Concepts — Indicadores e Estratégias

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.

Smart Structure Breaks & Order Blocks Overview (What it does) The indicator “Smart Structure Breaks & Order Blocks” detects market structure using swing highs and lows, identifies Break of Structure (BOS) events, and automatically draws order blocks (OBs) from the origin candle. These zones extend to the right and change color/outline when mitigated or invalidated. By formalizing and automating part of discretionary analysis, it provides consistent zone recognition. Main Components Swing Detection: ta.pivothigh/ta.pivotlow identify confirmed swing points. BOS Detection: Determines if the recent swing high/low is broken by close (strict mode) or crossover. OB Creation: After a BOS, the opposite candle (bearish for bullish BOS, bullish for bearish BOS) is used to generate an order block zone. Zone Management: Limits the number of zones, extends them to the right, and tracks tagged (mitigated) or invalidated states. Input Parameters Left/Right Pivot (default 6/6): Number of bars required on each side to confirm a swing. Higher values = smoother swings. Max Zones (default 4): Maximum zones stored per direction (bull/bear). Oldest zones are overwritten. Zone Confirmation Lookback (default 3): Ensures OB origin candle validity by checking recent highs/lows. Show Swing Points (default ON): Displays triangles on swing highs/lows. Require close for BOS? (default ON): Strict BOS (close required) vs loose BOS (line crossover). Use candle body for zones (default OFF): Zones drawn from candle body (ON) or wick (OFF). Signal Definition & Logic Swing Updates: Latest confirmed pivots update lastHighLevel / lastLowLevel. BOS (Break of Structure): 　Bullish – close breaks last swing high. 　Bearish – close breaks last swing low. 　Only one valid BOS per swing (avoids duplicates). OB Detection: 　Bullish BOS → previous bearish candle with lowest low forms the OB. 　Bearish BOS → previous bullish candle with highest high forms the OB. 　Zones: Bull = green, Bear = red, semi-transparent, extended to the right. Zone States: 　Mitigated: Price touches the zone → border highlighted. 　Invalidated: 　　Bull zone → close below → turns red. 　　Bear zone → close above → turns green. Chart Appearance Swing High: red triangle above bar Swing Low: green triangle below bar Bull OB: green zone (border highlighted on touch) Bear OB: red zone (border highlighted on touch) Invalid Zones: Bull zones turn reddish, Bear zones turn greenish Practical Use (Trading Assistance) Trend Following Entries: Buy pullbacks into green OBs in uptrends, sell rallies into red OBs in downtrends. Focus on First Touch: First mitigation after BOS often has higher reaction probability. Confluence: Combine with higher timeframe trend, volume, session levels, key price levels (previous highs/lows, VWAP, etc.). Stops/Targets: 　Bull – stop below zone, partial take profit at swing high or resistance. 　Bear – stop above zone, partial take profit at swing low or support. Parameter Tuning (per market/timeframe) Pivot (6/6 → 4/4/8/8): Lower for scalping (3–5), medium for day trading (5–8), higher for swing trading (8–14). Increase to reduce noise. Strict Break: ON to reduce false breaks in ranging markets; OFF for earlier signals. Body Zones: ON for assets with long wicks, OFF for cleaner OBs in liquid instruments. Zone Confirmation (default 3): Increase for stricter OB origin, fewer zones. Max Zones (default 4 → 6–10): Increase for higher volatility, decrease to avoid clutter. Strengths Standardizes BOS and OB detection that is usually subjective. Tracks mitigation and invalidation automatically. Adaptable: allows body/wick zone switching for different instruments. Limitations Pivot-based: Signals appear only after pivots confirm (slight lag). Zones reflect past balance: Can fail after new events (news, earnings, macro data). Range-heavy markets: More false BOS; consider stricter settings. Backtesting: This script is for drawing/visual aid; trading rules must be defined separately. Workflow Example Identify higher timeframe trend (4H/Daily). On lower TF (15–60m), wait for BOS and new OB. Enter on first mitigation with confirmation candle. Stop beyond zone; targets based on R multiples and swing points. FAQ Q: Why are zones invalidated quickly? A: Flow reversal after BOS. Adjust pivots higher, enable Strict mode, or switch to Body zones to reduce noise. Q: What does “tagged” mean? A: Price touched the zone once = mitigated. Implies some orders in that zone may have been filled. Q: Body or Wick zones? A: Wick zones are fine in clean markets. For volatile pairs with long wicks, body zones provide more realistic areas. Customization Tips (Code perspective) Zone storage: Currently ring buffer ((idx+1) % zoneLimit). Could prioritize keeping unmitigated zones. Automated testing: Add strategy.entry/exit for rule-based backtests. Multi-timeframe: Use request.security() for higher timeframe swings/BOS. Visualization: Add labels for BOS bars, tag zones with IDs, count touches. Summary This indicator formalizes the cycle Swing → BOS → OB creation → Mitigation/Invalidation, providing consistent structure analysis and zone tracking. By tuning sensitivity and strictness, and combining with higher timeframe context, it enhances pullback/continuation trading setups. Always combine with proper risk management.

TrendSync Pro (SMC)📊 TrendSync Pro (SMC) – Advanced Trend-Following Strategy with HTF Alignment Created by Shubham Singh 🔍 Strategy Overview TrendSync Pro (SMC) is a precision-based smart trend-following strategy inspired by Smart Money Concepts (SMC). It combines: Real-time pivot-based trendline detection Higher Time Frame (HTF) filtering to align trades with dominant trend Risk management via adjustable Stop Loss (SL) and Take Profit (TP) Directional control — trade only bullish, bearish, or both setups Realistic backtesting using commissions and slippage Pre-optimized profiles for scalpers, intraday, swing, and long-term traders 🧠 How It Works: 🔧 Strategy Settings Image: beeimg.com The strategy dynamically identifies trend direction by using swing high/low pivots. When a new pivot forms: It draws a trendline from the last significant pivot Detects whether the trend is up (based on pivot lows) or down (based on pivot highs) Waits for price to break above/below the trendline Confirms with HTF price direction (HTF close > previous HTF close = bullish) Only then it triggers a long or short trade It exits either at TP, SL, or a manual trendline break 🛠️ Adjustable Parameters: Trend Period: Length for pivot detection (affects sensitivity of trendlines) HTF Timeframe: Aligns lower timeframe entries with higher timeframe direction SL% and TP%: Customize your risk-reward profile Commission & Slippage: Make backtests more realistic Trade Direction: Choose to trade: Long only, Short only, or Both 🎛️ Trade Direction Control: In settings, you can choose: Bullish Only: Executes only long entries Bearish Only: Executes only short entries Both: Executes both long and short entries when conditions are met This allows you to align trades with your own market bias or external analysis. 📈 Entry Logic: Long Entry: • Price crosses above trendline • HTF is bullish (HTF close > previous close) • Latest pivot is a low (trend is considered up) Short Entry: • Price crosses below trendline • HTF is bearish (HTF close < previous close) • Latest pivot is a high (trend is considered down) 📉 Exit Logic: Hit Take Profit or Stop Loss Manual trendline invalidation: If price crosses opposite of the trend direction ⏰ Best Timeframes & Recommended Settings: Scalping (1m to 5m): HTF = 15m | Trend Period = 7 SL = 0.5% | TP = 1% to 2% Intraday (15m to 30m): HTF = 1H | Trend Period = 10–14 SL = 0.75% | TP = 2% to 3% 6 Hour Trading (30m to 1H): HTF = 4H | Trend Period = 20 SL = 1% | TP = 4% to 6% Swing Trading (4H to 1D): HTF = 1D | Trend Period = 35 SL = 2% | TP = 8% to 12% Long-Term Investing (1D+): HTF = 1W | Trend Period = 50 SL = 3% | TP = 15%+ Note: These are recommended base settings. Adjust based on volatility, asset class, or personal trading style. 📸 Testing Note: beeimg.com TradingView limits test length to 20k bars (~40 trades on smaller timeframes). To show long-term results: Test on higher timeframes (e.g., 1H, 4H, 1D) Share images of backtest result in description Host longer test result screenshots on Imgur or any public drive 📍 Asset Behavior Insight: This strategy works on multiple assets, including BTC, ETH, etc. Performance varies by trend strength: Sometimes BTC performs better than ETH Other times ETH gives better results That’s normal as both assets follow different volatility and trend behavior It’s a trend-following setup. Longer and clearer the trend → better the results. ✅ Best Practices: Avoid ranging markets Use proper SL/TP for each timeframe Use directional filter if you already have a directional bias Always forward test before going live ⚠️ Trading Disclaimer: This script is for educational and backtesting purposes only. Trading involves risk. Always use risk management and never invest more than you can afford to lose.

Imbalance(FVG) Detector Imbalance (FVG) Detector Overview The Imbalance (FVG) Detector is a technical analysis tool designed to highlight price inefficiencies by identifying Fair Value Gaps (FVGs). These gaps occur when rapid price movement leaves an area with little to no traded volume, which may later act as a zone of interest. The indicator automatically detects and marks these imbalances on the chart, allowing users to observe historical price behavior more effectively. Key Features - Automatic Imbalance Detection: Identifies bullish and bearish imbalances based on a structured three-bar price action model. - Customizable Sensitivity: Users can adjust the minimum imbalance percentage threshold to tailor detection settings to different assets and market conditions. - Real-time Visualization: Marked imbalances are displayed as colored boxes directly on the chart. - Dynamic Box Updates: Imbalance zones extend forward in time until price interacts with them. - Alert System: Users can set alerts for when new imbalances appear or when price tests an existing imbalance. How It Works The indicator identifies market imbalances using a three-bar price structure: - Bullish Imbalance: Occurs when the high of three bars ago is lower than the low of the previous bar, forming a price gap. - Bearish Imbalance: Occurs when the low of three bars ago is higher than the high of the previous bar, creating a downward gap. When an imbalance is detected: - Green Boxes indicate bullish imbalances. - Red Boxes indicate bearish imbalances. - Once price interacts with an imbalance, the box fades to gray, marking it as tested. ! Designed for Crypto Markets This indicator is particularly useful in crypto markets, where frequent volatility can create price inefficiencies. It provides a structured way to visualize gaps in price movement, helping users analyze historical liquidity areas. Customization Options - Min Imbalance Percentage Size: Adjusts the sensitivity of the imbalance detection. - Alerts: Users can enable alerts to stay notified of new or tested imbalances. Important Notes - This indicator is a technical analysis tool and does not provide trading signals or financial advice. - It does not predict future price movement but highlights historical price inefficiencies. - Always use this tool alongside other market analysis methods and risk management strategies.

NormInvTargetSeeker NormInvTargetSeeker The NormInvTargetSeeker is a trading tool designed to aid traders in identifying and capitalizing on Distribution and Accumulation zones, highlighting specific price levels that could serve as targets for future price movements. Although the indicator itself is not multi-timeframe, an effective trading strategy might involve signal validation across multiple timeframes. 🔶 USAGE The indicator identifies Distribution and Accumulation zones, providing potential targets for future price moves. Traders are encouraged to use these zones as profit targets or potential reversal points. Confluence Zones These zones are identified as regions where various factors or levels converge, signaling an increased probability of price reaction. They can be used to reinforce signals or identify levels where price might encounter significant resistance or support. 🔹 Trading Strategy First, identify a signal on your primary trading timeframe. Manually check higher timeframes to ensure the signal aligns with them. Use the identified zones, whether Distribution or Accumulation, as target zones for your trades. 🔶 Order Blocks The NormInvTargetSeeker identifies "Order Blocks" by examining a specified number of consecutive candles with a specific condition: the current candle must completely engulf the previous candle. This means that both the high and low of the current candle are higher and lower, respectively, than the high and low of the previous candle, signifying a dominant move in the direction of the current candle. 🔹 Trading Strategy Target Confirmation: Order Blocks can serve to confirm target points, providing additional validation for identified levels. Market Insight: They offer crucial insights into whether "big hands" or institutional players are positioned as buyers or sellers in the market. Traders can use Order Blocks as a means to validate targets or key price levels, observing if the price reacts significantly upon reaching these blocks. They can also provide insights into the general market direction or underlying market strength by identifying where the major market players are placing their orders. 🔶 SETTINGS The indicator allows users to adjust various parameters to customize the display and logic of the tool to fit their needs. 🔹 Display Settings Users can customize the colors and displays of various zones and labels to match their preferences. 🔶 LICENSE AND CREDITS This work is licensed under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). More information here: creativecommons.org This indicator utilizes a TypeScript implementation of the Normal Inverse function as a reference, which can be found here : github.com Special thanks to the authors of the referenced code for providing a foundation upon which this indicator was built. 🔶 UPDATES Current Version: 1.0.0 For future updates, please check the comment section. 🔶 CONTACT For any questions or suggestions, please feel free to contact @RickSimpson on TradingView.

ADR/AWR/AMR Average Daily+Weekly+Monthly Range[Traders Reality]Advanced ADR/AWR/AMR indicator created for Traders Reality community, as well as the greater trading community. Thanks to the TR community discord guys: infernix, peshocore and xtech5192 Everything is modular and can be turned on/off, including a customisable table showing daily/weekly/monthly average pips/dollars. If you just want the average daily range lines for example, you can just disable everything else. You can choose how many days to look back; as well as for weeks or months. Check out Traders Reality on YouTube if you want to see this implemented as part of Tino's strategy that utilizes market manipulation, imbalances, times of day etc. Price regularly reverses from ADR, making it one of the few highly valuable indicators in price action/smart money trading.