Divergence Histogram for Many IndicatorHello Traders,
This script analyses divergences for 11 predefined indicators and then draws column on the graph. Red columns for negatif divergence (means prices may go down or trend reversal), Lime columns for positive divergences (means prices may go up or trend reversal)
The script uses Pivot Points and on each bar it checks divergence between last Pivot Point and current High/Low and if it finds any divergence then immediately draws column. There is no Latency/Lag.
There are predefined 11 indicators in the script, which are RSI , MACD , MACD Histogram, Stochastic , CCI , Momentum, OBV, Diosc, VWMACD, CMF and MFI.
Smaller Pivot Point Period check smaller areas and if you use smaller numbers it would be more sensitive and may give alerts very often. So you should set it accordingly.
There is "Check Cut-Through in indicators" option, I recomment you to enable it. it checks that there is cut-through in indicators or not, if no cut-through then it's shown as valid divergence.
You should see following one as well if you haven't yet:
Enjoy!
ابحث في النصوص البرمجية عن "电力行业+股票+11年涨幅"
candlestick patternsCleaning up and updating vcsWo8mh-Candlestick-Patterns-Identified-updated-3-11-15 .
As I learn more candlestick patterns I'll add them in.
Please post requests and any potential implementations I could port to pine script.
I'm applying autopep8 as best I can for readability.
MAC-Z & MACD Leader signal [ChuckBanger]This is a combination of my MACD Leader script and MAC-Z with option to add Laguerre filter. The advantage of the MAC-Z over MACD is that it is a more accurate and “assumption-free” indicator that can more accurately describe how a market actually perform. But you can use this as a regular MACD indicator.
Crossovers signals
The MAC-Z line and signal line can be utilized in the same way as a stochastic oscillator, with the crossover between the two lines providing buy and sell signals. As with most crossover strategies, a buy signal comes when the shorter-term, more reactive line – in this case the MAC-Z line (blue line) crosses above the slower signal line (orange line). For example, when the MAC-Z line crosses below the signal line it provides a bearish sell signal.
Zero line crossing
The zero cross strategy is based on either of the lines crossing the zero line. If the MAC-Z crosses the zero line from below, it is a signal for a possible new uptrend, while the MAC-Z crossing from above is a signal that a new downtrend may be starting. This is special powerful if the lines has a fast up or down movement but the price action doesn't reflect that movement.
Divergences
Bearish and bullish divergences is my favorite signals. When price action and oscillators follow the same path it is called Convergences, when they don’t, it’s called a Divergence. Don't confuse the two because they have not the same meaning. But be aware that for example during consolidation or low liquidity, some small divergences between price and indicators might form, but that doesn't mean we should consider them as real divergences.
There is many different types of divergences. It is easier to show a picture then explaining it so I recommend you to check out the link below. Especially the top image. It sums this up very well
medium.com
MACD Leader
The MACD leader is only showing the crossing of MACD as a vertical line
Green vertical line = MACD Leader Bullish Cross
Red vertical line = MACD Leader Bearish Cross
MACD Leader:
MAC-Z:
More Information
cssanalytics.wordpress.com
en.wikipedia.org
drive.google.com
Edward EMA 8-21-89-144Explain the application of moving averages through the disk surface:
When the price runs above 89, it only looks for the buy signal.
When the price runs below 89, it only looks for sell signals.
The first step up through the 89 moving average after the first confirmation can buy homeoply,
The first pull down after crossing the 89 moving average for the first time confirms that it can be sold in line with the trend.
Price horizontal finishing, moving average frequently across the field observation.
The yellow area in the interval from 8 to 21 is the homeopathic warehouse addition signal.
When the price is above the 89 moving average, the k-line closes below the 21-day moving average as a callback signal
Prices below the 89 ema close above the 21 - day ema as a rebound signal
After the correction and rebound signals come out, we should make half of the profit and the other half of the stop loss in the break-even place.
Moving average is very suitable for the trend of strong varieties, is not suitable for volatile market.
Only at the end of the shock market moving average upward or downward divergent when it is possible to be used.
1. Repeatedly entangle the mean line of horizontal disk stage and observe it from the field
2. Sell the three EMA moving averages when they can't exceed 89EMA with downward crossing
3, many times can not break the new low when prices go sideways profit
4. Buy when the price reaches 89EMA after the convergence of triangle 3 is broken
5, the Angle of price rise slowed and closed below the 21 moving average when profit
6. Left field observation during transverse oscillation.
Sit tight while news or data cause prices to fall quickly
8. Buy when the price triangle breaks through the 89 moving average upward
9, the price does not rise to slow down when the horizontal closed below the 21 moving average when profit
10, price horizontal shock finishing at the same time the average line also transverse finishing field observation
11, the price of the triangle after finishing through the 89 moving average to buy.At this point all the averages have turned up
12, the second time can not break through the new high when the negative line can profit
13, the price of the first time in the same period of time through 89 after the first step back can be re-bought.
通过盘面讲解均线运用：
价格在89上面运行时时只找买入信号、
价格在89下面运行时只寻找卖出信号、
第一次向上穿过89均线后的第一次回踩确认可以顺势买入、
第一次向下穿过89均线后的第一次回抽确认可以顺势卖出、
价格横盘整理，均线频繁穿越时离场观察。
8-21区间里面黄色区域为顺势加仓信号，
价格在89均线上面时K线收盘在21天均线下面时为回调信号
价格在89均线下面时K线收盘在21天均线上面时为反弹信号
在回调和反弹信号出来之后我们应该获利一半的头寸，另外一半止损放到盈亏平衡的地方。
均线非常适合趋势性很强的品种，并不适合震荡行情。
只有在震荡行情结束时均线向上或向下发散时才有被运用的可能。
1、横盘阶段均线反复纠缠，离场观察
2、三条EMA均线向下交叉回抽无法超越89EMA时卖出
3、多次不能破新低时价格走横时获利
4、价格在3处三角形收敛被突破后站上了89EMA时买入
5、价格上涨角度变缓并收盘在21均线下面时获利
6、横盘震荡时离场观察。
7、见死不救新闻或数据导致价格快速下跌时观望
8、价格三角形向上突破时穿过89均线时买入
9、价格不升减速走横时收盘于21均线下面时获利
10、价格横盘震荡整理同时均线也横向整理时离场观察
11、价格突破三角形整理后重新穿过89均线时买入。此时所有均线已经向上翘头
12、第二次不能突破新高时收阴线可以获利
13、价格在同一个时间周期内第一次穿过89以后的第一次回踩可以重新买入
14、89-144作为牛熊的分水岭。在89-144区域之下只考虑做空，89-144只考虑做多。如果89-144走横则以位置决定高位倾向空低位倾向多。
15、K线会因为指标的设置自动变成两个颜色块，绿色看涨，红色看跌。做趋势看K线颜色。牛市的红色可以当成入场K熊市绿色当成入场K
Moving Averages & Bollinger Bands with ForecastsMoving Averages & Bollinger Bands with Forecasts
11 Moving Averages
SMA, EMA, WMA
Highly Customizable
Linear Regression Forecast
Bollonger Bands
Personal Setup: Add indicator twice
1st indicator = SMA using #4, 7, 10, 11 (20, 50, 100, 200 SMAs) with bollonger bands on 20.
2nd indicator = EMA using #1, 2, 3, 5, 6, 8, 9 (5, 8, 13, 21 ,34, 55, 89 EMAs).
This allows easy toggling between SMAs/Bolls and Fib EMAs
Thank you to yatrader2 for the forecast code
Moving Averages & Bollinger Bands with ForecastsMoving Averages & Bollinger Bands with Forecasts
11 Moving Averages
SMA, EMA, WMA
Highly Customizable
Linear Regression Forecast
Bollonger Bands
Personal Setup: Add indicator twice
1st indicator = SMA using #4, 7, 10, 11 (20, 50, 100, 200 SMAs) with bollonger bands on 20.
2nd indicator = EMA using #1, 2, 3, 5, 6, 8, 9 (5, 8, 13, 21 ,34, 55, 89 EMAs).
This allows easy toggling between SMAs/Bolls and Fib EMAs
Thank you to yatrader2 for the forecast code
Moving Averages & Bollinger Bands with ForecastsMoving Averages & Bollinger Bands with Forecasts
11 Moving Averages
SMA, EMA, WMA
Highly Customizable
Linear Regression Forecast
Bollonger Bands
Personal Setup: Add indicator twice
1st indicator = SMA using #4, 7, 10, 11 (20, 50, 100, 200 SMAs) with bollonger bands on 20.
2nd indicator = EMA using #1, 2, 3, 5, 6, 8, 9 (5, 8, 13, 21 ,34, 55, 89 EMAs).
This allows easy toggling between SMAs/Bolls and Fib EMAs
Thank you to yatrader2 for the forecast code
Moving Averages & Bollinger Bands with ForecastsMoving Averages & Bollinger Bands with Forecasts
11 Moving Averages
SMA, EMA, WMA
Highly Customizable
Linear Regression Forecast
Bollonger Bands
Personal Setup: Add indicator twice
1st indicator = SMA using #4, 7, 10, 11 (20, 50, 100, 200 SMAs) with bollonger bands on 20.
2nd indicator = EMA using #1, 2, 3, 5, 6, 8, 9 (5, 8, 13, 21 ,34, 55, 89 EMAs).
This allows easy toggling between SMAs/Bolls and Fib EMAs
Thank you to yatrader2 for the forecast code
[astropark] MACD, RSI+, AO, DMI, ADX, OBV, ADI//******************************************************************************
// Copyright by astropark v4.1.0
// MACD, RSI+, Awesome Oscillator, DMI, ADX, OBV, ADI
// 24/10/2018 Added RSI with Center line to have clear glue of current trend
// 10/12/2018 Added MACD
// 13/12/2018 Added multiplier for MACD in order to make it clearly visible over RSI graph
// 11/01/2019 Added Awesome Ascillator (AO)
// 11/01/2019 Added Directional Movement Index (DMI) with ADX
// 14/01/2019 Added On Balance Volume (OBV)
// 14/01/2019 Added Accelerator Decelerator Indicator (ADI)
//******************************************************************************
[astropark] MACD, RSI+, Awesome Oscillator, DMI, ADX, OBV//******************************************************************************
// Copyright by astropark v4.0.0
// MACD, RSI+, Awesome Oscillator, DMI, ADX, OBV
// 24/10/2018 Added RSI with Center line to have clear glue of current trend
// 10/12/2018 Added MACD
// 13/12/2018 Added multiplier for MACD in order to make it clearly visible over RSI graph
// 11/01/2019 Added Awesome Oscillator (AO)
// 11/01/2019 Added Directional Movement Index (DMI) with ADX
// 14/01/2019 Added On Balance Volume (OBV)
//******************************************************************************
[astropark] MACD, RSI+, Awesome Oscillator, DMI with ADX//******************************************************************************
// Copyright by astropark v3.1.0
// MACD, RSI+, Awesome Oscillator, DMI, ADX
// 24/10/2018 Added RSI with Center line to have clear glue of current trend
// 10/12/2018 Added MACD
// 13/12/2018 Added multiplier for MACD in order to make it clearly visible over RSI graph
// 11/01/2019 Added Awesome Ascillator (AO)
// 11/01/2019 Added Directional Movement Index (DMI) with ADX
//******************************************************************************
King 4EMA TraderKing 4EMA trader 8/21/89EMA+(233)V3.3
Explain the application of moving averages through the disk surface:
When the price runs above 89, it only looks for the buy signal.
When the price runs below 89, it only looks for sell signals.
The first step up through the 89 moving average after the first confirmation can buy homeoply,
The first pull down after crossing the 89 moving average for the first time confirms that it can be sold in line with the trend.
Price horizontal finishing, moving average frequently across the field observation.
The yellow area in the interval from 8 to 21 is the homeopathic warehouse addition signal.
When the price is above the 89 moving average, the k-line closes below the 21-day moving average as a callback signal
Prices below the 89 ema close above the 21 - day ema as a rebound signal
After the correction and rebound signals come out, we should make half of the profit and the other half of the stop loss in the break-even place.
Moving average is very suitable for the trend of strong varieties, is not suitable for volatile market.
Only at the end of the shock market moving average upward or downward divergent when it is possible to be used.
1. Repeatedly entangle the mean line of horizontal disk stage and observe it from the field
2. Sell the three EMA moving averages when they can't exceed 89EMA with downward crossing
3, many times can not break the new low when prices go sideways profit
4. Buy when the price reaches 89EMA after the convergence of triangle 3 is broken
5, the Angle of price rise slowed and closed below the 21 moving average when profit
6. Left field observation during transverse oscillation.
Sit tight while news or data cause prices to fall quickly
8. Buy when the price triangle breaks through the 89 moving average upward
9, the price does not rise to slow down when the horizontal closed below the 21 moving average when profit
10, price horizontal shock finishing at the same time the average line also transverse finishing field observation
11, the price of the triangle after finishing through the 89 moving average to buy.At this point all the averages have turned up
12, the second time can not break through the new high when the negative line can profit
13, the price of the first time in the same period of time through 89 after the first step back can be re-bought.
通过盘面讲解均线运用：
价格在89上面运行时时只找买入信号、
价格在89下面运行时只寻找卖出信号、
第一次向上穿过89均线后的第一次回踩确认可以顺势买入、
第一次向下穿过89均线后的第一次回抽确认可以顺势卖出、
价格横盘整理，均线频繁穿越时离场观察。
8-21区间里面黄色区域为顺势加仓信号，
价格在89均线上面时K线收盘在21天均线下面时为回调信号
价格在89均线下面时K线收盘在21天均线上面时为反弹信号
在回调和反弹信号出来之后我们应该获利一半的头寸，另外一半止损放到盈亏平衡的地方。
均线非常适合趋势性很强的品种，并不适合震荡行情。
只有在震荡行情结束时均线向上或向下发散时才有被运用的可能。
1、横盘阶段均线反复纠缠，离场观察
2、三条EMA均线向下交叉回抽无法超越89EMA时卖出
3、多次不能破新低时价格走横时获利
4、价格在3处三角形收敛被突破后站上了89EMA时买入
5、价格上涨角度变缓并收盘在21均线下面时获利
6、横盘震荡时离场观察。
7、见死不救新闻或数据导致价格快速下跌时观望
8、价格三角形向上突破时穿过89均线时买入
9、价格不升减速走横时收盘于21均线下面时获利
10、价格横盘震荡整理同时均线也横向整理时离场观察
11、价格突破三角形整理后重新穿过89均线时买入。此时所有均线已经向上翘头
12、第二次不能突破新高时收阴线可以获利
13、价格在同一个时间周期内第一次穿过89以后的第一次回踩可以重新买入。
Bitfinex Longs/Shorts Multi-Coin [acatwithcharts]This script plots the longs/shorts ratio derived from Bitfinex for BTCUSDLONGS, BTCUSDSHORTS, and similar for 11 top cryptocurrencies chosen selected based on marketcap, trading volume on Bitfinex, and the maximum number of times that TradingView would let me call the "security" function in one script. Included coins:
BTC, ETH, LTC, BCH, XRP, EOS, IOT (IOTA), ETC, ZEC, NEO, XMR
In addition to just plotting the ratios for the individual coins, this script also calculates for a customizable selection of the 11 coins both the average ratio and a weighted average weighted by (USD price of coin * sum of long and short positions).
I wrote it both to use both for a big picture overview of leveraged positions across major coins and to use as a Swiss army knife of longs/shorts ratio indicators for individual coins, most of which do not currently have individual scripts published.
I'm an amateur and you definitely shouldn't take anything I say or use any of my scripts as financial advice. I'd appreciate any feedback.
Stochastic Momentum IndexStochastic Momentum Index indicator script. This indicator was originally developed by William Blau (Stocks & Commodities V. 11:1 (11-18)).
Ram Trend Scoring (Current TF Enhanced)Overview
The Ram Trend Scoring indicator is a trend & momentum scoring tool for Forex and other instruments. It evaluates multiple technical factors on the current timeframe to classify pairs as:
8 EMA Momentum Pair – strong trending momentum
20 EMA Pullback Pair – weaker trend, possible pullback setups
It uses a points-based system, where points are added for positive factors or subtracted for failed EMA conditions.
Scoring Components
Trend Structure – price relative to EMA20
ADX Strength – trend strength (>25 strong, >20 moderate)
Distance from EMA8 – price proximity to short-term EMA
Candle Body Strength – larger bodies indicate stronger momentum
Pullback Depth – evaluates how deep the retracement is
EMA8 Wick Rejection – bullish/bearish rejection near EMA8
EMA Separation – priority #1; ≥20 pips difference required, penalty -2 if not
EMA Angle – priority #2; slope ≥30° required, penalty -2 if not
EMA Order – priority #3; correct EMA8/EMA20 alignment, penalty -2 if not
Total Score = Sum of all factor scores.
Classification Threshold: default 12
Total ≥ threshold → “8 EMA Momentum Pair”
Total < threshold → “20 EMA Pullback Pair”
Table Display
2 columns × 11 rows:
Left column = factor name
Right column = score or value
Shows total score, individual scores, and classification
Usage / How to Trade
Trend Identification
Use the indicator to quickly see momentum strength
Check EMA plots and table scores for alignment
Priority Factors
First check EMA Separation (≥20 pips)
Then EMA Angle (≥30° slope)
Then EMA Order
Only if all conditions are met, consider the setup strong
Trade Planning
8 EMA Momentum Pair → Trend continuation setups
20 EMA Pullback Pair → Wait for retracement or reversal signals
Confirmation
Combine with your usual support/resistance, FVG, or price action for entry
Higher total scores → higher probability setups
Alerts
Use the built-in alerts for “8 EMA Momentum Pair” and “20 EMA Pullback Pair”
Key Advantages
Works entirely on current timeframe → no HTF errors
Easy visual scoring table
Adjustable parameters: EMAs, ADX, ATR, angle, separation
Helps identify high-probability trend continuation or pullback trades
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio
A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent.
Table of contents
What the script does at a glance
Momentum, what it is, how to know if it is present
Volatility targeting, why and how it is done here
Portfolio construction modes: Long Only, Short Only, Delta Neutral
Regime filter and when the strategy goes to cash
Transaction cost modelling in this script
Backtest metrics and definitions
Performance attribution chart
Monte Carlo simulation
Scatter plot analysis modes
Asset allocation pie chart
Inputs, presets, and deployment checklist
Suggested workflow
1) What the script does at a glance
Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility.
Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter.
Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite.
Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views.
2) Momentum: definition, detection, and validation
Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints.
Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization.
How to know momentum is present .
Leaders and laggards persist across adjacent windows rather than flipping every bar.
Spread between average momentum of leaders and laggards is materially positive in sample.
Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak.
Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries.
Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime.
3) Volatility targeting: purpose and implementation here
Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk.
Computation in this script (per asset, rolling):
Return series ret = log(price/price ).
Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays).
Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0).
This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero.
Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations.
Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure.
4) Portfolio construction modes
Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties.
Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult.
Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs.
Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3).
Notes .
The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage.
If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta.
5) Regime filter
A fast EMA(12) vs EMA(21) filter gates exposure.
Long Only active when EMA12 > EMA21. Otherwise the book is set to cash.
Short Only active when EMA12 < EMA21. Otherwise cash.
Delta Neutral is always active.
This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar.
6) Transaction cost modelling
There are two cost touchpoints in the script.
Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar.
Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots.
Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders.
Practical advice .
If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue.
For crypto perp microcaps, stress higher cost assumptions and add slippage buffers.
If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag.
7) Backtest metrics and definitions
The script computes a standard set of portfolio statistics once the start date is reached.
Net Profit percent over the full test.
Max Drawdown percent, tracked from running peaks.
Annualized Mean and Stdev using the chosen trading day count.
Variance is the square of annualized stdev.
Sharpe uses daily mean adjusted by risk-free rate and annualized.
Sortino uses downside stdev only.
Omega ratio of sum of gains to sum of losses.
Gain-to-Pain total gains divided by total losses absolute.
CAGR compounded annual growth from start date to now.
Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM.
Skewness of daily returns.
VaR 95 linear-interpolated 5th percentile of daily returns.
CVaR average of the worst 5 percent of daily returns.
Benchmark Buy-and-Hold equity path for comparison.
8) Performance attribution
Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels.
9) Monte Carlo simulation
The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed.
Normal uses Box-Muller with your seed. Good for quick, smooth envelopes.
Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption.
Bands show 10th, 25th, 75th, 90th percentiles and the path mean.
10) Scatter plot analysis
Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels.
Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite.
Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations.
Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta.
Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk.
11) Asset allocation pie chart
Builds a wheel of current allocations.
Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent.
Short Only, weights show the short book as positive slices that sum to 100 percent.
Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional.
Labels can show asset, percent, and current leverage.
12) Inputs and quick presets
Core
Portfolio Strategy . Long Only, Short Only, Delta Neutral.
Initial Capital . For equity scaling in the panel.
Trading Days/Year . 252 for stocks, 365 for crypto.
Target Volatility . Annualized, drives volMult.
Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above.
Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier.
Start Date . Ensure every symbol has data back to this date to avoid bias.
Benchmark . Used for alpha, beta, and B&H line.
Diagnostics
Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill.
Attribution panel. Toggle winners only to focus on what matters.
Monte Carlo mode with Normal or Bootstrap and confidence bands.
Scatter plot type and styling, labels, and portfolio star.
Pie chart and labels for current allocation.
Presets
Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands.
Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward.
Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta.
13) Suggested workflow
Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees.
Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe.
Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity.
Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear.
Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it.
Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence.
Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier.
Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs.
Important implementation details mapped to code
Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting.
Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult.
Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively.
Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate.
Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison.
Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures.
Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds.
Final notes
Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call.
Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it.
Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
Aquantprice: Institutional Structure MatrixSETUP GUIDE
Open TradingView
Go to Indicators
Search: Aquantprice: Institutional Structure Matrix
Click Add to Chart
Customize:
Min Buy = 10, Min Sell = 7
Show only PP, R1, S1, TC, BC
Set Decimals = 5 (Forex) or 8 (Crypto)
USE CASES & TRADING STRATEGIES
1. CPR Confluence Trading (Most Popular)
Rule: Enter when ≥3 timeframes show Buy ≥10/15 or Sell ≥7/13
text Example:
Daily: 12/15 Buy
Weekly: 11/15 Buy
Monthly: 10/15 Buy
→ **STRONG LONG BIAS**
Enter on pullback to nearest **S1 or L3**
2. Hot Zone Scalping (Forex & Indices)
Rule: Trade only when price is in Hot Zone (closest 2 levels)
text Hot: S1-PP → Expect bounce or breakout
Action:
- Buy at S1 if Buy Count ↑
- Sell at PP if Sell Count ↑
3. Institutional Reversal Setup
Rule: Price at H3/L3 + Reversal Condition
text Scenario:
Price touches **Monthly L3**
L3 in **Hot Zone**
Buy Count = 13/15
→ **High-Probability Reversal Long**
4. CPR Width Filter (Avoid Choppy Markets)
Rule: Trade only if CPR Label = "Strong Trend"
text CPR Size < 0.25 → Trending
CPR Size > 0.75 → Sideways (Avoid)
5. Multi-Timeframe Bias Dashboard
Use "Buy" and "Sell" columns as a sentiment meter
TimeframeBuySellBiasDaily123BullishWeekly89BearishMonthly112Bullish
→ Wait for alignment before entering
HOW TO READ THE TABLE
Column Meaning Time frame D, W, M, 3M, 6M, 12MOpen Price Current session open PP, TC, BC, etc. Pivot levels (color-coded if in Hot Zone) Buy X/15 conditions met (≥10 = Strong Buy)Sell X/13 conditions met (≥7 = Strong Sell)CPR Size Histogram + Label (Trend vs Range)Zone Hot: PP-S1, Med: S2-L3, etc. + PP Distance
PRO TIPS
Best on 5M–1H charts for entries
Use with volume or order flow for confirmation
Set alerts on Buy ≥12/15 or Sell ≥10/13
Hide unused levels to reduce clutter
Combine with AQuantPrice Dashboard (Small TF) for full system
IDEAL MARKETS
Forex (EURUSD, GBPUSD, USDJPY)
Indices (NAS100, SPX500, DAX)
Crypto (BTC, ETH – use 6–8 decimals)
Commodities (Gold, Oil)
🚀 **NEW INDICATOR ALERT**
**Aquantprice: Institutional Structure Matrix**
The **ALL-IN-ONE CPR Dashboard** used by smart money traders.
✅ **6 Timeframes in 1 Table** (Daily → Yearly)
✅ **15 Buy + 13 Sell Conditions** (Institutional Logic)
✅ **Hot Zones, CPR Width, PP Distance**
✅ **Fully Customizable – Show/Hide Any Level**
✅ **Real-Time Zone Detection** (Hot, Med, Low)
✅ **Precision up to 8 Decimals**
**No more switching charts. No more confusion.**
See **where institutions are positioned** — instantly.
👉 **Add to Chart Now**: Search **"Aquantprice: Institutional Structure Matrix"**
🔥 **Free Access | Pro-Level Insights**
*By AQuant – Trusted by 10,000+ Traders*
#CPR #PivotTrading #SmartMoney #TradingView
FINAL TAGLINE
"See What Institutions See — Before They Move."
Aquantprice: Institutional Structure Matrix
Your Edge. One Dashboard.
Trendline Detector - 3 TimeframesThis advanced Pine Script indicator automatically identifies and draws diagonal support and resistance trendlines across three customizable timeframes simultaneously.
Key Features:
Multi-Timeframe Analysis: Configure three independent sets (A, B, C) to analyze different timeframes on a single chart
Smart Pivot Detection: Identifies local minimums and maximums based on open/close prices rather than wicks, reducing false signals from volatile candle shadows
Automatic Trendline Drawing: Calculates ascending support lines from pivot lows and descending resistance lines from pivot highs
Touch Validation: Only displays trendlines that meet your minimum touch requirements, ensuring statistical significance
Customizable Parameters: Full control over lookback period, pivot window size, deviation tolerance, and minimum touches for each timeframe
Visual Pivot Markers: Optional display of all detected pivot points with color-coded arrows (green for lows, red for highs)
Extended Lines: All valid trendlines extend to the right for forward projection
How It Works:
The indicator scans historical bars within your specified lookback period to identify pivot points. It then evaluates all possible trendline combinations, counting how many price points touch each potential line within your deviation tolerance. The trendline with the most touches (meeting your minimum requirement) is displayed.
Parameter Breakdown:
Each set (A, B, C) includes five critical parameters:
Timeframe: The chart timeframe for analysis (e.g., "1" for 1-minute, "15" for 15-minute, "1D" for daily)
Lookback Bars: How many historical bars to scan for pivot points (default: 250). Higher values capture longer-term trends but may increase computation time.
Min Touches: Minimum number of price touches required for a trendline to be considered valid (default: 3). Higher values ensure stronger, more reliable trendlines but may filter out emerging trends.
Deviation %: Percentage tolerance for what constitutes a "touch" (default: 0.1-1.0%). A 0.5% deviation means prices within 0.5% of the theoretical trendline are counted as touches. Lower values create stricter trendlines; higher values are more forgiving.
Pivot Window: Number of bars on each side used to identify local highs/lows (default: 5). A pivot window of 5 means the center bar must be the highest/lowest among 11 bars total (5 left + center + 5 right). Larger values identify more significant pivots but may miss shorter-term turning points.
Display Options:
Show Min/Max Points: Toggle visibility of pivot point markers to see exactly which price levels the algorithm identified as potential trendline anchors.
Perfect For:
Swing traders looking for multi-timeframe confluence zones
Technical analysts who rely on diagonal support/resistance levels
Traders who want automated trendline detection without manual drawing
Anyone seeking to identify trend channels and breakout opportunities
Color Coding:
Support lines are displayed in green with varying transparency, while resistance lines appear in red. Each timeframe set can be independently enabled/disabled based on which chart timeframe you're currently viewing, preventing clutter and maintaining clarity.
Technical Notes:
The indicator uses efficient algorithms to process large datasets while maintaining accuracy. It avoids repainting by only considering confirmed pivot points. The algorithm prioritizes trendlines with more touches and, in case of ties, favors more recent formations with steeper angles for maximum relevance.
📋 Trading Checklist – Precision Entry SystemTake your trading discipline to the next level with this Precision Trading Checklist for TradingView. Designed for intraday traders following liquidity, structure, and Smart Money Concepts (SMC) AKA ICT Concepts, this overlay ensures you never miss a key confirmation before entering a trade.
Features:
✅ Pre-Market Preparation: Track previous session highs/lows, AM/PM sessions, and key liquidity zones.
✅ Bias & Narrative Check: Quickly confirm daily trend, price position relative to daily open, and higher timeframe confluence.
✅ Session-Specific Rules: Focused sessions like Silver Bullet (10:00–11:30), Afternoon (13:30–15:00), and Final Hour (15:00–16:00).
✅ Structure & Setup Validation: Confirm liquidity sweeps, market structure shifts, expansion candles, fair value gaps, and order blocks.
✅ Risk Management Reminders: Stop-loss, target points, risk percentage, breakeven management, and pyramiding rules.
✅ Post-Trade Journaling: Document entries, session, setup type, trade outcome, and grading for continuous improvement.
✅ Golden Rules: Visual reminders to enforce discipline, avoid emotional trades, and respect session limits.
Why Use It:
This checklist is perfect for traders who want to stay consistent, minimise mistakes, and follow a disciplined routine. Displayed as an overlay on your chart, it provides all essential checks in one glance, keeping you focused on the setup rather than scrolling through notes or separate trackers.
How to use:
Add the indicator to your chart
Click the settings/gear icon
Check off items as you complete them
The checklist on your chart updates in real-time with green checkmarks!
The checkboxes will persist as long as the indicator is on your chart,
making it perfect for tracking your pre-trade and post-trade routines!
Follow the checklist items step by step before entering trades.
Use the session-specific guidelines to filter setups.
Journal your trades post-execution for growth and analysis.
Session ParmezanForex Session Range Boxes (Asia, Europe, US) — visual intraday session tracker for Forex and metals.
This indicator automatically marks the three major Forex trading sessions — Asian (Tokyo), European (London), and American (New York) — directly on your chart using dynamic colored boxes.
Each box represents the full price range (High–Low) formed during that session, helping traders visualize how volatility and liquidity evolve across the global trading day.
The script is built for intraday traders and session-based strategies, especially those who monitor breakouts from the Asian range or reactions during London–New York overlaps.
⚙️ Features
• Accurate session timing (UTC+3 / Moscow Time) — Asia: 03:00–12:00, Europe: 11:00–20:00, US: 16:00–01:00.
• Dynamic range boxes: each box expands in real time as new highs and lows are set during the session.
• Clear visual separation: each session is shown in its own color (blue for Asia, orange for Europe, green for US).
• Automatic daily reset — new boxes start every new session.
• Intraday focus only — visible up to the 1-hour timeframe (M1–H1) for clarity.
• Transparent design — semi-transparent fills keep candles readable even when sessions overlap.
• Lightweight performance — optimized use of box.new() and var variables avoids lag on lower timeframes.
🧭 Typical Use-Cases
• Identify Asian session ranges and watch for London breakouts or New York reversals.
• Visually align your intraday strategy with session volatility cycles.
• Combine with VWAP, liquidity zones, or market profile indicators for deeper confluence.
• Spot overlapping sessions — often the most active periods of the day.
Tristan's Tri-band StrategyTristan's Tri-band Strategy - Confluence Trading System
Strategy Overview:
This strategy combines three powerful technical indicators - RSI, Williams %R, and Bollinger Bands - into a single visual trading system. Instead of cluttering your chart with separate indicator panels, all signals are displayed directly on the price chart using color-coded gradient overlays, making it easy to spot high-probability trade setups at a glance.
How It Works:
The strategy identifies trading opportunities when multiple indicators align (confluence), suggesting strong momentum shifts:
📈 Long Entry Signals:
RSI drops to 30 or below (oversold)
Williams %R reaches -80 to -100 range (oversold)
Price touches or breaks below the lower Bollinger Band
All three conditions must align during your selected trading session
📉 Short Entry Signals:
RSI rises to 70 or above (overbought)
Williams %R reaches 0 to -20 range (overbought)
Price touches or breaks above the upper Bollinger Band
All three conditions must align during your selected trading session
Visual Indicators:
(faint) Green gradients below candles = Bullish oversold conditions (buying opportunity)
(faint) Red/Orange gradients above candles = Bearish overbought conditions (selling opportunity)
Stacked/brighter gradients = Multiple indicators confirming the same signal (higher probability) will stack and show brighter / less faint
Blue Bollinger Bands = Volatility boundaries and mean reversion zones
Exit Strategy:
Long trades exit when price reaches the upper Bollinger Band OR RSI becomes overbought (≥70)
Short trades exit when price reaches the lower Bollinger Band OR RSI becomes oversold (≤30)
Key Features:
✅ Session Filters - Trade only during NY (9:30 AM-4 PM), London (3 AM-11:30 AM), or Asia (7 PM-1 AM EST) sessions
✅ No Repainting - Signals are confirmed on candle close for realistic backtesting and live trading
✅ Customizable Parameters - Adjust RSI levels, BB standard deviations, Williams %R periods, and gradient visibility
✅ Visual Clarity - See all three indicators at once without switching between panels
✅ Built-in Alerts - Get notified when entry and exit conditions are met
How to Use Effectively:
Choose Your Trading Session - For day trading US stocks, enable only the NY session. For forex or 24-hour markets, select the sessions that match your schedule.
Look for Gradient Stacking - The brightest, most visible gradients occur when both RSI and Williams %R signal together. These are your highest-probability setups.
Confirm with Price Action - Wait for the candle to close before entering. The strategy enters on the next bar's open to prevent repainting.
Respect the Bollinger Bands - Entries occur at the outer bands (price extremes), and exits occur at the opposite band or when momentum reverses.
Backtest First - Test the strategy on your preferred instruments and timeframes. Works best on liquid assets with clear trends and mean reversion patterns (stocks, major forex pairs, indices).
Adjust Gradient Visibility - Use the "Gradient Strength" slider (lower = more visible) to make signals stand out on your chart style.
Best Timeframes: 5-minute to 1-hour charts for intraday trading; 4-hour to daily for swing trading (I have also found the 3 hour timeframe to work really well for some stocks / ETFs.)
Best Markets: Liquid instruments with volatility - SPY, QQQ, major stocks, EUR/USD, GBP/USD, major indices
Risk Management: This is a mean reversion strategy that works best in ranging or choppy markets. In strong trends, signals may appear less frequently. Always use proper position sizing and stop losses based on your risk tolerance.
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Note: Past performance does not guarantee future results. This strategy is provided for educational purposes. Always backtest thoroughly and practice proper risk management before live trading.RetryClaude can make mistakes. Please double-check responses. Sonnet 4.5
Sector Analysis [SS]Introducing the most powerful sector analysis tool/indicator available, to date, in Pine!
This is a whopper indicator, so be sure to read carefully to ensure you understand its applications and uses!
First of all, because this is a whopper, let's go over the key functional points of the indicator.
The indicator compares the 11 main sector ETFs against whichever ticker you are looking at.
The functions include the following:
Ability to pull technicals from the sectors, such as RSI, Stochastic and Z-Score;
Ability to look at the correlation of the sector ETF to the current ticker you are looking at.
Ability to calculate the R2 value between the ticker you are looking at and each sector.
The ability to run a Two Tailed T-Test against the log returns of the Ticker of interest and the Sector (to analyze statistically significant returns between sectors/tickers).
The ability to analyze the distribution of returns across all sector ETFs.
The ability to pull buying and selling volume across all sector ETFs.
The ability to create an integrated moving average using a sector ETF to predict the expected close range of a ticker of interest.
These are the highlight functions. Below, I will go more into them, what they mean and how to use them.
Pulling Technicals
This is pretty straight forward. You can pull technicals, such as RSI, Stochastic and Z-Score from all the sector ETFs and view them in a table.
See below for the example:
Pulling Correlation
In order to see which sector your ticker of interest follows more closely, we need to look first at correlation and then at R2.
The correlation will look at the immediate relationship over a specified time. A highly positive value, indicates a strong, symbiotic relationship, which the sector and the ticker follow each other. This would be represented by a correlation of 0.8 or higher.
A strong negative correlation, such as -0.8 or lower, indicates that the sector and the ticker are completely opposite. When one goes up, the other goes down and vice versa.
You can adjust your correlation assessment length directly in the settings menu:
If you want to use a sector ETF to find the expected range for a ticker of interest, it is important to locate the highest, POSITIVE, correlation value. Here are the results for MSFT at a correlation lookback of 200:
In this example, we can see the best relationship is with the ETF XLK.
Analysis of R2
R2 is an important metric. It essentially measures how much of the variance between 2 tickers are explained by a simple, linear relationship.
A high R2 means that a huge degree of variance can be explained between the 2 tickers. A low R2 means that it cannot and that the 2 tickers are likely not integrated or closely related.
In general, if you want to use the sector ETF to find the mean and trading range and identify over-valuation/over-extension and under-extension statistically, you need to see both a high correlation and a high R-Squared. These 2 metrics should be analyzed together.
Let's take a look at MSFT:
Here, despite the correlation implying that XLK was the ticker we should use to analyze, when we look at the R Squared, we see actually, we should be using XLI.
XLI has a strong positive relationship with MSFT, albeit a bit less than XLK, but the R2 is solid, > 0.9, indicating the XLI explains much of MSFT's variance.
Two Tailed T-Test
A two tailed T-test analyzes whether there is a statistically significant difference between 2 different groups, or in our case, tickers.
The T-Test is conducted on the log returns of the ticker of interest and the sector. You then can see the P value results, whether it is significant or not. Let's look at MSFT again:
Looking at this, we can see there is no statistically significant difference in returns between MSFT and any of the sectors.
We can also see the SMA of the log returns for more detailed comparison.
If we were to observe a significant finding on the T-Test metrics, this would indicate that one sector either outperforms or underperforms your ticker to a statistically significant degree! If you stumble upon this, you would check the average log returns to compare against the average returns of your ticker of interest, to see whether there is better performance or worse performance from the sector ETF vs. your ticker of interest.
Analyzing the Distribution
The indicator will also analyze the distribution of returns.
This is an interesting option as it can help you ascertain risk. Normally distributed returns imply mean reverting behavviour. Deviations from that imply trending behaviour with higher risk expectancy. If we look at the distribution statistics currently over the last 200 trading days, here are the results:
Here, we can see all show signs of trending, as none of the returns are normally distributed. The highest risk sectors are XLK and XLY.
Why are they the highest risk?
Because the indicator has found a heavy right tailed distribution, indicated sudden and erratic mean reversion/losses are possible.
Creating an MA
Now for the big bonus of the indicator!
The indicator can actually create a regression based range from closely correlated sectors, so you can see, in sectors that are strongly correlated to your ticker, whether your ticker is over-bought, oversold or has mean reverted.
Let's look at MSFT using XLI, our previously identified sector with a high correlation and high R2 value:
The results are pretty impressive.
You can see that MSFT has rode the mean of the sector on the daily timeframe for quite some time. Each time it over extended itself above the sector implied range, it mean reverted.
Currently, if you were to trade based on Pairs or statistics, MSFT is no trade as it is currently trading at its sector mean.
If you are a visual person, you can have the indicator plot the mean reversion points directly:
Green represents a bullish mean reversion and red a bearish mean reversion.
Concluding Remarks
If you like pair trading, following the link between sectors and tickers or want a more objective way to determine whether a ticker is over-bought or oversold, this indicator can help you.
In addition to doing this, the indicator can provide risk insights into different sectors by looking at the distribution, as well as identify under-performing sectors or tickers.
It can also shed light on sectors that may be technically over-bought or oversold by looking at Z-Score, stochastics and RSI.
Its a whopper and I really hope you find it helpful and useful!
Thanks everyone for reading and checking this out!
Safe trades!
