panic.
Sell: Herd buying + (distribution or up trap).
Momentum Signals:
Buy: Below Nash + positive smart money + no herd buying.
Sell: Above Nash + negative smart money + no herd selling.
Nash Reversion Signals:
Buy: Below Nash + rising close (close > close ) + volume > MA.
Sell: Above Nash + falling close + volume > MA.
Final Signals:
Long Signal: Contrarian buy OR momentum buy OR Nash reversion buy.
Short Signal: Contrarian sell OR momentum sell OR Nash reversion sell.
g. Position Management
Position Sizing (Minimax Principle):
Default: 1.0 (10% of equity).
In Nash equilibrium: Reduced to 0.5 (conservative).
During institutional volume: Increased to 1.5 (aggressive).
Entries:
Long: If long_signal is true and no existing long position (strategy.position_size <= 0).
Short: If short_signal is true and no existing short position (strategy.position_size >= 0).
Exits:
Stop-Loss: If use_sl=true, set at 2% below/above entry price.
Take-Profit: If use_tp=true, set at 5% above/below entry price.
Pyramiding: Up to 3 concurrent positions allowed.
h. Visualization
Nash Bands: Orange (mean), red (upper), green (lower).
Background Colors:
Herd buying: Red (90% transparency).
Herd selling: Green.
Institutional volume: Blue.
Signals:
Contrarian buy/sell: Green/red triangles below/above bars.
Liquidity traps: Red/green crosses above/below bars.
Tables:
Game Theory Table (Top-Right):
Herd Behavior: Buying frenzy, selling panic, or normal.
Institutional Flow: Accumulation, distribution, or neutral.
Nash Equilibrium: In equilibrium, above, or below.
Liquidity Status: Trap detected or safe.
Position Suggestion: Long (green), Short (red), or Wait (gray).
Backtest Table (Bottom-Right):
Total Trades: Number of closed trades.
Win Rate: Percentage of winning trades.
Net Profit/Loss: In USD, colored green/red.
Profit Factor: Gross profit / gross loss.
Max Drawdown: Peak-to-trough equity drop (%).
Win/Loss Trades: Number of winning/losing trades.
Risk/Reward Ratio: Simplified Sharpe ratio (returns / drawdown).
Avg Win/Loss Ratio: Average win per trade / average loss per trade.
Last Update: Current time.
i. Backtesting Metrics
Tracks:
Total trades, winning/losing trades.
Win rate (%).
Net profit ($).
Profit factor (gross profit / gross loss).
Max drawdown (%).
Simplified Sharpe ratio (returns / drawdown).
Average win/loss ratio.
Updates metrics on each closed trade.
Displays a label on the last bar with backtest period, total trades, win rate, and net profit.
j. Alerts
No explicit alertconditions defined, but you can add them for long_signal and short_signal (e.g., alertcondition(long_signal, "GT Long Entry", "Long Signal Detected!")).
Use TradingView's alert system with Strategy Tester outputs.
3. Usage Tips
Timeframe: Best for H1-D1 timeframes. Shorter frames (M1-M15) may produce noisy signals.
Settings:
Risk Management: Adjust sl_percent (e.g., 1% for volatile markets) and tp_percent (e.g., 3% for scalping).
Herd Threshold: Increase to 2.5 for stricter herd detection in choppy markets.
Liquidity Lookback: Reduce to 20 for faster markets (e.g., crypto).
Nash Period: Increase to 200 for longer-term analysis.
Backtesting:
Use TradingView's Strategy Tester to evaluate performance.
Check win rate (>50%), profit factor (>1.5), and max drawdown (<20%) for viability.
Test on different assets/timeframes to ensure robustness.
Live Trading:
Start with a demo account.
Combine with other indicators (e.g., EMAs, support/resistance) for confirmation.
Monitor liquidity traps and institutional flow for context.
Risk Management:
Always use SL/TP to limit losses.
Adjust position_size for risk tolerance (e.g., 5% of equity for conservative trading).
Avoid over-leveraging (pyramiding=3 can amplify risk).
Troubleshooting:
If no trades are executed, check signal conditions (e.g., lower herd_threshold or liquidity_sensitivity).
Ensure sufficient historical data for Nash and liquidity calculations.
If tables overlap, adjust position.top_right/bottom_right coordinates.
4. Key Differences from the Previous Indicator
Indicator vs. Strategy: The previous code was an indicator (VP + Game Theory Integrated Strategy) focused on visualization and alerts. This is a strategy with automated entries/exits and backtesting.
Volume Profile: Absent in this strategy, making it lighter but less focused on high-volume zones.
Wick Analysis: Not included here, unlike the previous indicator's heavy reliance on wick patterns.
Backtesting: This strategy includes detailed performance metrics and a backtest table, absent in the indicator.
Simpler Signals: Focuses on Game Theory signals (contrarian, momentum, Nash reversion) without the "Power/Ultra Power" hierarchy.
Risk Management: Explicit SL/TP and dynamic position sizing, not present in the indicator.
5. Conclusion
The "Game Theory Trading Strategy" is a sophisticated system leveraging herd behavior, institutional flows, liquidity traps, and Nash equilibrium to trade market inefficiencies. It’s designed for traders who understand Game Theory principles and want automated execution with robust risk management. However, it requires thorough backtesting and parameter optimization for specific markets (e.g., forex, crypto, stocks). The backtest table and visual aids make it easy to monitor performance, but always combine with other analysis tools and proper capital management.
If you need help with backtesting, adding alerts, or optimizing parameters, let me know!
In den Scripts nach " TABLE " suchen
Uptrick: Z-Trend BandsOverview
Uptrick: Z-Trend Bands is a Pine Script overlay crafted to capture high-probability mean-reversion opportunities. It dynamically plots upper and lower statistical bands around an EMA baseline by converting price deviations into z-scores. Once price moves outside these bands and then reenters, the indicator verifies that momentum is genuinely reversing via an EMA-smoothed RSI slope. Signal memory ensures only one entry per momentum swing, and traders receive clear, real-time feedback through customizable bar-coloring modes, a semi-transparent fill highlighting the statistical zone, concise “Up”/“Down” labels, and a live five-metric scoring table.
Introduction
Markets often oscillate between trending and reverting, and simple thresholds or static envelopes frequently misfire when volatility shifts. Standard deviation quantifies how “wide” recent price moves have been, and a z-score transforms each deviation into a measure of how rare it is relative to its own history. By anchoring these bands to an exponential moving average, the script maintains a fluid statistical envelope that adapts instantly to both calm and turbulent regimes. Meanwhile, the Relative Strength Index (RSI) tracks momentum; smoothing RSI with an EMA and observing its slope filters out erratic spikes, ensuring that only genuine momentum flips—upward for longs and downward for shorts—qualify.
Purpose
This indicator is purpose-built for short-term mean-reversion traders operating on lower–timeframe charts. It reveals when price has strayed into the outer 5 percent of its recent range, signaling an increased likelihood of a bounce back toward fair value. Rather than firing on price alone, it demands that momentum follow suit: the smoothed RSI slope must flip in the opposite direction before any trade marker appears. This dual-filter approach dramatically reduces noise-driven, false setups. Traders then see immediate visual confirmation—bar colors that reflect the latest signal and age over time, clear entry labels, and an always-visible table of metric scores—so they can gauge both the validity and freshness of each signal at a glance.
Originality and Uniqueness
Uptrick: Z-Trend Bands stands apart from typical envelope or oscillator tools in four key ways. First, it employs fully normalized z-score bands, meaning ±2 always captures roughly the top and bottom 5 percent of moves, regardless of volatility regime. Second, it insists on two simultaneous conditions—price reentry into the bands and a confirming RSI slope flip—dramatically reducing whipsaw signals. Third, it uses slope-phase memory to lock out duplicate signals until momentum truly reverses again, enforcing disciplined entries. Finally, it offers four distinct bar-coloring schemes (solid reversal, fading reversal, exceeding bands, and classic heatmap) plus a dynamic scoring table, rather than a single, opaque alert, giving traders deep insight into every layer of analysis.
Why Each Component Was Picked
The EMA baseline was chosen for its blend of responsiveness—weighting recent price heavily—and smoothness, which filters market noise. Z-score deviation bands standardize price extremes relative to their own history, adapting automatically to shifting volatility so that “extreme” always means statistically rare. The RSI, smoothed with an EMA before slope calculation, captures true momentum shifts without the false spikes that raw RSI often produces. Slope-phase memory flags prevent repeated alerts within a single swing, curbing over-trading in choppy conditions. Bar-coloring modes provide flexible visual contexts—whether you prefer to track the latest reversal, see signal age, highlight every breakout, or view a continuous gradient—and the scoring table breaks down all five core checks for complete transparency.
Features
This indicator offers a suite of configurable visual and logical tools designed to make reversal signals both robust and transparent:
Dynamic z-score bands that expand or contract in real time to reflect current volatility regimes, ensuring the outer ±zThreshold levels always represent statistically rare extremes.
A smooth EMA baseline that weights recent price more heavily, serving as a fair-value anchor around which deviations are measured.
EMA-smoothed RSI slope confirmation, which filters out erratic momentum spikes by first smoothing raw RSI and then requiring its bar-to-bar slope to flip before any signal is allowed.
Slope-phase memory logic that locks out duplicate buy or sell markers until the RSI slope crosses back through zero, preventing over-trading during choppy swings.
Four distinct bar-coloring modes—Reversal Solid, Reversal Fade, Exceeding Bands, Classic Heat—plus a “None” option, so traders can choose whether to highlight the latest signal, show signal age, emphasize breakout bars, or view a continuous heat gradient within the bands.
A semi-transparent fill between the EMA and the upper/lower bands that visually frames the statistical zone and makes extremes immediately obvious.
Concise “Up” and “Down” labels that plot exactly when price re-enters a band with confirming momentum, keeping chart clutter to a minimum.
A real-time, five-metric scoring table (z-score, RSI slope, price vs. EMA, trend state, re-entry) that updates every two bars, displaying individual +1/–1/0 scores and an averaged Buy/Sell/Neutral verdict for complete transparency.
Calculations
Compute the fair-value EMA over fairLen bars.
Subtract that EMA from current price each bar to derive the raw deviation.
Over zLen bars, calculate the rolling mean and standard deviation of those deviations.
Convert each deviation into a z-score by subtracting the mean and dividing by the standard deviation.
Plot the upper and lower bands at ±zThreshold × standard deviation around the EMA.
Calculate raw RSI over rsiLen bars, then smooth it with an EMA of length rsiEmaLen.
Derive the RSI slope by taking the difference between the current and previous smoothed RSI.
Detect a potential reentry when price exits one of the bands on the prior bar and re-enters on the current bar.
Require that reentry coincide with an RSI slope flip (positive for a lower-band reentry, negative for an upper-band reentry).
On first valid reentry per momentum swing, fire a buy or sell signal and set a memory flag; reset that flag only when the RSI slope crosses back through zero.
For each bar, assign scores of +1, –1, or 0 for the z-score direction, RSI slope, price vs. EMA, trend-state, and reentry status.
Average those five scores; if the result exceeds +0.1, label “Buy,” if below –0.1, label “Sell,” otherwise “Neutral.”
Update bar colors, the semi-transparent fill, reversal labels, and the scoring table every two bars to reflect the latest calculations.
How It Actually Works
On each new candle, the EMA baseline and band widths update to reflect current volatility. The RSI is smoothed and its slope recalculated. The script then looks back one bar to see if price exited either band and forward to see if it reentered. If that reentry coincides with an appropriate RSI slope flip—and no signal has yet been generated in that swing—a concise label appears. Bar colors refresh according to your selected mode, and the scoring table updates to show which of the five conditions passed or failed, along with the overall verdict. This process repeats seamlessly at each bar, giving traders a continuous feed of disciplined, statistically filtered reversal cues.
Inputs
All parameters are fully user-configurable, allowing you to tailor sensitivity, lookbacks, and visuals to your trading style:
EMA length (fairLen): number of bars for the fair-value EMA; higher values smooth more but lag further behind price.
Z-Score lookback (zLen): window for calculating the mean and standard deviation of price deviations; longer lookbacks reduce noise but respond more slowly to new volatility.
Z-Score threshold (zThreshold): number of standard deviations defining the upper and lower bands; common default is 2.0 for roughly the outer 5 percent of moves.
Source (src): choice of price series (close, hl2, etc.) used for EMA, deviation, and RSI calculations.
RSI length (rsiLen): period for raw RSI calculation; shorter values react faster to momentum changes but can be choppier.
RSI EMA length (rsiEmaLen): period for smoothing raw RSI before taking its slope; higher values filter more noise.
Bar coloring mode (colorMode): select from None, Reversal Solid, Reversal Fade, Exceeding Bands, or Classic Heat to control how bars are shaded in relation to signals and band positions.
Show signals (showSignals): toggle on-chart “Up” and “Down” labels for reversal entries.
Show scoring table (enableTable): toggle the display of the five-metric breakdown table.
Table position (tablePos): choose which corner (Top Left, Top Right, Bottom Left, Bottom Right) hosts the scoring table.
Conclusion
By merging a normalized z-score framework, momentum slope confirmation, disciplined signal memory, flexible visuals, and transparent scoring into one Pine Script overlay, Uptrick: Z-Trend Bands offers a powerful yet intuitive tool for intraday mean-reversion trading. Its adaptability to real-time volatility and multi-layered filter logic deliver clear, high-confidence reversal cues without the clutter or confusion of simpler indicators.
Disclaimer
This indicator is provided solely for educational and informational purposes. It does not constitute financial advice. Trading involves substantial risk and may not be suitable for all investors. Past performance is not indicative of future results. Always conduct your own testing and apply careful risk management before trading live.
VIX Z-Score (Inverted)📘 Indicator: VIX Z-Score (Inverted) + Table
🔍 Overview
This indicator calculates the Z-Score of the VIX (Volatility Index) and inverts it to identify potential buying opportunities during periods of fear and caution during periods of extreme optimism. The Z-Score is smoothed and visually displayed alongside a dynamic info table.
⚙️ How It Works
VIX Data: The VIX (ticker: CBOE:VIX) is pulled in real time.
Z-Score Calculation:
𝑍
=
(
𝑉
𝐼
𝑋
−
mean
)
standard deviation
Z=
standard deviation
(VIX−mean)
Over a customizable lookback period (default: 50).
Inversion:
Since high VIX usually means fear (often a contrarian buying signal), we invert the Z-Score:
𝑍
inv
=
−
𝑍
Z
inv
=−Z
Smoothing:
An EMA is applied to reduce noise and false signals.
Clamping:
The Z-Score is linearly scaled and capped between +2 and -2 for easy visualization in the info table.
📊 Z-Score Table (Top-Right)
Range Interpretation Table Color
+1.5 to +2 Extreme fear → Buy zone 🟩 Green
+0.5 to +1.5 Moderate fear 🟨 Lime
–0.5 to +0.5 Neutral ⬜ Gray
–0.5 to –1.5 Growing complacency 🟧 Orange
–1.5 to –2 Extreme optimism → Caution 🟥 Red
The current Z-Score (clamped version) is shown in real time on the right-hand info panel.
🧠 How to Use It
+2 Zone (Table: Green):
Market fear is at an extreme. Historically, such conditions are contrarian bullish—possible entry zones.
–2 Zone (Table: Red):
Indicates extreme optimism and low fear. Often a signal to be cautious or take profits.
Middle range (±0.5):
Market is neutral. Avoid major decisions based solely on sentiment here.
🧪 Best Practices
Combine with price action, volume, or trend filters.
Works well on daily or 4H timeframes.
Not a standalone signal—best used to confirm or fade sentiment extremes.
Comprehensive Volume and Metrics with Pre-Market Volume Data
This script is designed for traders who want a detailed view of market activity, including regular market and pre-market volume, dollar volume, relative volume (RVOL), average daily range (ADR), average true range (ATR), relative strength index (RSI), and the QQQ’s percentage change.
The script includes customizable metrics displayed in tables on the chart for easy analysis, with the option to toggle the visibility of each metric.
Key Features:
Volume and Dollar Volume:
Displays the volume of shares traded during the current day (or pre-market, if enabled).
Includes a calculation of dollar volume, representing the total dollar amount of trades (Volume × Close Price).
Relative Volume (RVOL):
Displays RVOL Day, which is the relative volume of the current day compared to the 2-day moving average.
Shows RVOL 90D, indicating relative volume over the past 90 days.
Both RVOL metrics are calculated as percentages and display the percentage change compared to the standard (100%).
Pre-Market Data:
Includes pre-market volume (PVOL) and pre-market dollar volume (P$ VOL) which are displayed only if pre-market data is enabled.
Tracks volume and dollar volume during pre-market hours (4:00 AM to 9:30 AM Eastern Time) for more in-depth analysis.
Optionally, shows pre-market RSI based on volume-weighted close prices.
Average Daily Range (ADR):
Displays the percentage change between the highest and lowest prices over the defined ADR period (default is 20 days).
Average True Range (ATR):
Shows the ATR, a popular volatility indicator, for a given period (default is 14 bars).
RSI (Relative Strength Index):
Displays RSI for the given period (default is 14).
RSI is calculated using pre-market data when available.
QQQ:
Shows the percentage change of the QQQ ETF from the previous day’s close.
The QQQ percentage change is color-coded: green for positive, red for negative, and gray for no change.
Customizable Inputs:
Visibility Options: Toggle the visibility of each metric, such as volume, dollar volume, RVOL, ADR, ATR, RSI, and QQQ.
Pre-Market Data: Enable or disable the display of pre-market data for volume and dollar volume.
Table Positioning: Adjust the position of tables displaying the metrics either at the bottom-left or bottom-right of the chart.
Text Color and Table Background: Choose between white or black text for the tables and customize the background color.
Tables:
The script utilizes tables to display multiple metrics in an organized and easy-to-read format.
The values are updated dynamically, reflecting real-time data as the market moves.
Pre-Market Data:
The script calculates pre-market volume and dollar volume, along with other key metrics like RSI and RVOL, to help assess market sentiment before the market officially opens.
The pre-market data is accumulated from 4:00 AM to 9:30 AM ET, allowing for pre-market analysis and comparison to regular market hours.
User-Friendly and Flexible:
This script is designed to be highly customizable, giving you the ability to toggle which metrics to display and where they appear on the chart. You can easily focus on the data that matters most to your trading strategy.
Z-Score Probability IndicatorThis is the Z-Score Probability indicator. As many people like my original Z-Score indicator and have expressed more interest in the powers of the Z, I decided to make this indicator which shows additional powers of the Z-Score.
Z-Score is not only useful for measuring a ticker or any other variable’s distance from the mean, it is also useful to calculate general probability in a normal distribution set. Not only can it calculate probability in a dataset, but it can also calculate the variables within said dataset by using the Standard Deviation and the Mean of the dataset.
Using these 2 aspects of the Z-Score, you can, In principle, have an indicator that operates similar to Fibonacci retracement levels with the added bonus of being able to actually ascertain the realistic probability of said retracement.
Let’s take a look at an example:
This is a chart showing SPY on the daily timeframe. If we look at the current Z-Score level, we can see that SPY is pushing into the 2 to 3 Z-Score range. We can see two things from this:
1. We can see that a retracement to a Z-Score of 2 would correspond to a price of 425.26 based on the current dataset. And
2. We can see that the probability that SPY retraces to a Z-Score of 2 is around 0.9800 or 98%.
To take it one step further, we can look at the various other variables in the distribution. If we were to bet on SPY retracing back to -1 SDs, that would correspond to a price of around 397.15, with a probability of around 0.1600 or 16% (see image below):
Let’s say, we thought SPY would go to $440. Well, we can see that the probability SPY goes to 434.64 currently is pretty low. How do we know? Because the Z-Score table shows us the probability of values falling BELOW that Z-score level in the current distribution. So if we look at this example below:
We can see that 0.9998 or roughly 99% of values in the current SPY distribution will fall below 434.64. Thus, it may be unrealistic, at this point in time, to target said value.
So what is a Z-Score Table?
Well, I need to disclose/clarify that the Z-Score Table being displayed in this indicator does Z-Score probability a HUGE injustice. However, with the constraints what is realistic to fit into an indicator, I had to make it far more succinct. Let’s take a look at an actual Z-Score Table below:
Above is a look an the actual Z-Score table. How it works is you first identify you’re Z-Score and then find the corresponding value that relates to your score. The number displayed in the dataset represents the number of variables in the dataset/density distribution that fall BELOW that particular Z-score.
So, for example, if we have a Z-Score of -2.31, we can consult that table, go to the -2.3 then scroll across to the 0.01 to represent -2.31. We would see that this Z-Score corresponds to a 0.0104 probability zone (or essentially 1%) indicating that the majority of the variables in the distribution fall below that mean Z-score. In terms of tickers and stocks, that would mean it would theoretically be “overbought”.
So what does the indicator Z-Table tell us?
I have averaged out the data for the purposes of this indicator. However, you can also reference a manual Z-Table to get the exact probability for the current precise Z-Score. However, the reality is it doesn’t necessarily matter to be exact when it comes to tickers. The reason being, ticker’s are in constant flux, and by the time you identify that probability, the ticker will already be at a different level. So generalizations are okay in these circumstances, you just need to get the “gist” of where the distribution lies.
So how do I use the indicator?
Using the indicator is pretty straightforward. Once launched, you will see the current Z-Score of the ticker, the current levels based on the distribution and the summarized Z-Table.
The Z-Table will turn gray to indicate the zone the ticker is currently in. In this case, we can see that SPY currently is in the 2 SD Zone, meaning that 0.98 or 98% of the current dataset being shown falls below the price we are at:
When we launch the settings, we can see a few inputs.
Lookback Length: This determines the number of candles back we want to calculate the distribution for. It is defaulted to 75, but you can adjust it to whichever length you want.
SMA Length: The SMA is optional but defaults to on. If you want to see the smoothed trend of the Z-Score, this will do the trick. It does not need to be set to the same
length as the Z-Score lookback. Thus, if you want a more or less responsive SMA with, say, a larger dataset, then you can reduce the SMA length yourself.
Distribution Probability Fills: This simply colour codes the distribution zones / probability zones on the indicator.
Show Z-Table: This will display the summarized Z-Table.
Show SMA: As I indicated, the SMA is optional, you can toggle it on or off to see the overall Z-Score trend.
Concluding Remarks:
And that my friends is the Z-Score Probability Indicator.
I hope you all enjoy it and find it helpful. As always leave your comments, questions and suggestions below.
Safe trades to all and take care!
Double Top/Bottom Screener - Today Only v2 //@version=6
indicator("Double Top/Bottom Screener - Today Only", overlay=true, max_lines_count=500)
// Inputs
leftBars = input.int(5, "Left Bars")
rightBars = input.int(5, "Right Bars")
tolerance = input.float(0.02, "Max Difference (e.g., 0.02 for 2 cents)", step=0.01)
atrLength = input.int(14, "ATR Length for Normalized Distance", minval=1)
requiredPeaks = input.int(3, "Required Identical Peaks", minval=2, maxval=5)
// Declarations of persistent variables and arrays
var array resistanceLevels = array.new(0)
var array resistanceCounts = array.new(0)
var array supportLevels = array.new(0)
var array supportCounts = array.new(0)
var array resLines = array.new(0)
var array supLines = array.new(0)
var bool hasDoubleTop = false
var bool hasDoubleBottom = false
var float doubleTopLevel = na
var float doubleBottomLevel = na
var int todayStart = na
// Step 1: Identify Swing Highs/Lows
swingHigh = ta.pivothigh(high, leftBars, rightBars)
swingLow = ta.pivotlow(low, leftBars, rightBars)
// Today's premarket start (04:00 AM ET)
todayStart := timestamp(syminfo.timezone, year, month, dayofmonth, 4, 0, 0)
// Clear arrays and delete lines on the first bar or new day
if barstate.isfirst or (dayofmonth != dayofmonth and time >= todayStart)
// Delete all existing lines only if arrays are not empty
if array.size(resLines) > 0
for i = array.size(resLines) - 1 to 0
line.delete(array.get(resLines, i))
if array.size(supLines) > 0
for i = array.size(supLines) - 1 to 0
line.delete(array.get(supLines, i))
// Clear arrays
array.clear(resistanceLevels)
array.clear(supportLevels)
array.clear(resistanceCounts)
array.clear(supportCounts)
array.clear(resLines)
array.clear(supLines)
// Reset flags
hasDoubleTop := false
hasDoubleBottom := false
doubleTopLevel := na
doubleBottomLevel := na
// Add new swings only if today and after premarket
if not na(swingHigh) and time >= todayStart and dayofmonth == dayofmonth
bool isEqualHigh = false
int peakIndex = -1
float prevLevel = na
if array.size(resistanceLevels) > 0
for i = 0 to array.size(resistanceLevels) - 1
prevLevel := array.get(resistanceLevels, i)
if math.abs(swingHigh - prevLevel) <= tolerance
isEqualHigh := true
peakIndex := i
break
if isEqualHigh and peakIndex >= 0
array.set(resistanceCounts, peakIndex, array.get(resistanceCounts, peakIndex) + 1)
if array.get(resistanceCounts, peakIndex) == requiredPeaks
hasDoubleTop := true
doubleTopLevel := prevLevel
else
array.push(resistanceLevels, swingHigh)
array.push(resistanceCounts, 1)
line newResLine = line.new(bar_index - rightBars, swingHigh, bar_index, swingHigh, color=color.red, width=2, extend=extend.none)
array.push(resLines, newResLine)
if not na(swingLow) and time >= todayStart and dayofmonth == dayofmonth
bool isEqualLow = false
int peakIndex = -1
float prevLevel = na
if array.size(supportLevels) > 0
for i = 0 to array.size(supportLevels) - 1
prevLevel := array.get(supportLevels, i)
if math.abs(swingLow - prevLevel) <= tolerance
isEqualLow := true
peakIndex := i
break
if isEqualLow and peakIndex >= 0
array.set(supportCounts, peakIndex, array.get(supportCounts, peakIndex) + 1)
if array.get(supportCounts, peakIndex) == requiredPeaks
hasDoubleBottom := true
doubleBottomLevel := prevLevel
else
array.push(supportLevels, swingLow)
array.push(supportCounts, 1)
line newSupLine = line.new(bar_index - rightBars, swingLow, bar_index, swingLow, color=color.green, width=2, extend=extend.none)
array.push(supLines, newSupLine)
// Monitor and remove broken levels/lines; reset pattern if the equal level breaks
if array.size(resistanceLevels) > 0
for i = array.size(resistanceLevels) - 1 to 0
float level = array.get(resistanceLevels, i)
if close > level
line.delete(array.get(resLines, i))
array.remove(resLines, i)
array.remove(resistanceLevels, i)
array.remove(resistanceCounts, i)
if level == doubleTopLevel
hasDoubleTop := false
doubleTopLevel := na
if array.size(supportLevels) > 0
for i = array.size(supportLevels) - 1 to 0
float level = array.get(supportLevels, i)
if close < level
line.delete(array.get(supLines, i))
array.remove(supLines, i)
array.remove(supportLevels, i)
array.remove(supportCounts, i)
if level == doubleBottomLevel
hasDoubleBottom := false
doubleBottomLevel := na
// Limit arrays (after removals)
if array.size(resistanceLevels) > 10
line oldLine = array.shift(resLines)
line.delete(oldLine)
array.shift(resistanceLevels)
array.shift(resistanceCounts)
if array.size(supportLevels) > 10
line oldLine = array.shift(supLines)
line.delete(oldLine)
array.shift(supportLevels)
array.shift(supportCounts)
// Pattern Signal: 1 only if the exact required number of peaks is met
patternSignal = (hasDoubleTop or hasDoubleBottom) ? 1 : 0
// New: Nearest Double Level Price
var float nearestDoubleLevel = na
if hasDoubleTop and not na(doubleTopLevel)
nearestDoubleLevel := doubleTopLevel
if hasDoubleBottom and not na(doubleBottomLevel)
nearestDoubleLevel := na(nearestDoubleLevel) ? doubleBottomLevel : (math.abs(close - doubleBottomLevel) < math.abs(close - nearestDoubleLevel) ? doubleBottomLevel : nearestDoubleLevel)
// New: Distance to Nearest Level (using ATR for normalization)
var float atr = ta.atr(atrLength)
var float distanceNormalizedATR = na
if not na(nearestDoubleLevel) and not na(atr) and atr > 0
distanceNormalizedATR := math.abs(close - nearestDoubleLevel) / atr
// Optional Bounce Signal (for reference)
bounceSignal = 0
if array.size(resistanceLevels) > 0
for i = 0 to array.size(resistanceLevels) - 1
float level = array.get(resistanceLevels, i)
if low <= level and high >= level and close < level
bounceSignal := 1
if array.size(supportLevels) > 0
for i = 0 to array.size(supportLevels) - 1
float level = array.get(supportLevels, i)
if high >= level and low <= level and close > level
bounceSignal := 1
// Outputs
plot(patternSignal, title="Pattern Signal", color=patternSignal == 1 ? color.purple : na, style=plot.style_circles)
plot(bounceSignal, title="Bounce Signal", color=bounceSignal == 1 ? color.yellow : na, style=plot.style_circles)
plot(nearestDoubleLevel, title="Nearest Double Level Price", color=color.orange)
plot(distanceNormalizedATR, title="Normalized Distance (ATR)", color=color.green)
bgcolor(patternSignal == 1 ? color.new(color.purple, 80) : na)
if patternSignal == 1 and barstate.isconfirmed
alert("Double Pattern detected on " + syminfo.ticker + " at " + str.tostring(close), alert.freq_once_per_bar_close)
if barstate.islast
var table infoTable = table.new(position.top_right, 1, 4, bgcolor=color.new(color.black, 50))
table.cell(infoTable, 0, 0, "Pattern: " + str.tostring(patternSignal), bgcolor=patternSignal == 1 ? color.purple : color.gray)
table.cell(infoTable, 0, 1, "Bounce: " + str.tostring(bounceSignal), bgcolor=bounceSignal == 1 ? color.yellow : color.gray)
table.cell(infoTable, 0, 2, "Level: " + str.tostring(nearestDoubleLevel, "#.##"), bgcolor=color.orange)
table.cell(infoTable, 0, 3, "ATR Dist: " + str.tostring(distanceNormalizedATR, "#.##"), bgcolor=color.green)
Double Top/Bottom Screener - Today Only//@version=6
indicator("Double Top/Bottom Screener - Today Only", overlay=true, max_lines_count=500)
// Inputs
leftBars = input.int(5, "Left Bars")
rightBars = input.int(5, "Right Bars")
tolerance = input.float(0.02, "Max Difference (e.g., 0.02 for 2 cents)", step=0.01)
atrLength = input.int(14, "ATR Length for Normalized Distance", minval=1)
requiredPeaks = input.int(3, "Required Identical Peaks", minval=2, maxval=5)
// Declarations of persistent variables and arrays
var array resistanceLevels = array.new(0)
var array resistanceCounts = array.new(0)
var array supportLevels = array.new(0)
var array supportCounts = array.new(0)
var array resLines = array.new(0)
var array supLines = array.new(0)
var bool hasDoubleTop = false
var bool hasDoubleBottom = false
var float doubleTopLevel = na
var float doubleBottomLevel = na
var int todayStart = na
// Step 1: Identify Swing Highs/Lows
swingHigh = ta.pivothigh(high, leftBars, rightBars)
swingLow = ta.pivotlow(low, leftBars, rightBars)
// Today's premarket start (04:00 AM ET)
todayStart := timestamp(syminfo.timezone, year, month, dayofmonth, 4, 0, 0)
// Clear arrays and delete lines on the first bar or new day
if barstate.isfirst or (dayofmonth != dayofmonth and time >= todayStart)
// Delete all existing lines
for i = array.size(resLines) - 1 to 0
line.delete(array.get(resLines, i))
for i = array.size(supLines) - 1 to 0
line.delete(array.get(supLines, i))
// Clear arrays
array.clear(resistanceLevels)
array.clear(supportLevels)
array.clear(resistanceCounts)
array.clear(supportCounts)
array.clear(resLines)
array.clear(supLines)
// Reset flags
hasDoubleTop := false
hasDoubleBottom := false
doubleTopLevel := na
doubleBottomLevel := na
// Add new swings only if today and after premarket
if not na(swingHigh) and time >= todayStart and dayofmonth == dayofmonth
bool isEqualHigh = false
int peakIndex = -1
float prevLevel = na
if array.size(resistanceLevels) > 0
for i = 0 to array.size(resistanceLevels) - 1
prevLevel := array.get(resistanceLevels, i)
if math.abs(swingHigh - prevLevel) <= tolerance
isEqualHigh := true
peakIndex := i
break
if isEqualHigh and peakIndex >= 0
array.set(resistanceCounts, peakIndex, array.get(resistanceCounts, peakIndex) + 1)
if array.get(resistanceCounts, peakIndex) == requiredPeaks
hasDoubleTop := true
doubleTopLevel := prevLevel
else
array.push(resistanceLevels, swingHigh)
array.push(resistanceCounts, 1)
line newResLine = line.new(bar_index - rightBars, swingHigh, bar_index, swingHigh, color=color.red, width=2, extend=extend.none)
array.push(resLines, newResLine)
if not na(swingLow) and time >= todayStart and dayofmonth == dayofmonth
bool isEqualLow = false
int peakIndex = -1
float prevLevel = na
if array.size(supportLevels) > 0
for i = 0 to array.size(supportLevels) - 1
prevLevel := array.get(supportLevels, i)
if math.abs(swingLow - prevLevel) <= tolerance
isEqualLow := true
peakIndex := i
break
if isEqualLow and peakIndex >= 0
array.set(supportCounts, peakIndex, array.get(supportCounts, peakIndex) + 1)
if array.get(supportCounts, peakIndex) == requiredPeaks
hasDoubleBottom := true
doubleBottomLevel := prevLevel
else
array.push(supportLevels, swingLow)
array.push(supportCounts, 1)
line newSupLine = line.new(bar_index - rightBars, swingLow, bar_index, swingLow, color=color.green, width=2, extend=extend.none)
array.push(supLines, newSupLine)
// Monitor and remove broken levels/lines; reset pattern if the equal level breaks
if array.size(resistanceLevels) > 0
for i = array.size(resistanceLevels) - 1 to 0
float level = array.get(resistanceLevels, i)
if close > level
line.delete(array.get(resLines, i))
array.remove(resLines, i)
array.remove(resistanceLevels, i)
array.remove(resistanceCounts, i)
if level == doubleTopLevel
hasDoubleTop := false
doubleTopLevel := na
if array.size(supportLevels) > 0
for i = array.size(supportLevels) - 1 to 0
float level = array.get(supportLevels, i)
if close < level
line.delete(array.get(supLines, i))
array.remove(supLines, i)
array.remove(supportLevels, i)
array.remove(supportCounts, i)
if level == doubleBottomLevel
hasDoubleBottom := false
doubleBottomLevel := na
// Limit arrays (after removals)
if array.size(resistanceLevels) > 10
line oldLine = array.shift(resLines)
line.delete(oldLine)
array.shift(resistanceLevels)
array.shift(resistanceCounts)
if array.size(supportLevels) > 10
line oldLine = array.shift(supLines)
line.delete(oldLine)
array.shift(supportLevels)
array.shift(supportCounts)
// Pattern Signal: 1 only if the exact required number of peaks is met
patternSignal = (hasDoubleTop or hasDoubleBottom) ? 1 : 0
// New: Nearest Double Level Price
var float nearestDoubleLevel = na
if hasDoubleTop and not na(doubleTopLevel)
nearestDoubleLevel := doubleTopLevel
if hasDoubleBottom and not na(doubleBottomLevel)
nearestDoubleLevel := na(nearestDoubleLevel) ? doubleBottomLevel : (math.abs(close - doubleBottomLevel) < math.abs(close - nearestDoubleLevel) ? doubleBottomLevel : nearestDoubleLevel)
// New: Distance to Nearest Level (using ATR for normalization)
var float atr = ta.atr(atrLength)
var float distanceNormalizedATR = na
if not na(nearestDoubleLevel) and not na(atr) and atr > 0
distanceNormalizedATR := math.abs(close - nearestDoubleLevel) / atr
// Optional Bounce Signal (for reference)
bounceSignal = 0
if array.size(resistanceLevels) > 0
for i = 0 to array.size(resistanceLevels) - 1
float level = array.get(resistanceLevels, i)
if low <= level and high >= level and close < level
bounceSignal := 1
if array.size(supportLevels) > 0
for i = 0 to array.size(supportLevels) - 1
float level = array.get(supportLevels, i)
if high >= level and low <= level and close > level
bounceSignal := 1
// Outputs
plot(patternSignal, title="Pattern Signal", color=patternSignal == 1 ? color.purple : na, style=plot.style_circles)
plot(bounceSignal, title="Bounce Signal", color=bounceSignal == 1 ? color.yellow : na, style=plot.style_circles)
plot(nearestDoubleLevel, title="Nearest Double Level Price", color=color.orange)
plot(distanceNormalizedATR, title="Normalized Distance (ATR)", color=color.green)
bgcolor(patternSignal == 1 ? color.new(color.purple, 80) : na)
if patternSignal == 1 and barstate.isconfirmed
alert("Double Pattern detected on " + syminfo.ticker + " at " + str.tostring(close), alert.freq_once_per_bar_close)
if barstate.islast
var table infoTable = table.new(position.top_right, 1, 4, bgcolor=color.new(color.black, 50))
table.cell(infoTable, 0, 0, "Pattern: " + str.tostring(patternSignal), bgcolor=patternSignal == 1 ? color.purple : color.gray)
table.cell(infoTable, 0, 1, "Bounce: " + str.tostring(bounceSignal), bgcolor=bounceSignal == 1 ? color.yellow : color.gray)
table.cell(infoTable, 0, 2, "Level: " + str.tostring(nearestDoubleLevel, "#.##"), bgcolor=color.orange)
table.cell(infoTable, 0, 3, "ATR Dist: " + str.tostring(distanceNormalizedATR, "#.##"), bgcolor=color.green)
Double Top/Bottom Screener V1//@version=6
indicator("Double Top/Bottom Screener", overlay=true, max_lines_count=500)
// Inputs
leftBars = input.int(5, "Left Bars")
rightBars = input.int(5, "Right Bars")
tolerance = input.float(0.02, "Max Difference (e.g., 0.02 for 2 cents)", step=0.01)
atrLength = input.int(14, "ATR Length for Normalized Distance", minval=1)
requiredPeaks = input.int(3, "Required Identical Peaks", minval=2, maxval=5)
// Declarations of persistent variables and arrays
var array resistanceLevels = array.new(0)
var array resistanceCounts = array.new(0)
var array supportLevels = array.new(0)
var array supportCounts = array.new(0)
var array resLines = array.new(0)
var array supLines = array.new(0)
var bool hasDoubleTop = false
var bool hasDoubleBottom = false
var float doubleTopLevel = na
var float doubleBottomLevel = na
var int todayStart = na
var bool isNewDay = false
// Step 1: Identify Swing Highs/Lows
swingHigh = ta.pivothigh(high, leftBars, rightBars)
swingLow = ta.pivotlow(low, leftBars, rightBars)
// Today's premarket start (04:00 AM ET)
if dayofmonth != dayofmonth
todayStart := timestamp(syminfo.timezone, year, month, dayofmonth, 4, 0, 0)
isNewDay := true
else
isNewDay := false
// Clear arrays and reset flags only once at premarket start
if isNewDay and time >= todayStart
array.clear(resistanceLevels)
array.clear(supportLevels)
array.clear(resistanceCounts)
array.clear(supportCounts)
array.clear(resLines)
array.clear(supLines)
hasDoubleTop := false
hasDoubleBottom := false
doubleTopLevel := na
doubleBottomLevel := na
// Add new swings and check for identical peaks
if not na(swingHigh) and time >= todayStart
bool isEqualHigh = false
int peakIndex = -1
float prevLevel = na
if array.size(resistanceLevels) > 0
for i = 0 to array.size(resistanceLevels) - 1
prevLevel := array.get(resistanceLevels, i)
if math.abs(swingHigh - prevLevel) <= tolerance
isEqualHigh := true
peakIndex := i
break
if isEqualHigh and peakIndex >= 0
array.set(resistanceCounts, peakIndex, array.get(resistanceCounts, peakIndex) + 1)
if array.get(resistanceCounts, peakIndex) == requiredPeaks
hasDoubleTop := true
doubleTopLevel := prevLevel
else
array.push(resistanceLevels, swingHigh)
array.push(resistanceCounts, 1)
line newResLine = line.new(bar_index - rightBars, swingHigh, bar_index, swingHigh, color=color.red, width=2, extend=extend.right)
array.push(resLines, newResLine)
if not na(swingLow) and time >= todayStart
bool isEqualLow = false
int peakIndex = -1
float prevLevel = na
if array.size(supportLevels) > 0
for i = 0 to array.size(supportLevels) - 1
prevLevel := array.get(supportLevels, i)
if math.abs(swingLow - prevLevel) <= tolerance
isEqualLow := true
peakIndex := i
break
if isEqualLow and peakIndex >= 0
array.set(supportCounts, peakIndex, array.get(supportCounts, peakIndex) + 1)
if array.get(supportCounts, peakIndex) == requiredPeaks
hasDoubleBottom := true
doubleBottomLevel := prevLevel
else
array.push(supportLevels, swingLow)
array.push(supportCounts, 1)
line newSupLine = line.new(bar_index - rightBars, swingLow, bar_index, swingLow, color=color.green, width=2, extend=extend.right)
array.push(supLines, newSupLine)
// Monitor and remove broken levels/lines; reset pattern if the equal level breaks
if array.size(resistanceLevels) > 0
for i = array.size(resistanceLevels) - 1 to 0
float level = array.get(resistanceLevels, i)
if close > level
line.delete(array.get(resLines, i))
array.remove(resLines, i)
array.remove(resistanceLevels, i)
array.remove(resistanceCounts, i)
if level == doubleTopLevel
hasDoubleTop := false
doubleTopLevel := na
if array.size(supportLevels) > 0
for i = array.size(supportLevels) - 1 to 0
float level = array.get(supportLevels, i)
if close < level
line.delete(array.get(supLines, i))
array.remove(supLines, i)
array.remove(supportLevels, i)
array.remove(supportCounts, i)
if level == doubleBottomLevel
hasDoubleBottom := false
doubleBottomLevel := na
// Limit arrays (after removals)
if array.size(resistanceLevels) > 10
line oldLine = array.shift(resLines)
line.delete(oldLine)
array.shift(resistanceLevels)
array.shift(resistanceCounts)
if array.size(supportLevels) > 10
line oldLine = array.shift(supLines)
line.delete(oldLine)
array.shift(supportLevels)
array.shift(supportCounts)
// Pattern Signal: 1 only if the exact required number of peaks is met
patternSignal = (hasDoubleTop or hasDoubleBottom) and (array.size(resistanceCounts) > 0 and array.get(resistanceCounts, array.size(resistanceCounts) - 1) == requiredPeaks or array.size(supportCounts) > 0 and array.get(supportCounts, array.size(supportCounts) - 1) == requiredPeaks) ? 1 : 0
// New: Nearest Double Level Price
var float nearestDoubleLevel = na
if hasDoubleTop and not na(doubleTopLevel)
nearestDoubleLevel := doubleTopLevel
if hasDoubleBottom and not na(doubleBottomLevel)
nearestDoubleLevel := na(nearestDoubleLevel) ? doubleBottomLevel : (math.abs(close - doubleBottomLevel) < math.abs(close - nearestDoubleLevel) ? doubleBottomLevel : nearestDoubleLevel)
// New: Distance to Nearest Level (using ATR for normalization)
var float atr = ta.atr(atrLength)
var float distanceNormalizedATR = na
if not na(nearestDoubleLevel) and not na(atr) and atr > 0
distanceNormalizedATR := math.abs(close - nearestDoubleLevel) / atr
// Optional Bounce Signal (for reference)
bounceSignal = 0
if array.size(resistanceLevels) > 0
for i = 0 to array.size(resistanceLevels) - 1
float level = array.get(resistanceLevels, i)
if low <= level and high >= level and close < level
bounceSignal := 1
if array.size(supportLevels) > 0
for i = 0 to array.size(supportLevels) - 1
float level = array.get(supportLevels, i)
if high >= level and low <= level and close > level
bounceSignal := 1
// Outputs
plot(patternSignal, title="Pattern Signal", color=patternSignal == 1 ? color.purple : na, style=plot.style_circles)
plot(bounceSignal, title="Bounce Signal", color=bounceSignal == 1 ? color.yellow : na, style=plot.style_circles)
plot(nearestDoubleLevel, title="Nearest Double Level Price", color=color.orange)
plot(distanceNormalizedATR, title="Normalized Distance (ATR)", color=color.green)
bgcolor(patternSignal == 1 ? color.new(color.purple, 80) : na)
if patternSignal == 1 and barstate.isconfirmed
alert("Double Pattern detected on " + syminfo.ticker + " at " + str.tostring(close), alert.freq_once_per_bar_close)
if barstate.islast
var table infoTable = table.new(position.top_right, 1, 4, bgcolor=color.new(color.black, 50))
table.cell(infoTable, 0, 0, "Pattern: " + str.tostring(patternSignal), bgcolor=patternSignal == 1 ? color.purple : color.gray)
table.cell(infoTable, 0, 1, "Bounce: " + str.tostring(bounceSignal), bgcolor=bounceSignal == 1 ? color.yellow : color.gray)
table.cell(infoTable, 0, 2, "Level: " + str.tostring(nearestDoubleLevel, "#.##"), bgcolor=color.orange)
table.cell(infoTable, 0, 3, "ATR Dist: " + str.tostring(distanceNormalizedATR, "#.##"), bgcolor=color.green)
Double Top/Bottom Screener 3-5 peaks //@version=6
indicator("Double Top/Bottom Screener", overlay=true, max_lines_count=500)
// Inputs
leftBars = input.int(5, "Left Bars")
rightBars = input.int(5, "Right Bars")
tolerance = input.float(0.02, "Max Difference (e.g., 0.02 for 2 cents)", step=0.01)
atrLength = input.int(14, "ATR Length for Normalized Distance", minval=1)
maxPeaks = input.int(5, "Max Identical Peaks", minval=2, maxval=5)
// Declarations of persistent variables and arrays
var array resistanceLevels = array.new(0)
var array resistanceCounts = array.new(0)
var array supportLevels = array.new(0)
var array supportCounts = array.new(0)
var array resLines = array.new(0)
var array supLines = array.new(0)
var bool hasDoubleTop = false
var bool hasDoubleBottom = false
var float doubleTopLevel = na
var float doubleBottomLevel = na
var int todayStart = na
var bool isNewDay = false
// Step 1: Identify Swing Highs/Lows
swingHigh = ta.pivothigh(high, leftBars, rightBars)
swingLow = ta.pivotlow(low, leftBars, rightBars)
// Today's premarket start (04:00 AM ET)
if dayofmonth != dayofmonth
todayStart := timestamp(syminfo.timezone, year, month, dayofmonth, 4, 0, 0)
isNewDay := true
else
isNewDay := false
// Clear arrays and reset flags only once at premarket start
if isNewDay and time >= todayStart
array.clear(resistanceLevels)
array.clear(supportLevels)
array.clear(resistanceCounts)
array.clear(supportCounts)
array.clear(resLines)
array.clear(supLines)
hasDoubleTop := false
hasDoubleBottom := false
doubleTopLevel := na
doubleBottomLevel := na
// Add new swings and check for identical peaks
if not na(swingHigh) and time >= todayStart
bool isEqualHigh = false
int peakIndex = -1
float prevLevel = na // Declare prevLevel with initial value
if array.size(resistanceLevels) > 0
for i = 0 to array.size(resistanceLevels) - 1
prevLevel := array.get(resistanceLevels, i)
if math.abs(swingHigh - prevLevel) <= tolerance
isEqualHigh := true
peakIndex := i
break
if isEqualHigh and peakIndex >= 0
array.set(resistanceCounts, peakIndex, array.get(resistanceCounts, peakIndex) + 1)
if array.get(resistanceCounts, peakIndex) >= maxPeaks
hasDoubleTop := true
doubleTopLevel := prevLevel
else
array.push(resistanceLevels, swingHigh)
array.push(resistanceCounts, 1)
line newResLine = line.new(bar_index - rightBars, swingHigh, bar_index, swingHigh, color=color.red, width=2, extend=extend.right)
array.push(resLines, newResLine)
if not na(swingLow) and time >= todayStart
bool isEqualLow = false
int peakIndex = -1
float prevLevel = na // Declare prevLevel with initial value
if array.size(supportLevels) > 0
for i = 0 to array.size(supportLevels) - 1
prevLevel := array.get(supportLevels, i)
if math.abs(swingLow - prevLevel) <= tolerance
isEqualLow := true
peakIndex := i
break
if isEqualLow and peakIndex >= 0
array.set(supportCounts, peakIndex, array.get(supportCounts, peakIndex) + 1)
if array.get(supportCounts, peakIndex) >= maxPeaks
hasDoubleBottom := true
doubleBottomLevel := prevLevel
else
array.push(supportLevels, swingLow)
array.push(supportCounts, 1)
line newSupLine = line.new(bar_index - rightBars, swingLow, bar_index, swingLow, color=color.green, width=2, extend=extend.right)
array.push(supLines, newSupLine)
// Monitor and remove broken levels/lines; reset pattern if the equal level breaks
if array.size(resistanceLevels) > 0
for i = array.size(resistanceLevels) - 1 to 0
float level = array.get(resistanceLevels, i)
if close > level
line.delete(array.get(resLines, i))
array.remove(resLines, i)
array.remove(resistanceLevels, i)
array.remove(resistanceCounts, i)
if level == doubleTopLevel
hasDoubleTop := false
doubleTopLevel := na
if array.size(supportLevels) > 0
for i = array.size(supportLevels) - 1 to 0
float level = array.get(supportLevels, i)
if close < level
line.delete(array.get(supLines, i))
array.remove(supLines, i)
array.remove(supportLevels, i)
array.remove(supportCounts, i)
if level == doubleBottomLevel
hasDoubleBottom := false
doubleBottomLevel := na
// Limit arrays (after removals)
if array.size(resistanceLevels) > 10
line oldLine = array.shift(resLines)
line.delete(oldLine)
array.shift(resistanceLevels)
array.shift(resistanceCounts)
if array.size(supportLevels) > 10
line oldLine = array.shift(supLines)
line.delete(oldLine)
array.shift(supportLevels)
array.shift(supportCounts)
// Pattern Signal: 1 if any pattern with maxPeaks is active and unbroken
patternSignal = (hasDoubleTop or hasDoubleBottom) ? 1 : 0
// New: Nearest Double Level Price
var float nearestDoubleLevel = na
if hasDoubleTop and not na(doubleTopLevel)
nearestDoubleLevel := doubleTopLevel
if hasDoubleBottom and not na(doubleBottomLevel)
nearestDoubleLevel := na(nearestDoubleLevel) ? doubleBottomLevel : (math.abs(close - doubleBottomLevel) < math.abs(close - nearestDoubleLevel) ? doubleBottomLevel : nearestDoubleLevel)
// New: Distance to Nearest Level (using ATR for normalization)
var float atr = ta.atr(atrLength)
var float distanceNormalizedATR = na
if not na(nearestDoubleLevel) and not na(atr) and atr > 0
distanceNormalizedATR := math.abs(close - nearestDoubleLevel) / atr
// Optional Bounce Signal (for reference)
bounceSignal = 0
if array.size(resistanceLevels) > 0
for i = 0 to array.size(resistanceLevels) - 1
float level = array.get(resistanceLevels, i)
if low <= level and high >= level and close < level
bounceSignal := 1
if array.size(supportLevels) > 0
for i = 0 to array.size(supportLevels) - 1
float level = array.get(supportLevels, i)
if high >= level and low <= level and close > level
bounceSignal := 1
// Outputs
plot(patternSignal, title="Pattern Signal", color=patternSignal == 1 ? color.purple : na, style=plot.style_circles)
plot(bounceSignal, title="Bounce Signal", color=bounceSignal == 1 ? color.yellow : na, style=plot.style_circles)
plot(nearestDoubleLevel, title="Nearest Double Level Price", color=color.orange)
plot(distanceNormalizedATR, title="Normalized Distance (ATR)", color=color.green)
bgcolor(patternSignal == 1 ? color.new(color.purple, 80) : na)
if patternSignal == 1 and barstate.isconfirmed
alert("Double Pattern detected on " + syminfo.ticker + " at " + str.tostring(close), alert.freq_once_per_bar_close)
if barstate.islast
var table infoTable = table.new(position.top_right, 1, 4, bgcolor=color.new(color.black, 50))
table.cell(infoTable, 0, 0, "Pattern: " + str.tostring(patternSignal), bgcolor=patternSignal == 1 ? color.purple : color.gray)
table.cell(infoTable, 0, 1, "Bounce: " + str.tostring(bounceSignal), bgcolor=bounceSignal == 1 ? color.yellow : color.gray)
table.cell(infoTable, 0, 2, "Level: " + str.tostring(nearestDoubleLevel, "#.##"), bgcolor=color.orange)
table.cell(infoTable, 0, 3, "ATR Dist: " + str.tostring(distanceNormalizedATR, "#.##"), bgcolor=color.green)
iD EMARSI on ChartSCRIPT OVERVIEW
The EMARSI indicator is an advanced technical analysis tool that maps RSI values directly onto price charts. With adaptive scaling capabilities, it provides a unique visualization of momentum that flows naturally with price action, making it particularly valuable for FOREX and low-priced securities trading.
KEY FEATURES
1 PRICE MAPPED RSI VISUALIZATION
Unlike traditional RSI that displays in a separate window, EMARSI plots the RSI directly on the price chart, creating a flowing line that identifies momentum shifts within the context of price action:
// Map RSI to price chart with better scaling
mappedRsi = useAdaptiveScaling ?
median + ((rsi - 50) / 50 * (pQH - pQL) / 2 * math.min(1.0, 1/scalingFactor)) :
down == pQL ? pQH : up == pQL ? pQL : median - (median / (1 + up / down))
2 ADAPTIVE SCALING SYSTEM
The script features an intelligent scaling system that automatically adjusts to different market conditions and price levels:
// Calculate adaptive scaling factor based on selected method
scalingFactor = if scalingMethod == "ATR-Based"
math.min(maxScalingFactor, math.max(1.0, minTickSize / (atrValue/avgPrice)))
else if scalingMethod == "Price-Based"
math.min(maxScalingFactor, math.max(1.0, math.sqrt(100 / math.max(avgPrice, 0.01))))
else // Volume-Based
math.min(maxScalingFactor, math.max(1.0, math.sqrt(1000000 / math.max(volume, 100))))
3 MODIFIED RSI CALCULATION
EMARSI uses a specially formulated RSI calculation that works with an adaptive base value to maintain consistency across different price ranges:
// Adaptive RSI Base based on price levels to improve flow
adaptiveRsiBase = useAdaptiveScaling ? rsiBase * scalingFactor : rsiBase
// Calculate RSI components with adaptivity
up = ta.rma(math.max(ta.change(rsiSourceInput), adaptiveRsiBase), emaSlowLength)
down = ta.rma(-math.min(ta.change(rsiSourceInput), adaptiveRsiBase), rsiLengthInput)
// Improved RSI calculation with value constraint
rsi = down == 0 ? 100 : up == 0 ? 0 : 100 - (100 / (1 + up / down))
4 MOVING AVERAGE CROSSOVER SYSTEM
The indicator creates a smooth moving average of the RSI line, enabling a crossover system that generates trading signals:
// Calculate MA of mapped RSI
rsiMA = ma(mappedRsi, emaSlowLength, maTypeInput)
// Strategy entries
if ta.crossover(mappedRsi, rsiMA)
strategy.entry("RSI Long", strategy.long)
if ta.crossunder(mappedRsi, rsiMA)
strategy.entry("RSI Short", strategy.short)
5 VISUAL REFERENCE FRAMEWORK
The script includes visual guides that help interpret the RSI movement within the context of recent price action:
// Calculate pivot high and low
pQH = ta.highest(high, hlLen)
pQL = ta.lowest(low, hlLen)
median = (pQH + pQL) / 2
// Plotting
plot(pQH, "Pivot High", color=color.rgb(82, 228, 102, 90))
plot(pQL, "Pivot Low", color=color.rgb(231, 65, 65, 90))
med = plot(median, style=plot.style_steplinebr, linewidth=1, color=color.rgb(238, 101, 59, 90))
6 DYNAMIC COLOR SYSTEM
The indicator uses color fills to clearly visualize the relationship between the RSI and its moving average:
// Color fills based on RSI vs MA
colUp = mappedRsi > rsiMA ? input.color(color.rgb(128, 255, 0), '', group= 'RSI > EMA', inline= 'up') :
input.color(color.rgb(240, 9, 9, 95), '', group= 'RSI < EMA', inline= 'dn')
colDn = mappedRsi > rsiMA ? input.color(color.rgb(0, 230, 35, 95), '', group= 'RSI > EMA', inline= 'up') :
input.color(color.rgb(255, 47, 0), '', group= 'RSI < EMA', inline= 'dn')
fill(rsiPlot, emarsi, mappedRsi > rsiMA ? pQH : rsiMA, mappedRsi > rsiMA ? rsiMA : pQL, colUp, colDn)
7 REAL TIME PARAMETER MONITORING
A transparent information panel provides real-time feedback on the adaptive parameters being applied:
// Information display
var table infoPanel = table.new(position.top_right, 2, 3, bgcolor=color.rgb(0, 0, 0, 80))
if barstate.islast
table.cell(infoPanel, 0, 0, "Current Scaling Factor", text_color=color.white)
table.cell(infoPanel, 1, 0, str.tostring(scalingFactor, "#.###"), text_color=color.white)
table.cell(infoPanel, 0, 1, "Adaptive RSI Base", text_color=color.white)
table.cell(infoPanel, 1, 1, str.tostring(adaptiveRsiBase, "#.####"), text_color=color.white)
BENEFITS FOR TRADERS
INTUITIVE MOMENTUM VISUALIZATION
By mapping RSI directly onto the price chart, traders can immediately see the relationship between momentum and price without switching between different indicator windows.
ADAPTIVE TO ANY MARKET CONDITION
The three scaling methods (ATR-Based, Price-Based, and Volume-Based) ensure the indicator performs consistently across different market conditions, volatility regimes, and price levels.
PREVENTS EXTREME VALUES
The adaptive scaling system prevents the RSI from generating extreme values that exceed chart boundaries when trading low-priced securities or during high volatility periods.
CLEAR TRADING SIGNALS
The RSI and moving average crossover system provides clear entry signals that are visually reinforced through color changes, making it easy to identify potential trading opportunities.
SUITABLE FOR MULTIPLE TIMEFRAMES
The indicator works effectively across multiple timeframes, from intraday to daily charts, making it versatile for different trading styles and strategies.
TRANSPARENT PARAMETER ADJUSTMENT
The information panel provides real-time feedback on how the adaptive system is adjusting to current market conditions, helping traders understand why the indicator is behaving as it is.
CUSTOMIZABLE VISUALIZATION
Multiple visualization options including Bollinger Bands, different moving average types, and customizable colors allow traders to adapt the indicator to their personal preferences.
CONCLUSION
The EMARSI indicator represents a significant advancement in RSI visualization by directly mapping momentum onto price charts with adaptive scaling. This approach makes momentum shifts more intuitive to identify and helps prevent the scaling issues that commonly affect RSI-based indicators when applied to low-priced securities or volatile markets.
MTF Volume Flow IndicatorThe MTF Volume Flow Indicator (MTF VFI) is an advanced and versatile tool that enhances market analysis by tracking the flow of volume across multiple timeframes. By integrating volume flow with multi-timeframe analysis, this indicator provides traders with a comprehensive understanding of market trends, momentum, and potential reversals.
Key Features
Multi-Timeframe Volume Flow Analysis: The MTF VFI computes the Volume Flow Indicator across various timeframes, ranging from 1 minute to 1 month. This multi-timeframe analysis enables traders to observe and compare volume flow dynamics across different time horizons, offering deeper insights into market behavior.
Customizable VFI Settings: The indicator includes configurable VFI parameters such as length, coefficient, and volume cutoff, allowing users to tailor the analysis to different market conditions and trading strategies. This flexibility ensures that the indicator remains relevant across diverse market environments.
Signal Line and Delta Calculations: The script features a signal line derived from the VFI and calculates the delta values (the difference between VFI and the signal line). These delta values are essential for identifying potential buy or sell signals and are presented as histograms for easy visual interpretation.
Cumulative Delta with Dynamic Bands: The indicator introduces cumulative delta, a powerful tool that combines average and median VFI values to provide a clearer picture of market sentiment. Two standard deviation bands are plotted around the cumulative delta, offering a range within which price movements are likely to remain. These bands are smoothed using a 21-period EMA, providing a more refined view of market volatility.
Multi-Timeframe and Analysis Tables: The MTF VFI includes optional tables that display VFI, signal line, and delta values across all selected timeframes. Additionally, an analysis table presents key statistical metrics such as the highest, lowest, average, standard deviation, range, and median VFI values. These tables provide a concise summary of market conditions, aiding in strategic decision-making.
Dynamic Display Options: The indicator offers extensive customization options, allowing traders to display or hide elements such as delta histograms, delta bands, and tables. This ensures that users can focus on the most relevant information for their trading strategy.
Neutral Candle Coloring Option: Traders can enable neutral candle colors, where bearish candles are gray and bullish candles are white. This feature helps to reduce noise and maintain focus on the overall trend and volume flow analysis.
How It Works
Volume Flow Indicator Calculation: The VFI is calculated using a combination of typical price, volume, and the standard deviation of price changes. The indicator smooths the VFI based on user preferences, allowing traders to adjust the sensitivity of the analysis to better match their trading style.
Multi-Timeframe Integration: The script pulls VFI calculations from multiple timeframes, providing a holistic view of market trends. By analyzing VFI across different timeframes, traders can detect alignments or divergences in volume flow that might indicate trend strength or weakness.
Cumulative Delta and Dynamic Bands: The cumulative delta is computed by combining the average and median VFI values. Dynamic two-standard-deviation bands are plotted around this cumulative delta, providing upper and lower bounds for expected price movements. These bands are further smoothed with a 21-period EMA, enhancing their effectiveness in volatile markets.
Delta Analysis and Histogram Display: The difference between the VFI and its signal line (delta) is calculated and displayed as histograms. This visual representation helps traders quickly assess momentum and identify potential reversals or trend continuations. The cumulative delta is color-coded dynamically based on its direction, adding an extra layer of visual clarity.
Alerts
VFI Crossover Alerts: The indicator includes customizable alerts that notify traders when the VFI crosses above or below its signal line. These alerts are crucial for catching potential trend reversals or continuation signals, even when the trader is not actively monitoring the chart.
Customizable Alert Conditions: Traders can tailor alert conditions to their preferred timeframes and VFI settings, ensuring that the notifications they receive are relevant and timely for their specific trading strategies.
Application
Trend Identification and Confirmation: The MTF VFI aids in identifying and confirming trends by analyzing volume flow across multiple timeframes. This capability is particularly useful for detecting trends that may not be visible on a single timeframe.
Momentum and Divergence Analysis: By comparing VFI and delta values across timeframes, and analyzing cumulative delta with dynamic bands, traders can gain insights into market momentum and potential divergences, which are often precursors to reversals.
Strategic Decision-Making: With its comprehensive multi-timeframe analysis, cumulative delta, and statistical summaries, the MTF VFI equips traders with the information needed to make informed trading decisions, whether for short-term trades or long-term investments.
Visual Clarity and Customization: The indicator’s dynamic display options and neutral candle coloring help traders maintain a clear and focused view of the market, customizing the visualization to match their specific needs.
The MTF Volume Flow Indicator (MTF VFI) by CryptoSea is an essential tool for traders who seek to gain a deeper understanding of market trends and volume dynamics across multiple timeframes. Its advanced features and customization options make it a valuable addition to any trader’s toolkit.
Bias AnalyzerName: Bias Analyzer
Category: Market Analyzer
Timeframe: 1H and 1D, depending on the Analysis type.
Technical Analysis: Usually when we think about a Trading System we start from an idea. This idea comes normally from observation and the study of the market.
Have we ever observed a market - for example Bitcoin - and thought that it increases at the start of a USA session? Great, this is a well-known category of Trading System and the purpose of the Bias Analyzer is to study these phenomena.
There are different types of Trading System that we can classify considering the market in-efficiency that we use to our advantage. In this case we make the Bias. Literally "inclination" or "presupposition" or precisely "tendency" of the price to go up or down in a temporal way.
The characteristics of the Bias depend on how much the Bias is persistent on the market since the analysed period. therefore we can consider:
Hourly Bias : analysing the hourly behaviours during the week. Trades normally last from a few hours to a few days.
Seasonal Bias : analysing the behaviour of the weeks in the monthly or annual context, evaluating the seasons.
Suggested usage: The possibilities of the tool are infinite, these are some scenarios of use:
Development of Intraday Trading Systems based on Hourly Bias with possible filters for specific days of the week.
Development of a Multi-day Trading System based on daily Bias with monthly analysis.
To identify the best day to execute our investment through Dollar Cost Average with a bit of healthy buy the dip
Main features:
Hourly Summary organized in Week
The cells contain the sum of the various price deltas for the single hour. The transparency indicates the frequency in which the candles close positive or negative. This information is available both in a synthetic way, as in the first column "Sum", and for each day of the week.
Hourly Details organized in different entry/exit
Shows the cumulative data of the various deltas, considering the purchase and the sale at certain times. In the rows are represented the buying hours and in the columns the selling hours.
Daily Summary organized in Months
The cells contain the summation of the various price deltas for the single day.
Hourly Details organized in different entry/exit
Allows to visualise the detailed analysis table, choosing to do it for all the months or for a specific month and shows the cumulative data of the various deltas, considering the purchase and the sale in certain days.
Configuration: You can configure the tool easily and completely.
Analysis
Calculate from Close to Open : this is the core of the whole analysis where the "Price Delta" to be calculated is defined. At this moment there is the possibility to calculate the distance between opening and closing.
Calculate in Percent or Cash : this allows to calculate the Price Delta in Percent or in Cash.
Analysis on 1H Timeframe
Show Hourly Summary on : allows to visualise the summary analysis table of the week. The cells contain the sum of the various price deltas for the single hour. The transparency indicates the frequency in which the candles close positive or negative. This information is available both in a synthetic way, as in the first column "Sum", and for each day of the week. At the bottom left there is also data which allows us to understand how many candles are being analysed. At the bottom of each day it is possible to visualise the cumulative data of the day. The position of the table is customizable.
Show Hourly Details of on : allows to visualise the detailed analysis table, choosing to do it for all days or for a specific day, and shows the cumulative data of the various deltas, considering the purchase and the sale at certain times. In the rows are represented the buying hours and in the columns the selling hours. For example, going to the table "All Days" we can see in the cell of row 13 and in column 22 the cumulative data of a possible buy on 13 and a sell at the end of 22. To facilitate the research of the values there is a configurable transparency system.
Analysis on 1D Timeframe
Show Daily Summary on : allows to visualise the summary analysis table of the month. The cells contain the summation of the various price deltas for the single day: The first row is the summation of all days of the month for all months in the analysis period, while the other rows represent the analysis for the various days of the individual months.
Show Daily Details of on : allows to visualise the detailed analysis table, choosing to do it for all the months or for a specific month and shows the cumulative data of the various deltas, considering the purchase and the sale in certain days. In the rows are represented the buying days and in the columns the selling days. For example, going to the table "All Months" we can see in the cell at row 1 and at column 3 the cumulative of a possible purchase on the 1st and the sale on the 3rd. To facilitate the research of the values, there is a configurable transparency system.
Table Layout
Size : allows to define the size of the text in the table.
Precision : it is possible to define the decimal precision of the calculations presented in the tables.
Transparency Factor : allows the application of a multiplication factor when the table calculates the transparency of detail tables.
Colours : allows to specify the colours of Profit, Loss and Neutral, besides to adapt a style coherent with the Dark Mode or Light Mode of Trading View
Volatility Filter
It is possible to directly apply a filter to the time series on which the delta is calculated. The volatility filter uses the ATR - an indicator that allows you to calculate the volatility in a given period. Briefly: the higher the ATR value, the higher the volatility. Therefore the filter works by comparing the volatility on two periods and indicates compression or expansion.
Backtest Dates
In order to facilitate the identification of in-sample and out-of-sample data, as well as the degradation of a given behaviour, it is possible to specify a period in which to do the analysis.
Double Top/Bottom Screener - Today Only V4//@version=6
indicator("Double Top/Bottom Screener - Today Only", overlay=true, max_lines_count=500)
// Inputs
leftBars = input.int(5, "Left Bars")
rightBars = input.int(5, "Right Bars")
tolerance = input.float(0.02, "Max Difference (e.g., 0.02 for 2 cents)", step=0.01)
atrLength = input.int(14, "ATR Length for Normalized Distance", minval=1)
requiredPeaks = input.int(3, "Required Identical Peaks", minval=2, maxval=5)
// Declarations of persistent variables and arrays
var array resistanceLevels = array.new(0)
var array resistanceCounts = array.new(0)
var array supportLevels = array.new(0)
var array supportCounts = array.new(0)
var array resLines = array.new(0)
var array supLines = array.new(0)
var bool hasDoubleTop = false
var bool hasDoubleBottom = false
var float doubleTopLevel = na
var float doubleBottomLevel = na
var int todayStart = na
var float nearestDoubleLevel = na // Explicitly declared as na by default
// Step 1: Identify Swing Highs/Lows
swingHigh = ta.pivothigh(high, leftBars, rightBars)
swingLow = ta.pivotlow(low, leftBars, rightBars)
// Today's premarket start (04:00 AM ET)
todayStart := timestamp(syminfo.timezone, year, month, dayofmonth, 4, 0, 0)
// Clear arrays and delete lines on the first bar or new day
if barstate.isfirst or (dayofmonth != dayofmonth and time >= todayStart)
// Delete all existing lines only if arrays are not empty
if array.size(resLines) > 0
for i = array.size(resLines) - 1 to 0
line.delete(array.get(resLines, i))
if array.size(supLines) > 0
for i = array.size(supLines) - 1 to 0
line.delete(array.get(supLines, i))
// Clear arrays
array.clear(resistanceLevels)
array.clear(supportLevels)
array.clear(resistanceCounts)
array.clear(supportCounts)
array.clear(resLines)
array.clear(supLines)
// Reset flags and levels
hasDoubleTop := false
hasDoubleBottom := false
doubleTopLevel := na
doubleBottomLevel := na
nearestDoubleLevel := na // Ensure reset on new day
// Add new swings only if today and after premarket
if not na(swingHigh) and time >= todayStart and dayofmonth == dayofmonth
bool isEqualHigh = false
int peakIndex = -1
float prevLevel = na
if array.size(resistanceLevels) > 0
for i = 0 to array.size(resistanceLevels) - 1
prevLevel := array.get(resistanceLevels, i)
if math.abs(swingHigh - prevLevel) <= tolerance
isEqualHigh := true
peakIndex := i
break
if isEqualHigh and peakIndex >= 0
array.set(resistanceCounts, peakIndex, array.get(resistanceCounts, peakIndex) + 1)
if array.get(resistanceCounts, peakIndex) == requiredPeaks
hasDoubleTop := true
doubleTopLevel := prevLevel
else
array.push(resistanceLevels, swingHigh)
array.push(resistanceCounts, 1)
line newResLine = line.new(bar_index - rightBars, swingHigh, bar_index, swingHigh, color=color.red, width=2, extend=extend.none)
array.push(resLines, newResLine)
if not na(swingLow) and time >= todayStart and dayofmonth == dayofmonth
bool isEqualLow = false
int peakIndex = -1
float prevLevel = na
if array.size(supportLevels) > 0
for i = 0 to array.size(supportLevels) - 1
prevLevel := array.get(supportLevels, i)
if math.abs(swingLow - prevLevel) <= tolerance
isEqualLow := true
peakIndex := i
break
if isEqualLow and peakIndex >= 0
array.set(supportCounts, peakIndex, array.get(supportCounts, peakIndex) + 1)
if array.get(supportCounts, peakIndex) == requiredPeaks
hasDoubleBottom := true
doubleBottomLevel := prevLevel
else
array.push(supportLevels, swingLow)
array.push(supportCounts, 1)
line newSupLine = line.new(bar_index - rightBars, swingLow, bar_index, swingLow, color=color.green, width=2, extend=extend.none)
array.push(supLines, newSupLine)
// Monitor and remove broken levels/lines; reset pattern if the equal level breaks
if array.size(resistanceLevels) > 0
for i = array.size(resistanceLevels) - 1 to 0
float level = array.get(resistanceLevels, i)
if close > level
line.delete(array.get(resLines, i))
array.remove(resLines, i)
array.remove(resistanceLevels, i)
array.remove(resistanceCounts, i)
if level == doubleTopLevel
hasDoubleTop := false
doubleTopLevel := na
nearestDoubleLevel := na // Reset if level breaks
if array.size(supportLevels) > 0
for i = array.size(supportLevels) - 1 to 0
float level = array.get(supportLevels, i)
if close < level
line.delete(array.get(supLines, i))
array.remove(supLines, i)
array.remove(supportLevels, i)
array.remove(supportCounts, i)
if level == doubleBottomLevel
hasDoubleBottom := false
doubleBottomLevel := na
nearestDoubleLevel := na // Reset if level breaks
// Limit arrays (after removals)
if array.size(resistanceLevels) > 10
line oldLine = array.shift(resLines)
line.delete(oldLine)
array.shift(resistanceLevels)
array.shift(resistanceCounts)
if array.size(supportLevels) > 10
line oldLine = array.shift(supLines)
line.delete(oldLine)
array.shift(supportLevels)
array.shift(supportCounts)
// Pattern Signal: 1 only if the exact required number of peaks is met
patternSignal = (hasDoubleTop or hasDoubleBottom) ? 1 : 0
// New: Nearest Double Level Price - Only update if pattern is active today and on current day
if time >= todayStart and dayofmonth == dayofmonth // Restrict to today
if patternSignal == 1 // Only set if pattern is active
if hasDoubleTop and not na(doubleTopLevel)
nearestDoubleLevel := doubleTopLevel
if hasDoubleBottom and not na(doubleBottomLevel)
nearestDoubleLevel := na(nearestDoubleLevel) ? doubleBottomLevel : (math.abs(close - doubleBottomLevel) < math.abs(close - nearestDoubleLevel) ? doubleBottomLevel : nearestDoubleLevel)
else
nearestDoubleLevel := na // Reset to na if no pattern today
else
nearestDoubleLevel := na // Reset for all historical bars
// New: Distance to Nearest Level (using ATR for normalization)
var float atr = ta.atr(atrLength)
var float distanceNormalizedATR = na
if not na(nearestDoubleLevel) and not na(atr) and atr > 0
distanceNormalizedATR := math.abs(close - nearestDoubleLevel) / atr
// Outputs
plot(patternSignal, title="Pattern Signal", color=patternSignal == 1 ? color.purple : na, style=plot.style_circles)
plot(nearestDoubleLevel, title="Nearest Double Level Price", color=color.orange)
plot(distanceNormalizedATR, title="Normalized Distance (ATR)", color=color.green)
bgcolor(patternSignal == 1 ? color.new(color.purple, 80) : na)
if patternSignal == 1 and barstate.isconfirmed
alert("Double Pattern detected on " + syminfo.ticker + " at " + str.tostring(close), alert.freq_once_per_bar_close)
if barstate.islast
var table infoTable = table.new(position.top_right, 1, 3, bgcolor=color.new(color.black, 50))
table.cell(infoTable, 0, 0, "Pattern: " + str.tostring(patternSignal), bgcolor=patternSignal == 1 ? color.purple : color.gray)
table.cell(infoTable, 0, 1, "Level: " + str.tostring(nearestDoubleLevel, "#.##"), bgcolor=color.orange)
table.cell(infoTable, 0, 2, "ATR Dist: " + str.tostring(distanceNormalizedATR, "#.##"), bgcolor=color.green)
Double Top/Bottom Screener - Today Only v3 //@version=6
indicator("Double Top/Bottom Screener - Today Only", overlay=true, max_lines_count=500)
// Inputs
leftBars = input.int(5, "Left Bars")
rightBars = input.int(5, "Right Bars")
tolerance = input.float(0.02, "Max Difference (e.g., 0.02 for 2 cents)", step=0.01)
atrLength = input.int(14, "ATR Length for Normalized Distance", minval=1)
requiredPeaks = input.int(3, "Required Identical Peaks", minval=2, maxval=5)
// Declarations of persistent variables and arrays
var array resistanceLevels = array.new(0)
var array resistanceCounts = array.new(0)
var array supportLevels = array.new(0)
var array supportCounts = array.new(0)
var array resLines = array.new(0)
var array supLines = array.new(0)
var bool hasDoubleTop = false
var bool hasDoubleBottom = false
var float doubleTopLevel = na
var float doubleBottomLevel = na
var int todayStart = na
var float nearestDoubleLevel = na // Explicitly declared as na by default
// Step 1: Identify Swing Highs/Lows
swingHigh = ta.pivothigh(high, leftBars, rightBars)
swingLow = ta.pivotlow(low, leftBars, rightBars)
// Today's premarket start (04:00 AM ET)
todayStart := timestamp(syminfo.timezone, year, month, dayofmonth, 4, 0, 0)
// Clear arrays and delete lines on the first bar or new day
if barstate.isfirst or (dayofmonth != dayofmonth and time >= todayStart)
// Delete all existing lines only if arrays are not empty
if array.size(resLines) > 0
for i = array.size(resLines) - 1 to 0
line.delete(array.get(resLines, i))
if array.size(supLines) > 0
for i = array.size(supLines) - 1 to 0
line.delete(array.get(supLines, i))
// Clear arrays
array.clear(resistanceLevels)
array.clear(supportLevels)
array.clear(resistanceCounts)
array.clear(supportCounts)
array.clear(resLines)
array.clear(supLines)
// Reset flags and levels
hasDoubleTop := false
hasDoubleBottom := false
doubleTopLevel := na
doubleBottomLevel := na
nearestDoubleLevel := na // Ensure reset on new day
// Add new swings only if today and after premarket
if not na(swingHigh) and time >= todayStart and dayofmonth == dayofmonth
bool isEqualHigh = false
int peakIndex = -1
float prevLevel = na
if array.size(resistanceLevels) > 0
for i = 0 to array.size(resistanceLevels) - 1
prevLevel := array.get(resistanceLevels, i)
if math.abs(swingHigh - prevLevel) <= tolerance
isEqualHigh := true
peakIndex := i
break
if isEqualHigh and peakIndex >= 0
array.set(resistanceCounts, peakIndex, array.get(resistanceCounts, peakIndex) + 1)
if array.get(resistanceCounts, peakIndex) == requiredPeaks
hasDoubleTop := true
doubleTopLevel := prevLevel
else
array.push(resistanceLevels, swingHigh)
array.push(resistanceCounts, 1)
line newResLine = line.new(bar_index - rightBars, swingHigh, bar_index, swingHigh, color=color.red, width=2, extend=extend.none)
array.push(resLines, newResLine)
if not na(swingLow) and time >= todayStart and dayofmonth == dayofmonth
bool isEqualLow = false
int peakIndex = -1
float prevLevel = na
if array.size(supportLevels) > 0
for i = 0 to array.size(supportLevels) - 1
prevLevel := array.get(supportLevels, i)
if math.abs(swingLow - prevLevel) <= tolerance
isEqualLow := true
peakIndex := i
break
if isEqualLow and peakIndex >= 0
array.set(supportCounts, peakIndex, array.get(supportCounts, peakIndex) + 1)
if array.get(supportCounts, peakIndex) == requiredPeaks
hasDoubleBottom := true
doubleBottomLevel := prevLevel
else
array.push(supportLevels, swingLow)
array.push(supportCounts, 1)
line newSupLine = line.new(bar_index - rightBars, swingLow, bar_index, swingLow, color=color.green, width=2, extend=extend.none)
array.push(supLines, newSupLine)
// Monitor and remove broken levels/lines; reset pattern if the equal level breaks
if array.size(resistanceLevels) > 0
for i = array.size(resistanceLevels) - 1 to 0
float level = array.get(resistanceLevels, i)
if close > level
line.delete(array.get(resLines, i))
array.remove(resLines, i)
array.remove(resistanceLevels, i)
array.remove(resistanceCounts, i)
if level == doubleTopLevel
hasDoubleTop := false
doubleTopLevel := na
nearestDoubleLevel := na // Reset if level breaks
if array.size(supportLevels) > 0
for i = array.size(supportLevels) - 1 to 0
float level = array.get(supportLevels, i)
if close < level
line.delete(array.get(supLines, i))
array.remove(supLines, i)
array.remove(supportLevels, i)
array.remove(supportCounts, i)
if level == doubleBottomLevel
hasDoubleBottom := false
doubleBottomLevel := na
nearestDoubleLevel := na // Reset if level breaks
// Limit arrays (after removals)
if array.size(resistanceLevels) > 10
line oldLine = array.shift(resLines)
line.delete(oldLine)
array.shift(resistanceLevels)
array.shift(resistanceCounts)
if array.size(supportLevels) > 10
line oldLine = array.shift(supLines)
line.delete(oldLine)
array.shift(supportLevels)
array.shift(supportCounts)
// Pattern Signal: 1 only if the exact required number of peaks is met
patternSignal = (hasDoubleTop or hasDoubleBottom) ? 1 : 0
// New: Nearest Double Level Price - Only update if pattern is active today
if time >= todayStart and dayofmonth == dayofmonth // Restrict to today
if (hasDoubleTop and not na(doubleTopLevel)) or (hasDoubleBottom and not na(doubleBottomLevel))
if hasDoubleTop and not na(doubleTopLevel)
nearestDoubleLevel := doubleTopLevel
if hasDoubleBottom and not na(doubleBottomLevel)
nearestDoubleLevel := na(nearestDoubleLevel) ? doubleBottomLevel : (math.abs(close - doubleBottomLevel) < math.abs(close - nearestDoubleLevel) ? doubleBottomLevel : nearestDoubleLevel)
else
nearestDoubleLevel := na // Reset to na if no pattern today
else
nearestDoubleLevel := na // Reset for historical bars
// New: Distance to Nearest Level (using ATR for normalization)
var float atr = ta.atr(atrLength)
var float distanceNormalizedATR = na
if not na(nearestDoubleLevel) and not na(atr) and atr > 0
distanceNormalizedATR := math.abs(close - nearestDoubleLevel) / atr
// Outputs
plot(patternSignal, title="Pattern Signal", color=patternSignal == 1 ? color.purple : na, style=plot.style_circles)
plot(nearestDoubleLevel, title="Nearest Double Level Price", color=color.orange)
plot(distanceNormalizedATR, title="Normalized Distance (ATR)", color=color.green)
bgcolor(patternSignal == 1 ? color.new(color.purple, 80) : na)
if patternSignal == 1 and barstate.isconfirmed
alert("Double Pattern detected on " + syminfo.ticker + " at " + str.tostring(close), alert.freq_once_per_bar_close)
if barstate.islast
var table infoTable = table.new(position.top_right, 1, 3, bgcolor=color.new(color.black, 50))
table.cell(infoTable, 0, 0, "Pattern: " + str.tostring(patternSignal), bgcolor=patternSignal == 1 ? color.purple : color.gray)
table.cell(infoTable, 0, 1, "Level: " + str.tostring(nearestDoubleLevel, "#.##"), bgcolor=color.orange)
table.cell(infoTable, 0, 2, "ATR Dist: " + str.tostring(distanceNormalizedATR, "#.##"), bgcolor=color.green)
Algo MA💎 (V.4.3)Algo MA💎 V.4.3 - Multi-EMA System with Advanced Candle Analysis
**Algo MA💎 V.4.3** is a comprehensive trend analysis system that combines multiple EMA configurations with advanced candle coloring, support/resistance detection, and integrated trade management dashboards. This indicator provides a complete visual trading environment with sophisticated trend identification and portfolio tracking capabilities.
**Core Innovation & Originality**
This system uniquely integrates seven distinct analytical components:
1. **Dual-EMA Signal Engine** - Primary trend detection using 9/21 EMA crossovers with customizable sensitivity
2. **Advanced Candle Classification** - Multi-layer candle coloring with two sensitivity levels (violet/rose) based on ATR calculations
3. **Trend Confirmation System** - Secondary 20/50 EMA trend filter with dynamic cloud visualization
4. **Zero Lag EMA Implementation** - 144-period Zero Lag EMA with directional color coding for reduced lag trend analysis
5. **RSI Extreme Detection** - Overbought (75) and oversold (25) level identification with visual markers
6. **Dynamic Support/Resistance** - Pivot-based support and resistance level calculation with 50-bar lookback
7. **Integrated Trade Management** - Three customizable dashboard tables for real-time portfolio tracking
**System Architecture & Functionality**
**Primary Signal Generation:**
The core system uses a 9-period EMA and 21-period EMA comparison to generate directional bias. When EMA9 > EMA21, the system indicates bullish conditions; when EMA9 < EMA21, it signals bearish conditions. This creates the foundation for all visual elements and trend analysis.
**Advanced Candle Coloring Logic:**
The system employs a sophisticated three-layer candle coloring approach:
- **Green Candles**: EMA9 > EMA21 (bullish trend)
- **Red Candles**: EMA9 < EMA21 (bearish trend)
- **Violet Candles**: EMAs within sensitivity_violet * ATR(14) range (consolidation)
- **Rose Candles**: EMAs within sensitivity_rose * ATR(14) range (tight consolidation)
**Sensitivity-Based Classification:**
Two independent sensitivity parameters allow fine-tuning of consolidation detection:
- **Violet Sensitivity (0.3 default)**: Broader consolidation zones
- **Rose Sensitivity (0.1 default)**: Tighter consolidation zones
**Zero Lag EMA Implementation:**
Uses advanced calculation: `zlema = ema(src + src - src , length)` where lag = floor((length-1)/2). This reduces the inherent lag of traditional EMAs while maintaining smoothness.
**Trend Confirmation Framework:**
The 20/50 EMA system provides trend context with visual cloud fills:
- **Blue Cloud**: 20 EMA > 50 EMA (bullish trend environment)
- **Red Cloud**: 20 EMA < 50 EMA (bearish trend environment)
**Unique Visual Features**
**Multi-Layer Candle System:**
The indicator plots up to four candle layers simultaneously:
1. **Base Candles**: Primary EMA-based trend colors
2. **Violet Consolidation**: ATR-adjusted consolidation detection
3. **Rose Consolidation**: Tighter consolidation identification
4. **Bearish Overlay**: Optional bearish candle highlighting
**Support/Resistance Detection:**
Uses pivot point calculations with 50-bar left and right parameters:
- **Green Lines**: Resistance levels from pivot highs
- **Red Lines**: Support levels from pivot lows
- **Dynamic Updates**: Lines adjust based on price action
**RSI Extreme Markers:**
- **Red Triangles**: First occurrence of RSI > 75 (overbought)
- **Green Triangles**: First occurrence of RSI < 25 (oversold)
**Integrated Dashboard System**
**Trade Management Tables:**
Three independent dashboard tables provide comprehensive trade tracking:
- **Stock Information**: Ticker symbol and trade direction (BUY/SELL)
- **Order Details**: Entry price, stop loss, and take profit levels
- **Position Status**: Real-time trade monitoring with color-coded status
**Dashboard Customization:**
- **Positioning**: Bottom-right, bottom-center, bottom-left placement options
- **Color Coding**: Green for BUY positions, red for SELL positions
- **Manual Entry**: User-customizable fields for trade parameters
**Volume Analysis Integration**
**Volume Oscillator:**
Implements short (5) and long (10) EMA volume comparison:
`osc = 100 * (short_volume_ema - long_volume_ema) / long_volume_ema`
This provides additional confirmation for trend strength and potential reversals.
**Usage Instructions**
**Trend Identification:**
- **Primary Trend**: Monitor 9/21 EMA relationship and candle colors
- **Trend Strength**: Observe Zero Lag EMA color (green=bullish, red=bearish)
- **Trend Context**: Use 20/50 EMA cloud for higher timeframe bias
**Entry Signal Recognition:**
- **Bullish Setup**: Green candles + blue trend cloud + support level test
- **Bearish Setup**: Red candles + red trend cloud + resistance level test
- **Consolidation**: Violet/rose candles indicate ranging conditions
**Risk Management Application:**
- **Support/Resistance**: Use pivot levels for stop placement and targets
- **RSI Extremes**: Monitor overbought/oversold conditions for reversal potential
- **Dashboard Tracking**: Utilize tables for position management
**Advanced Analysis:**
- **Sensitivity Adjustment**: Modify violet/rose parameters for market volatility
- **Multi-Timeframe**: Apply system across different timeframes for confluence
- **Volume Confirmation**: Use volume oscillator for signal validation
**Customization Options**
**EMA Parameters:**
- **Main Flow EMAs**: Adjustable 9/21 period settings
- **Trend EMAs**: Customizable 20/50 period configuration
- **Zero Lag EMA**: Modifiable 144-period length
**Visual Settings:**
- **Candle Display**: Toggle bearish candle overlay
- **Trend Visualization**: Show/hide trend cloud and EMAs
- **Support/Resistance**: Enable/disable pivot level display
- **RSI Markers**: Control overbought/oversold triangle display
**Dashboard Configuration:**
- **Table Display**: Independent control for three dashboard tables
- **Trade Details**: Customizable entry, stop, and target fields
- **Position Status**: Manual BUY/SELL/neutral designation
**Alert System**
Built-in alert conditions for:
- **Bullish Signal**: EMA9 crosses above EMA21
- **Bearish Signal**: EMA9 crosses below EMA21
**Important Considerations**
This system works optimally in trending markets with clear directional bias. During consolidation periods, focus on violet/rose candle identification and range-bound strategies. The multiple EMA layers provide comprehensive trend analysis but may generate conflicting signals during choppy conditions.
The dashboard tables serve as trade management tools but require manual input for position tracking. The system combines established EMA techniques with original sensitivity-based consolidation detection and advanced visual presentation methods.
**Disclaimer**: This indicator is designed for educational and analytical purposes. The dashboard tables are for position tracking only and do not execute trades automatically. Past performance does not guarantee future results. Always implement proper risk management and consider multiple confirmation methods before making trading decisions.
40 Ticker Cross-Sectional Z-Scores [BackQuant]40 Ticker Cross-Sectional Z-Scores
BackQuant’s 40 Ticker Cross-Sectional Z-Scores is a powerful portfolio management strategy that analyzes the relative performance of up to 40 different assets, comparing them on a cross-sectional basis to identify the top and bottom performers. This indicator computes Z-scores for each asset based on their log returns and evaluates them relative to the mean and standard deviation over a rolling window. The Z-scores represent how far an asset's return deviates from the average, and these values are used to rank the assets, allowing for dynamic asset allocation based on performance.
By focusing on the strongest-performing assets and avoiding the weakest, this strategy aims to enhance returns while managing risk. Additionally, by adjusting for standard deviations, the system offers a risk-adjusted method of ranking assets, making it suitable for traders who want to dynamically allocate capital based on performance metrics rather than just price movements.
Key Features
1. Cross-Sectional Z-Score Calculation:
The system calculates Z-scores for 40 different assets, evaluating their log returns against the mean and standard deviation over a rolling window. This enables users to assess the relative performance of each asset dynamically, highlighting which assets are performing better or worse compared to their historical norms. The Z-score is a useful statistical tool for identifying outliers in asset performance.
2. Asset Ranking and Allocation:
The system ranks assets based on their Z-scores and allocates capital to the top performers. It identifies the top and bottom assets, and traders can allocate capital to the top-performing assets, ensuring that their portfolio is aligned with the best performers. Conversely, the bottom assets are removed from the portfolio, reducing exposure to underperforming assets.
3. Rolling Window for Mean and Standard Deviation Calculations:
The Z-scores are calculated based on rolling means and standard deviations, making the system adaptive to changing market conditions. This rolling calculation window allows the strategy to adjust to recent performance trends and minimize the impact of outdated data.
4. Mean and Standard Deviation Visualization:
The script provides real-time visualizations of the mean (x̄) and standard deviation (σ) of asset returns, helping traders quickly identify trends and volatility in their portfolio. These visual indicators are useful for understanding the current market environment and making more informed allocation decisions.
5. Top & Bottom Performer Tables:
The system generates tables that display the top and bottom performers, ranked by their Z-scores. Traders can quickly see which assets are outperforming and underperforming. These tables provide clear and actionable insights, helping traders make informed decisions about which assets to include in their portfolio.
6. Customizable Parameters:
The strategy allows traders to customize several key parameters, including:
Rolling Calculation Window: Set the window size for the rolling mean and standard deviation calculations.
Top & Bottom Tickers: Choose how many of the top and bottom assets to display and allocate capital to.
Table Orientation: Select between vertical or horizontal table formats to suit the user’s preference.
7. Forward Test & Out-of-Sample Testing:
The system includes out-of-sample forward tests, ensuring that the strategy is evaluated based on real-time performance, not just historical data. This forward testing approach helps validate the robustness of the strategy in dynamic market conditions.
8. Visual Feedback and Alerts:
The system provides visual feedback on the current asset rankings and allocations, with dynamic labels and plots on the chart. Additionally, users receive alerts when allocations change, keeping them informed of important adjustments.
9. Risk Management via Z-Scores and Std Dev:
The system’s approach to asset selection is based on Z-scores, which normalize performance relative to the historical mean. By incorporating standard deviation, it accounts for the volatility and risk associated with each asset. This allows for more precise risk management and portfolio construction.
10. Note on Mean Reversion Strategy:
If you take the inverse of the signals provided by this indicator, the strategy can be used for mean-reversion rather than trend-following. This would involve buying the underperforming assets and selling the outperforming ones. However, it's important to note that this approach does not work well with highly correlated assets, as the relationship between the assets could result in the same directional movement, undermining the effectiveness of the mean-reversion strategy.
References
www.uts.edu.au
onlinelibrary.wiley.com
www.cmegroup.com
Final Thoughts
The 40 Ticker Cross-Sectional Z-Scores strategy offers a data-driven approach to portfolio management, dynamically allocating capital based on the relative performance of assets. By using Z-scores and standard deviations, this strategy ensures that capital is directed to the strongest performers while avoiding weaker assets, ultimately improving the risk-adjusted returns of the portfolio. Whether you’re focused on trend-following or looking to explore mean-reversion strategies, this flexible system can be tailored to suit your investment goals.
Volume pressure by GSK-VIZAG-AP-INDIA🔍 Volume Pressure by GSK-VIZAG-AP-INDIA
🧠 Overview
“Volume Pressure” is a multi-timeframe, real-time table-based volume analysis tool designed to give traders a clear and immediate view of buying and selling pressure across custom-selected timeframes. By breaking down buy volume, sell volume, total volume, and their percentages, this indicator helps traders identify demand/supply imbalances and volume momentum in the market.
🎯 Purpose / Trading Use Case
This indicator is ideal for intraday and short-term traders who want to:
Spot aggressive buying or selling activity
Track volume dynamics across multiple timeframes *1 min time frame will give best results*
Use volume pressure as a confirming tool alongside price action or trend-based systems
It helps determine when large buying/selling activity is occurring and whether such behavior is consistent across timeframes—a strong signal of institutional interest or volume-driven trend shifts.
🧩 Key Features & Logic
Real-Time Table Display: A clean, dynamic table showing:
Buy Volume
Sell Volume
Total Volume
Buy % of total volume
Sell % of total volume
Multi-Time frame Analysis: Supports 8 user-selectable custom time frames from 1 to 240 minutes, giving flexibility to analyze volume pressure at various granularities.
Color-Coded Volume Bias:
Green for dominant Buy pressure
Red for dominant Sell pressure
Yellow for Neutral
Intensity-based blinking for extreme values (over 70%)
Dynamic Data Calculation:
Uses volume * (close > open) logic to estimate buy vs sell volumes bar-by-bar, then aggregates by timeframe.
⚙️ User Inputs & Settings
Timeframe Selectors (TF1 to TF8): Choose any 8 timeframes you want to monitor volume pressure across.
Text & Color Settings:
Customize text colors for Buy, Sell, Total volumes
Choose Buy/Sell bias colors
Enable/disable blinking for visual emphasis on extremes
Table Appearance:
Set header color, metric background, and text size
Table positioning: top-right, bottom-right, etc.
Blinking Highlight Toggle: Enable this to visually highlight when Buy/Sell % exceeds 70%—a sign of strong pressure.
📊 Visual Elements Explained
The table has 6 rows and 10 columns:
Row 0: Headers for Today and TF1 to TF8
Rows 1–3: Absolute values (Buy Vol, Sell Vol, Total Vol)
Rows 4–5: Relative percentages (Buy %, Sell %), with dynamic background color
First column shows the metric names (e.g., “Buy Vol”)
Cells blink using alternate background colors if volume pressure crosses thresholds
💡 How to Use It Effectively
Use Buy/Sell % rows to confirm potential breakout trades or identify volume exhaustion zones
Look for multi-timeframe confluence: If 5 or more TFs show >70% Buy pressure, buyers are in control
Combine with price action (e.g., breakouts, reversals) to increase conviction
Suitable for equities, indices, futures, crypto, especially on lower timeframes (1m to 15m)
🏆 What Makes It Unique
Table-based MTF Volume Pressure Display: Most indicators only show volume as bars or histograms; this script summarizes and color-codes volume bias across timeframes in a tabular format.
Customization-friendly: Full control over colors, themes, and timeframes
Blinking Alerts: Rare visual feature to capture user attention during extreme pressure
Designed with performance and readability in mind—even for fast-paced scalping environments.
🚨 Alerts / Extras
While this script doesn’t include TradingView alert functions directly, the visual blinking serves as a strong real-time alert mechanism.
Future versions may include built-in alert conditions for buy/sell bias thresholds.
🔬 Technical Concepts Used
Volume Dissection using close > open logic (to estimate buyer vs seller pressure)
Simple aggregation of volume over custom timeframes
Table plotting using Pine Script table.new, table.cell
Dynamic color logic for bias identification
Custom blinking logic using na(bar_index % 2 == 0 ? colorA : colorB)
⚠️ Disclaimer
This indicator is a tool for analysis, not financial advice. Always backtest and validate strategies before using any indicator for live trading. Past performance is not indicative of future results. Use at your own risk and apply proper risk management.
✍️ Author & Signature
Indicator Name: Volume Pressure
Author: GSK-VIZAG-AP-INDIA
TradingView Username: prowelltraders
Relative Performance SuiteOverview
The Relative Performance Suite (RPS) is a versatile and comprehensive indicator designed to evaluate an asset's performance relative to a benchmark. By offering multiple methods to measure performance, including Relative Performance, Alpha, and Price Ratio, this tool helps traders and investors assess asset strength, resilience, and overall behavior in different market conditions.
Key Features:
✅ Multiple Performance Measures:
Choose from various relative performance calculations, including:
Relative Performance:
Measures how much an asset has outperformed or underperformed its benchmark over a given period.
Relative Performance (Proportional):
A proportional version of relative performance,
factoring in scaling effects.
Relative Performance (MA Based):
Uses moving averages to smooth performance fluctuations.
Alpha:
A measure of an asset’s performance relative to what would be expected based on its beta and the benchmark’s return. It represents the excess return above the risk-free rate after adjusting for market risk.
Price Ratio:
Compares asset prices directly to determine relative value over time.
✅ Customizable Moving Averages:
Apply different moving average types (SMA, EMA, SMMA, WMA, VWMA) to smooth price inputs and refine calculations.
✅ Beta Calculation:
Includes a Beta measure used in Alpha calculation, which users can toggle the visibility of helping users understand an asset's sensitivity to market movements.
✅ Risk-Free Rate Adjustment:
Incorporate risk-free rates (e.g., US Treasury yields, Fed Funds Rate) for a more accurate calculation of Alpha.
✅ Logarithmic Returns Option:
Users can switch between standard returns and log returns for more refined performance analysis.
✅ Dynamic Color Coding:
Identify outperformance or underperformance with intuitive color coding.
Option to color bars based on relative strength, making chart analysis easier.
✅ Customizable Tables for Data Display:
Overview table summarizing key metrics.
Explanation table offering insights into how values are derived.
How to Use:
Select a Benchmark: Choose a comparison symbol (e.g., TOTAL or SPX ).
Pick a Performance Metric: Use different modes to analyze relative performance.
Customize Calculation Methods: Adjust moving averages, timeframes, and log returns based on preference.
Interpret the Colors & Tables: Utilize the dynamic coloring and tables to quickly assess market conditions.
Ideal For:
Traders looking to compare individual asset performance against an index or benchmark.
Investors analyzing Alpha & Beta to understand risk-adjusted returns.
Market analysts who want a visually intuitive and data-rich performance tracking tool.
This indicator provides a powerful and flexible way to track relative asset strength, helping users make more informed trading decisions.
ATR Price Range Prediction V.2### ATR Price Range Prediction V.2
This script calculates the expected high and low prices for the current day based on the Average True Range (ATR) and displays the proportion of days where the daily range (high - low) is greater than or equal to the ATR. Additionally, the script provides an option to adjust the size of the text displayed in the top-right corner of the chart.
#### How It Works
1. **ATR Calculation**: The script calculates the ATR for a specified period (`atrPeriod`). ATR is a measure of volatility that represents the average range between the high and low prices over a specified number of periods.
2. **Expected High and Low Calculation**:
- **Expected High**: Calculated by adding the ATR value to the low price of the current day.
- **Expected Low**: Calculated by subtracting the ATR value from the high price of the current day.
3. **Proportion Calculation**: The script calculates the proportion of days where the daily range (high - low) is greater than or equal to the ATR value. This proportion is updated in real-time as new data comes in.
4. **Table Display**: Instead of displaying labels on each candle, the script shows the expected high, expected low, and the calculated proportion in a table located at the top-right corner of the chart. The size of the text in this table can be adjusted using the `Table Size` input.
5. **Color Coding**: The script changes the color of the bars to yellow if the daily range is greater than or equal to the ATR value, making it easy to identify these bars visually.
#### How to Use
- **ATR Period (`atrPeriod`)**: Adjust the period for the ATR calculation using the input parameter. The default value is 14.
- **Table Size (`tableSizeOption`)**: Choose the size of the text displayed in the table. Options include `tiny`, `small`, `normal`, `large`, and `huge`.
- **Expected High and Low**: Use the green and red lines to identify potential target prices or stop-loss levels for your trades. The green line represents the expected high, and the red line represents the expected low.
- **Proportion**: The table in the top-right corner of the chart shows the proportion of days where the daily range is greater than or equal to the ATR value. This can provide insight into the volatility of the asset.
- **Color Coding**: Yellow bars indicate days where the daily range is greater than or equal to the ATR value.
---
### ภาษาไทย
### ATR คาดการณ์ราคาสูงสุดและต่ำสุด พร้อมสัดส่วน
สคริปต์นี้คำนวณราคาสูงสุดและต่ำสุดที่คาดการณ์สำหรับวันปัจจุบันโดยอิงจากค่าเฉลี่ยช่วงที่แท้จริง (ATR) และแสดงสัดส่วนของวันที่ช่วงราคาต่อวัน (สูง - ต่ำ) มากกว่าหรือเท่ากับค่า ATR นอกจากนี้ยังมีตัวเลือกในการปรับขนาดข้อความที่แสดงในกล่องข้อความมุมขวาบนของกราฟ
#### วิธีการทำงาน
1. **การคำนวณ ATR**: สคริปต์คำนวณค่า ATR สำหรับช่วงเวลาที่กำหนด (`atrPeriod`) ATR เป็นมาตรวัดความผันผวนที่แสดงช่วงเฉลี่ยระหว่างราคาสูงสุดและต่ำสุดในช่วงเวลาที่กำหนด
2. **การคำนวณราคาสูงสุดและต่ำสุดที่คาดการณ์**:
- **ราคาสูงสุดที่คาดการณ์**: คำนวณโดยการบวกค่า ATR กับราคาต่ำสุดของวันปัจจุบัน
- **ราคาต่ำสุดที่คาดการณ์**: คำนวณโดยการลบค่า ATR จากราคาสูงสุดของวันปัจจุบัน
3. **การคำนวณสัดส่วน**: สคริปต์คำนวณสัดส่วนของวันที่ช่วงราคาต่อวัน (สูง - ต่ำ) มากกว่าหรือเท่ากับค่า ATR สัดส่วนนี้จะอัปเดตแบบเรียลไทม์เมื่อมีข้อมูลใหม่เข้ามา
4. **การแสดงผลในตาราง**: แทนที่จะแสดงป้ายกำกับบนแท่งเทียนแต่ละแท่ง สคริปต์จะแสดงราคาสูงสุดที่คาดการณ์ ราคาต่ำสุดที่คาดการณ์ และสัดส่วนที่คำนวณในตารางที่มุมขวาบนของกราฟ โดยสามารถปรับขนาดข้อความในตารางได้
5. **การใช้สี**: สคริปต์จะเปลี่ยนสีของแท่งเทียนเป็นสีเหลืองหากช่วงราคาต่อวันมากกว่าหรือเท่ากับค่า ATR ทำให้สามารถระบุแท่งเทียนเหล่านี้ได้ง่ายขึ้น
#### วิธีการใช้งาน
- **ATR Period (`atrPeriod`)**: ปรับช่วงเวลาสำหรับการคำนวณ ATR โดยใช้พารามิเตอร์การป้อนค่า ค่าเริ่มต้นคือ 14
- **Table Size (`tableSizeOption`)**: เลือกขนาดข้อความที่แสดงในตาราง ตัวเลือกได้แก่ `tiny`, `small`, `normal`, `large`, และ `huge`
- **ราคาสูงสุดและต่ำสุดที่คาดการณ์**: ใช้เส้นสีเขียวและสีแดงเพื่อระบุราคาที่เป็นเป้าหมายหรือระดับการหยุดขาดทุนสำหรับการซื้อขายของคุณ เส้นสีเขียวแสดงถึงราคาสูงสุดที่คาดการณ์และเส้นสีแดงแสดงถึงราคาต่ำสุดที่คาดการณ์
- **สัดส่วน**: ตารางที่มุมขวาบนของกราฟแสดงสัดส่วนของวันที่ช่วงราคาต่อวันมากกว่าหรือเท่ากับค่า ATR ซึ่งสามารถให้ข้อมูลเชิงลึกเกี่ยวกับความผันผวนของสินทรัพย์
- **การใช้สี**: แท่งเทียนสีเหลืองบ่งบอกถึงวันที่ช่วงราคาต่อวันมากกว่าหรือเท่ากับค่า ATR
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Volume Profile Histogram [SS]I usually (and by usually, I mean the past year xD) release a significant indicator as my Christmas gift to the community on Christmas Eve. Last year, it was the Z-Score buy and sell signal; this year, it's something a little more conventional. So here is this year’s gift—hope you like it! 🎁
Seems like everyone has their take on Volume Profiles (aka SVP or VSP). I decided to create one, and in true Steversteves fashion, you can expect to find all the goodies that come with most of my stuff, including a volume profile presented in a bell-curve/histogram style (chart above) and statistical frequency tables showing the cases by ranges:
And it wouldn't be a true Steversteves indicator without some kind of ATR thing:
So, what does it do?
At the end of the day, it is a form of an SVP indicator. However, it is meant to operate on a larger scale, sorting volume in a traditional bell-curve style. In addition to displaying volume, it breaks down buying vs. selling volume. Selling volume is classified as such when the open is greater than close, while buying is when close is greater than open. This breakdown allows you to see the distribution, by price range, of where selling and buying occur.
This permits the indicator to provide 2 Points of Control (POCs). A POC is defined as an area of high volume activity. Because buying and selling volumes are broken down into two, we can identify areas with high selling and areas with high buying. Sometimes they coincide, sometimes they differ.
If we look at SQQQ, for example:
We can see that the bearish point of control is one point below the bullish POC. This is interesting because it essentially shows where people may be "panic selling" or setting their stop-outs. If SQQQ drops below 18.8, then it's likely to trigger panic selling, as indicated by the histogram.
Conversely, we can observe that traders tend to position long between $18 and $24. The POC is noted in the stats table and also displayed on the chart. Bullish POC is shown in purple, bearish in yellow. These, of course, can be toggled off.
The Frequency Table:
The frequency table shows how many observations were obtained in each price range. The histogram illustrates the cumulative volume traded, while the frequency simply counts how many cases occurred over the lookback period.
ATR Range Analytics by Volume:
The indicator also has the ability to display range analytics by volume. When you toggle on the range analytics by volume option, a range chart will appear:
www.tradingview.com
The range chart goes from the minimum recorded volume to the maximum recorded volume in the period, showing the average range and direction associated with this volume. This is crucial to pay attention to because not all stocks behave the same way.
For example, in the chart above (AMD), we can see that low volume produces a general bearish bias, and high volume produces a general bullish bias. However, if we look at the range analytics for SPY:
Low volume has the inverse effect. Low volume is associated with a more bullish bias, and high volume indicates a more bearish bias. In the ATR chart, the threshold volume to transition from bullish bias to bearish bias is approximately > 78,607,268 traded shares.
The Stats Table:
The stats table can be toggled on or off. It simply displays the POCs and the time range for the VSP. The default time range is 1 trading year (252 days), assuming you are on the daily timeframe. However, you can use this on any timeframe.
The percentages displayed in the histogram is the cumulative percent of buying and selling volume independently. So when you see the percentage on the selling histogram, its the percent of cumulative selling only. Same for the buying.
And that's the indicator! I hope you enjoy it. Let me know your thoughts. I hope you all have safe holidays, a Merry Christmas for you North Americans, and a Happy Christmas for you UKers, and whatever else you celebrate/care about and do! Safe trades, everyone, and enjoy your holidays! 🎁🎄🎄🎄⭐⭐⭐ 🕎 🕎 🕎
High/Low of week: Stats & Day of Week tendencies// Purpose:
-To show High of Week (HoW) day and Low of week (LoW) day frequencies/percentages for an asset.
-To further analyze Day of Week (DoW) tendencies based on averaged data from all various custom weeks. Giving a more reliable measure of DoW tendencies ('Meta Averages').
-To backtest day-of-week tendencies: across all asset history or across custom user input periods (i.e. consolidation vs trending periods).
-Education: to see how how data from a 'hard-defined-week' may be misleading when seeking statistical evidence of DoW tendencies.
// Notes & Tips:
-Only designed for use on DAILY timeframe.
-Verification table is to make sure HoW / LoW DAY (referencing previous finished week) is printing correctly and therefore the stats table is populating correctly.
-Generally, leaving Timezone input set to "America/New_York" is best, regardless of your asset or your chart timezone. But if misaligned by 1 day =>> tweak this timezone input to correct
-If you want to use manual backtesting period (e.g. for testing consolidation periods vs trending periods): toggle these settings on, then click the indicator display line three dots >> 'Reset Points' to quickly set start & end dates.
// On custom week start days:
-For assets like BTC which trade 7 days a week, this is quite simple. Pick custom start day, use verification table to check all is well. See the start week day & time in said verification table.
-For traditional assets like S&P which trade only 5 days a week and suffer from occasional Holidays, this is a bit more complicated. If the custom start day input is a bank holiday, its custom 'week' will be discounted from the data set. E.g.1: if you choose 'use custom start day' and set it to Monday, then bank holiday Monday weeks will be discounted from the data set. E.g.2: If you choose 'use custom start day' and set it to Thursday, then the Holiday Thursday custom week (e.g Thanksgiving Thursday >> following Weds) would be discounted from the data set.
// On 'Meta Averages':
-The idea is to try and mitigate out the 'continuation bias' that comes from having a fixed week start/end time: i.e. sometimes a market is trending through the week start/end time, so the start/end day stats are over-weighted if one is trying to tease out typical weekly profile tendencies or typical DoW tendencies. You'll notice this if you compare the stats with various custom start days ('bookend' start/end days are always more heavily weighted). I wanted to try to mitigate out this 'bias' by cycling through all the possible new week start/end days and taking an average of the results. i.e. on BTC/USD the 'meta average' for Tuesday would be the average of the Tuesday HoW frequencies from the set of all 7 possible custom weeks(Mon-Sun, Tues-Mon, Weds-Tues, etc etc).
// User Inputs:
~Week Start:
-use custom week start day (default toggled OFF); Choose custom week start day
-show Meta Averages (default toggled ON)
~Verification Table:
-show table, show new week lines, number of new week lines to show
-table formatting options (position, color, size)
-timezone (only for tweaking if printed DoW is misaligned by 1 day)
~Statistics Table:
-show table, table formatting options (position, color, size)
~Manual Backtesting:
-Use start date (default toggled OFF), choose start date, choose vline color
-Use end date (defautl toggled OFF), choose end date, choose vline color
// Demo charts:
NQ1! (Nasdaq), Full History, Traditional week (Mon>>Friday) stats. And Meta Averages. Annotations in purple:
NQ1! (Nasdaq), Full History, Custom week (custom start day = Wednesday). And Meta Averages. Annotations in purple:
Stock Performance Dashboard + Sector PerformanceThis indicator, Stock Performance Dashboard + Sector Performance, provides a comprehensive visual analysis of both individual stock performance and sectoral trends directly on your TradingView chart.
Key Features:
Performance Dashboard Table:
Displays the stock’s returns over multiple timeframes (1, 5, 10, 20, 50, 200 days) and compares them to a selected benchmark index (default: NIFTYMIDSML400).
Highlights whether the stock is outperforming the index for each period, shows the difference in performance, and includes an RSI (Relative Strength Index) column for additional momentum insight.
Calculates and displays a “conviction” score and level based on how often the stock outperforms the index across periods.
Sector Performance Table:
Ranks and displays the top-performing sectors from a predefined list of major NSE sector indices over four key periods (1D, 5D, 30D, 50D).
For each period, the top 5 sectors are shown, with color-coded performance for quick visual assessment.
Customization:
Includes options for dark/light mode, table size, position, and which columns to display.
Supports a compact “mini mode” for simplified visualization.
Usage:
This tool is ideal for traders and investors who want a quick, at-a-glance comparison of a stock’s short- and long-term momentum versus its benchmark, as well as a live snapshot of sector rotation and leadership in the Indian market. All data is presented in clear, color-coded tables for actionable decision-making.
Aggressive Predictor+ (Last Bar, Vol, Wick)# Aggressive Predictor+ Pine Script Indicator
**Version:** Based on the script incorporating Last Bar analysis, Volume Confirmation, and Wick Rejection.
## Overview
This TradingView Pine Script indicator aims to predict the likely directional bias of the **next** candle based on an aggressive analysis of the **last closed candle's** price action, volume, and wick characteristics relative to recent market volatility (ATR).
It is designed to be **highly reactive** to the most recent bar's information. The prediction is visualized directly on the chart through shapes, a projected line, a text label, and an information table.
**Please Note:** Predicting the next candle is inherently speculative. This indicator provides a probability assessment based on its specific logic and should be used as a supplementary tool within a broader trading strategy, not as a standalone signal. Its performance heavily depends on market conditions and the chosen settings.
## Core Logic Explained
The indicator follows these steps for each new bar, looking back at the **last closed bar** (` `):
1. **Calculate Recent Volatility:** Determines the Average True Range (ATR) over the specified `ATR Lookback Period` (`atr_len`). This provides context for how volatile the market has been recently.
2. **Analyze Last Bar's Body:** Calculates the price change from open to close (`close - open `) of the last completed bar.
3. **Compare Body to Volatility:** Compares the absolute size of the last bar's body to the calculated ATR (`prev_atr`) multiplied by a sensitivity threshold (`threshold_atr_mult`).
* If the body size (positive) exceeds the threshold, the initial prediction is "Upward".
* If the body size (negative) exceeds the negative threshold, the initial prediction is "Downward".
* Otherwise, the initial prediction is "Neutral".
4. **Check Volume Confirmation:** Compares the volume of the last bar (`volume `) to its recent average volume (`ta.sma(volume, vol_avg_len) `). If the volume is significantly higher (based on `vol_confirm_mult`), it adds context ("High Vol") to directional predictions.
5. **Check for Wick Rejection:** Analyzes the wicks of the last bar.
* If the initial prediction was "Upward" but there was a large upper wick (relative to the body size, defined by `wick_rejection_mult`), it indicates potential selling pressure rejecting higher prices. The prediction is **overridden to "Neutral"**.
* If the initial prediction was "Downward" but there was a large lower wick, it indicates potential buying pressure supporting lower prices. The prediction is **overridden to "Neutral"**.
6. **Determine Final Prediction:** The final state ("Upward", "Downward", or "Neutral") is determined after considering potential wick rejection overrides. Context about volume or wick rejection is added to the display text.
## Visual Elements
* **Prediction Shapes:**
* Green Up Triangle: Below the bar for an "Upward" prediction (without wick rejection).
* Red Down Triangle: Above the bar for a "Downward" prediction (without wick rejection).
* Gray Diamond: Above/Below the bar if a directional move was predicted but then neutralized due to Wick Rejection.
* **Prediction Line:**
* Extends forward from the current bar's close for `line_length` bars (Default: 20).
* Color indicates the final predicted state (Green: Upward, Red: Downward, Gray: Neutral).
* Style is solid for directional predictions, dotted for Neutral.
* The *slope/magnitude* of the line is based on recent volatility (ATR) and the `projection_mult` setting, representing a *potential* magnitude if the predicted direction holds, scaled by recent volatility. **This is purely a visual projection, not a precise price forecast.**
* **Prediction Label:**
* Appears at the end of the Prediction Line.
* Displays the final prediction state (e.g., "Upward (High Vol)", "Neutral (Wick Rej)").
* Shows the raw price change of the last bar's body and its size relative to ATR (e.g., "Last Body: 1.50 (120.5% ATR)").
* Tooltip provides more detailed calculation info.
* **Info Table:**
* Displays the prediction state, color-coded.
* Shows details about the last bar's body size relative to ATR.
* Dynamically positioned near the latest bar (offsets configurable).
## Configuration Settings (Inputs)
These settings allow you to customize the indicator's behavior and appearance. Access them by clicking the "Settings" gear icon on the indicator name on your chart.
### Price Action & ATR
* **`ATR Lookback Period` (`atr_len`):**
* *Default:* 14
* *Purpose:* Number of bars used to calculate the Average True Range (ATR), which measures recent volatility.
* **`Body Threshold (ATR Multiplier)` (`threshold_atr_mult`):**
* *Default:* 0.5
* *Purpose:* Key setting for **aggression**. The last bar's body size (`close - open`) must be greater than `ATR * this_multiplier` to be initially classified as "Upward" or "Downward".
* *Effect:* **Lower values** make the indicator **more aggressive** (easier to predict direction). Higher values require a stronger price move relative to volatility and result in more "Neutral" predictions.
### Volume Confirmation
* **`Volume Avg Lookback` (`vol_avg_len`):**
* *Default:* 20
* *Purpose:* Number of bars used to calculate the simple moving average of volume.
* **`Volume Confirmation Multiplier` (`vol_confirm_mult`):**
* *Default:* 1.5
* *Purpose:* Volume on the last bar is considered "High" if it's greater than `Average Volume * this_multiplier`.
* *Effect:* Primarily adds context "(High Vol)" or "(Low Vol)" to the display text for directional predictions. Doesn't change the core prediction state itself.
### Wick Rejection
* **`Wick Rejection Multiplier` (`wick_rejection_mult`):**
* *Default:* 1.0
* *Purpose:* If an opposing wick (upper wick on an up-candle, lower wick on a down-candle) is larger than `body size * this_multiplier`, the directional prediction is overridden to "Neutral".
* *Effect:* Higher values require a much larger opposing wick to cause a rejection override. Lower values make wick rejection more likely.
### Projection Settings
* **`Projection Multiplier (ATR based)` (`projection_mult`):**
* *Default:* 1.0
* *Purpose:* Scales the projected *magnitude* of the prediction line. The projected price change shown by the line is `+/- ATR * this_multiplier`.
* *Effect:* Adjusts how far up or down the prediction line visually slopes. Does not affect the predicted direction.
* **`Prediction Line Length (Bars)` (`line_length`):**
* *Default:* 20
* *Purpose:* Controls how many bars forward the **visual** prediction line extends.
* *Effect:* Purely visual length adjustment.
### Visuals
* **`Upward Color` (`bullish_color`):** Color for Upward predictions.
* **`Downward Color` (`bearish_color`):** Color for Downward predictions.
* **`Neutral Color` (`neutral_color`):** Color for Neutral predictions (including Wick Rejections).
* **`Show Prediction Shapes` (`show_shapes`):** Toggle visibility of the triangles/diamonds on the chart.
* **`Show Prediction Line` (`show_line`):** Toggle visibility of the projected line.
* **`Show Prediction Label` (`show_label`):** Toggle visibility of the text label at the end of the line.
* **`Show Info Table` (`show_table`):** Toggle visibility of the information table.
### Table Positioning
* **`Table X Offset (Bars)` (`table_x_offset`):**
* *Default:* 3
* *Purpose:* How many bars to the right of the current bar the info table should appear.
* **`Table Y Offset (ATR Multiplier)` (`table_y_offset_atr`):**
* *Default:* 0.5
* *Purpose:* How far above the high of the last bar the info table should appear, measured in multiples of ATR.
## How to Use
1. Open the Pine Editor in TradingView.
2. Paste the complete script code.
3. Click "Add to Chart".
4. Adjust the input settings (especially `threshold_atr_mult`) to tune the indicator's aggressiveness and visual preferences.
5. Observe the prediction elements alongside your other analysis methods.
## Disclaimer
**This indicator is for informational and educational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset.** Trading financial markets involves significant risk, and you could lose money. Predictions about future price movements are inherently uncertain. The performance of this indicator depends heavily on market conditions and the settings used. Always perform your own due diligence and consider multiple factors before making any trading decisions. Use this indicator at your own risk.
