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指标和策略

Trendline Breakouts With Targets [ omerprıme ]Indicator Explanation (English) This indicator is designed to detect trendline breakouts and provide early trading signals when the price breaks key support or resistance levels. Trendline Detection The indicator identifies recent swing highs and lows to construct dynamic trendlines. These trendlines act as support in an uptrend and resistance in a downtrend. Breakout Confirmation When the price closes above a resistance trendline, the indicator generates a bullish breakout signal. When the price closes below a support trendline, it generates a bearish breakout signal. Filtering False Signals To reduce false breakouts, additional conditions (such as candle confirmation, volume filters, or price momentum) can be applied. Only significant and confirmed breakouts are highlighted. Trading Logic Buy signals are triggered when the price breaks upward through resistance with confirmation. Sell signals are triggered when the price breaks downward through support with confirmation.
Pine Script®指标
由gomer3355提供
EMA 20+50 + MACD Strateji ( omerprıme)EASY BUY-SELL basitçe al -sat yapabileceğiniz macd indikatörü ve ema kullanılmış bir indikatördür unutmayın ki ne kadar basit o kadar verimli. Moving Averages) to generate trading signals and trend confirmation. Trend Identification with EMA Two EMAs are used to determine the overall market trend (commonly a short-term EMA and a long-term EMA). When the short EMA crosses above the long EMA, it indicates an uptrend. When the short EMA crosses below the long EMA, it signals a downtrend. Signal Confirmation with MACD The MACD line and Signal line are analyzed to detect momentum shifts. A bullish signal occurs when the MACD line crosses above the Signal line, especially if the EMAs confirm an uptrend. A bearish signal occurs when the MACD line crosses below the Signal line, especially if the EMAs confirm a downtrend. Trading Logic Buy signals appear only when both the EMA trend is bullish and the MACD confirms momentum to the upside. Sell signals appear only when both the EMA trend is bearish and the MACD confirms momentum to the downside.
Pine Script®指标
由gomer3355提供
Advantage RSI PredictorThe Relative Strength Index (RSI) is a widely used momentum oscillator that measures the speed and change of price movements, typically on a scale from 0 to 100, to identify overbought (above 70) or oversold (below 30) conditions. However, its reliance on historical data limits its ability to predict future price movements. To overcome this, an advanced indicator—termed the Advanced RSI Predictor (ARP)—can be developed to provide predictive bands for RSI levels, enhancing its forecasting potential.The ARP leverages machine learning techniques, such as Long Short-Term Memory (LSTM) networks, combined with traditional RSI calculations to forecast future RSI values and establish confidence intervals or bands. These bands represent a range within which the RSI is likely to fluctuate over a specified period, offering traders a probabilistic perspective on momentum shifts. The indicator starts with the standard RSI computation, using a 14-period lookback as a foundation, but enriches this by incorporating additional inputs like moving averages, volatility measures (e.g., Bollinger Bands width), and trading volume. These features are processed through an LSTM model trained on historical price and RSI data to predict future RSI trajectories.The output includes upper and lower predictive bands, typically set at a 95% confidence level, surrounding a central forecasted RSI line. For example, if the current RSI is 45, the ARP might project a band from 40 to 50 over the next five days, indicating potential momentum stability or a range for overbought/oversold thresholds. The bands adapt dynamically to market conditions—narrowing during stable trends and widening during volatile periods—using real-time data updates. This adaptability allows traders to anticipate breakouts or reversals before they manifest on the price chart.Validation can be strengthened through backtesting against historical data, ensuring the ARP’s bands align with significant market turns. This indicator proves especially valuable in trending markets, where traditional RSI levels (e.g., 70 or 30) may falter, offering a sophisticated tool for informed trading or investment decisions.
Pine Script®指标
由FomoFutures提供
Estrategia MACD//@version=6 indicator("Trading Sessions", overlay = true, max_boxes_count = 500, max_lines_count = 500, max_labels_count = 500) bool showSessionNames = input.bool(true, "Show session names") bool showSessionOC = input.bool(true, "Draw session open and close lines") bool showSessionTickRange = input.bool(true, "Show tick range for each session") bool showSessionAverage = input.bool(true, "Show average price per session") const string TZ_TOOLTIP_TEXT = "The session's time zone, specified in either GMT notation (e.g., 'GMT-5') or as an IANA time zone database name (e.g., 'America/New_York')." + " We recommend the latter since it includes other time-related changes, such as daylight savings." const string FIRST_SESSION_GROUP = "First Session" showFirst = input.bool(true, "Show session", group = FIRST_SESSION_GROUP, display = display.none) firstSessionName = input.string("Tokyo", "Displayed name", group = FIRST_SESSION_GROUP, display = display.none) firstSessionTime = input.session("0900-1500", "Session time", group = FIRST_SESSION_GROUP, display = display.none) firstSessionTZ = input.string("Asia/Tokyo", "Session timezone", group = FIRST_SESSION_GROUP, display = display.none, tooltip = TZ_TOOLTIP_TEXT) firstSessionColor = input.color(color.new(#2962FF, 85), "Session color", group = FIRST_SESSION_GROUP) const string SECOND_SESSION_GROUP = "Second session" showSecond = input.bool(true, "Show session", group = SECOND_SESSION_GROUP, display = display.none) secondSessionName = input.string("London", "Displayed name", group = SECOND_SESSION_GROUP, display = display.none) secondSessionTime = input.session("0830-1630", "Session time", group = SECOND_SESSION_GROUP, display = display.none) secondSessionTZ = input.string("Europe/London", "Session timezone", group = SECOND_SESSION_GROUP, display = display.none, tooltip = TZ_TOOLTIP_TEXT) secondSessionColor = input.color(color.new(#FF9800, 85), "Session color", group = SECOND_SESSION_GROUP) const string THIRD_SESSION_GROUP = "Third session" showThird = input.bool(true, "Show session", group = THIRD_SESSION_GROUP, display = display.none) thirdSessionName = input.string("New York", "Displayed name", group = THIRD_SESSION_GROUP, display = display.none) thirdSessionTime = input.session("0930-1600", "Session time", group = THIRD_SESSION_GROUP, display = display.none) thirdSessionTZ = input.string("America/New_York", "Session timezone", group = THIRD_SESSION_GROUP, display = display.none, tooltip = TZ_TOOLTIP_TEXT) thirdSessionColor = input.color(color.new(#089981, 85), "Session color", group = THIRD_SESSION_GROUP) type SessionDisplay box sessionBox label sessionLabel line openLine line avgLine line closeLine float sumClose int numOfBars type SessionInfo color color string name string session string timezone SessionDisplay active = na method setName(SessionDisplay this, string name) => sessionLabel = this.sessionLabel sessionBox = this.sessionBox boxText = array.new() if showSessionTickRange boxText.push("Range: " + str.tostring((sessionBox.get_top() - sessionBox.get_bottom()) / syminfo.mintick, format.mintick)) if showSessionAverage boxText.push("Avg: " + str.tostring(this.sumClose / this.numOfBars, format.mintick)) if showSessionNames boxText.push(name) sessionLabel.set_y(sessionBox.get_bottom()) sessionLabel.set_text(array.join(boxText, " ")) method createSessionDisplay(SessionInfo this) => boxColor = this.color opaqueColor = color.new(boxColor, 0) dis = SessionDisplay.new( sessionBox = box.new(bar_index, high, bar_index, low, bgcolor = boxColor, border_color = na), sessionLabel = label.new(bar_index, low, "", style = label.style_label_upper_left, textalign = text.align_left, textcolor = opaqueColor, color = color(na)), openLine = showSessionOC ? line.new(bar_index, open, bar_index, open, color = opaqueColor, style = line.style_dashed, width = 1) : na, closeLine = showSessionOC ? line.new(bar_index, close, bar_index, close, color = opaqueColor, style = line.style_dashed, width = 1) : na, avgLine = showSessionAverage ? line.new(bar_index, close, bar_index, close, style = line.style_dotted, width = 2, color = opaqueColor) : na, sumClose = close, numOfBars = 1 ) linefill.new(dis.openLine, dis.closeLine, boxColor) dis.setName(this.name) this.active := dis method updateSessionDisplay(SessionInfo this) => sessionDisp = this.active sessionBox = sessionDisp.sessionBox openLine = sessionDisp.openLine closeLine = sessionDisp.closeLine avgLine = sessionDisp.avgLine sessionDisp.sumClose += close sessionDisp.numOfBars += 1 sessionBox.set_top(math.max(sessionBox.get_top(), high)) sessionBox.set_bottom(math.min(sessionBox.get_bottom(), low)) sessionBox.set_right(bar_index) sessionDisp.setName(this.name) if showSessionOC openLine.set_x2(bar_index) closeLine.set_x2(bar_index) closeLine.set_y1(close) closeLine.set_y2(close) if showSessionAverage avgLine.set_x2(bar_index) avg = sessionDisp.sumClose / sessionDisp.numOfBars avgLine.set_y1(avg) avgLine.set_y2(avg) sessionDisp method update(SessionInfo this) => bool isChange = timeframe.change("1D") if (not na(time("", this.session, this.timezone))) // inSession if na(this.active) or isChange this.createSessionDisplay() else this.updateSessionDisplay() else if not na(this.active) this.active := na getSessionInfos()=> array sessionInfos = array.new() if showFirst sessionInfos.push(SessionInfo.new(firstSessionColor, firstSessionName, firstSessionTime, firstSessionTZ)) if showSecond sessionInfos.push(SessionInfo.new(secondSessionColor, secondSessionName, secondSessionTime, secondSessionTZ)) if showThird sessionInfos.push(SessionInfo.new(thirdSessionColor, thirdSessionName, thirdSessionTime, thirdSessionTZ)) sessionInfos var array sessionInfos = getSessionInfos() if timeframe.isdwm runtime.error("This indicator can only be used on intraday timeframes.") for info in sessionInfos info.update()
Pine Script®指标
由arissontoguchi提供
Brownian Motion Probabilistic Forecasting (Time Adaptive)Probabilistic Price Forecast Indicator Overview The Probabilistic Price Forecast is an advanced technical analysis tool designed for the TradingView platform. Instead of predicting a single future price, this indicator uses a Monte Carlo simulation to model thousands of potential future price paths, generating a cone of possibilities and calculating the probability of specific outcomes. This allows traders to move beyond simple price targets and ask more sophisticated questions, such as: "What is the probability that this stock will increase by 5% over the next 24 hours?" Core Concept: Geometric Brownian Motion The indicator's forecasting model is built on the principles of Geometric Brownian Motion (GBM) , a widely accepted mathematical model for describing the random movements of financial asset prices. The core idea is that the next price step is a function of the asset's historical trend (drift), its volatility, and a random "shock." The formula used to project each price step in the simulation is: next_price = current_price * exp( (μ - (σ²/2))Δt + σZ√(Δt) ) Where: μ (mu) represents the drift , which is the average historical return. σ (sigma) represents the volatility , measured by the standard deviation of historical returns. Z is a random variable from a standard normal distribution, representing the random "shock" or new information affecting the price. Δt (delta t) is the time step for each projection. How It Works The indicator performs a comprehensive analysis on the most recent bar of the chart: **Historical Analysis**: It first analyzes a user-defined historical period (e.g., the last 240 hours of price data) to calculate the asset's historical drift (μ) and volatility (σ) from its logarithmic returns. **Monte Carlo Simulation**: It then runs thousands of simulations (e.g., 2000) of future price paths over a specified forecast period (e.g., the next 24 hours). Each path is unique due to the random shock (Z) applied at every step. **Probability Distribution**: After all simulations are complete, it collects the final price of each path and sorts them to build a probability distribution of potential outcomes. **Visualization and Signaling**: Finally, it visualizes this distribution on the chart and generates signals based on the user's criteria. Key Features & Configuration The indicator is highly configurable, allowing you to tailor its analysis to your specific needs. Time-Adaptive Periods The lookback and forecast periods are defined in hours , not bars. The script automatically converts these hour-based inputs into the correct number of bars based on the chart's current timeframe, ensuring the analysis remains consistent across different chart resolutions. Forecast Quartiles You can visualize the forecast as a "cone of probability" on the chart. The indicator draws lines and a shaded area representing the price levels for different quartiles (percentiles) of the simulation results. By default, this shows the range between the 25th and 95th percentiles. Independent Bullish and Bearish Signals The indicator allows you to set independent criteria for bullish and bearish signals, providing greater flexibility. You can configure: A bullish signal for an X% confidence of a Y% price increase. A bearish signal for a W% confidence of a Z% price decrease. For example, you can set it to alert you for a 90% chance of a 2% drop, while simultaneously looking for a 60% chance of a 10% rally. How to Interpret the Indicator The Forecast Cone : The blue shaded area on the chart represents the probable range of future prices. The width of the cone indicates the expected volatility; a wider cone means higher uncertainty. The price labels on the right side of the cone show the calculated percentile levels at the end of the forecast period. Green Signal Label : A green "UP signal" label appears when the probability of the price increasing by your target percentage exceeds your defined confidence level. Red Signal Label : A red "DOWN signal" label appears when the probability of the price decreasing by your target percentage exceeds your confidence level. This tool provides a statistical edge for understanding future possibilities but should be used in conjunction with other analysis techniques.
Pine Script®指标
由mattimeo_mouse提供
AutoDay MA (Session-Normalized)📊 AutoDay MA (Session-Normalized Moving Average) ⚡ Daily power, intraday precision. AutoDay MA automatically converts any N-day moving average into the exact equivalent on your current intraday timeframe. 💡 Concept inspired by Brian Shannon (Alphatrends) – mapping daily MAs onto intraday charts by normalizing session minutes. 🛠 How it works Set Days (N) (e.g., 5, 10, 20). Define Session Minutes per Day (⏱ 390 = US RTH, 🌍 1440 = 24h). The indicator detects your chart’s timeframe and computes: Length = (Days × SessionMinutes) / BarMinutes Applies your chosen MA type (📐 SMA / EMA / RMA / WMA) with rounding (nearest, up, down). Displays all details in a clear corner info panel. ✅ Why use it Consistency 🔄: Same 5-day smoothing across all intraday charts. Session-aware 🕒: Works for equities, futures, FX, crypto. Transparency 🔍: Always shows the math & final MA length. Alerts built-in 🔔: Cross up/down vs. price. 📈 Examples 5-Day on 1m → 1950-period MA 5-Day on 15m → 130-period MA 5-Day on 65m → 30-period MA 10-Day on 24h/15m (crypto) → 960-period MA
Pine Script®指标
由TradeQUO提供
4
3SMA (1H only) by tophengzkyThis script plots three Simple Moving Averages (SMA 10, 20, 50), but they are only visible when the chart timeframe is set to 1 hour (1H). It helps traders focus on higher timeframe trend direction without cluttering charts on other timeframes. SMA1 = 10 (white) SMA2 = 20 (yellow) SMA3 = 200 (red) Works only on 1H timeframe Useful for swing traders and intraday traders who rely on hourly trend confirmation. why 1 hr only? the only purpose of this is just to know the bias of the market weather it will reverse or it will continue the trend. As long as the price action did not cross this 3 SMA's the trend will continue. as a trend trader it is very useful this strategy.. make it simple!
Pine Script®指标
由christophermarollano提供
1
💎DrFX Diamond Algo 💎Diamond Algo - Multi-Feature Trading System Advanced trading system combining Supertrend signals with multiple confirmation filters, risk management tools, and a comprehensive market analysis dashboard. ═══ CORE FEATURES ═══ • Smart Buy/Sell signals using modified Supertrend algorithm • Multi-timeframe trend analysis (M1 to D1) • Support & Resistance zone detection • Risk management with automatic TP/SL levels (1:1, 2:1, 3:1) • Real-time market dashboard with key metrics • Multiple trend cloud overlays for visual confirmation ═══ SIGNAL GENERATION ═══ BUY Signal: • Supertrend bullish crossover • Price above SMA filter • Optional smart signals (EMA 200 confirmation) SELL Signal: • Supertrend bearish crossunder • Price below SMA filter • Optional smart signals (EMA 200 confirmation) ═══ DASHBOARD COMPONENTS ═══ • Multi-timeframe trend status (8 timeframes) • Current position indicator • Market state analysis (Trending/Ranging/No trend) • Volatility percentage • Institutional activity monitor • Trading session tracker (NY/London/Tokyo/Sydney) • Trend pressure indicator ═══ VISUAL OVERLAYS ═══ • Trend Cloud: Long-term trend visualization • Trend Follower: Adaptive trend line • Comulus Cloud: Dual ALMA-based trend zones • Cirrus Cloud: Short-term trend bands • Smart Trail: Fibonacci-based trailing stop • Dynamic trend lines with breakout alerts ═══ RISK MANAGEMENT ═══ • Automatic Stop-Loss placement (ATR-based) • Three Take-Profit levels with Risk:Reward ratios • Entry price labeling • Optional distance and decimal customization • Visual lines connecting entry to targets ═══ INPUT PARAMETERS ═══ Sensitivity (1-20): Controls signal frequency Smart Signals Only: Filters for high-probability setups Bar Coloring: Trend-based or gradient coloring Dashboard Location/Size: Customizable placement Multiple overlay toggles for clean charts ═══ BEST PRACTICES ═══ • Lower sensitivity (1-5) for swing trading • Higher sensitivity (10-20) for scalping • Enable Smart Signals for conservative approach • Use dashboard to confirm multi-timeframe alignment • Monitor volatility % before entering trades ═══ ALERT CONDITIONS ═══ • Buy Alert: Triggered on bullish signal • Sell Alert: Triggered on bearish signal • Trend line breakout alerts (automated) ═══ VERSION INFO ═══ Pine Script: v5 Max Labels: 500 Repainting: Minimal (uses confirmed bars for signals) ```
Pine Script®指标
由DrFXGOD提供
RSI: chart overlay This indicator maps RSI thresholds directly onto price. Since the EMA of price aligns with RSI’s 50-line, it draws a volatility-based band around the EMA to reveal levels such as 70 and 30. By converting RSI values into visible price bands, the overlay lets you see exactly where price would have to move to hit traditional RSI boundaries. These bands adapt in real time to both price movement and market volatility, keeping the classic RSI logic intact while presenting it in the context of price action. This approach helps traders interpret RSI signals without leaving the main chart window. The calculation uses the same components as the RSI: alternative derivation script: Wilder’s EMA for smoothing, a volatility-based unit for scaling, and a normalization factor. The result is a dynamic band structure on the chart, representing RSI boundary levels in actual price terms. Key components and calculation breakdown: Wilder’s EMA Used as the anchor point for measuring price position. myEMA = ta.rma(close, Length) Volatility Unit Derived from the EMA of absolute close-to-close price changes. CC_vol = ta.rma(math.abs(close - close ), Length) Normalization Factor Scales the volatility unit to align with the RSI formula’s structure. normalization_factor = 1 / (Length - 1) Upper and Lower Boundaries Defines price bands corresponding to selected RSI threshold values. up_b = myEMA + ((upper - 50) / 50) * (CC_vol / normalization_factor) down_b = myEMA - ((50 - lower) / 50) * (CC_vol / normalization_factor) Inputs RSI length Upper boundary – RSI level above 50 Lower boundary – RSI level below 50 ON/OFF toggle for 50-point line (EMA of close prices) ON/OFF toggle for overbought/oversold coloring (use with line chart) Interpretation: Each band on the chart represents a chosen RSI level. When price touches a band, RSI is at that threshold. The distance between moving average and bands adjusts automatically with volatility and your selected RSI length. All calculations remain fully consistent with standard RSI values. Feedback and code suggestions are welcome, especially regarding implementation efficiency and customization.
Pine Script®指标
由AdaptiveRSI提供
6
Jarass regression linesDouble Linear Regression Ultimate + MA Ribbon (DLRC + MA) The DLRC + MA indicator is an advanced technical analysis tool that combines double linear regression channels with a moving average ribbon (MA Ribbon). Designed for traders who want to simultaneously track trend, volatility, and potential support/resistance levels. Key Features: 1. Double Linear Regression Channels: • Inner Channel – shorter period, more sensitive to recent price movements. • Outer Channel – longer period, reflects the long-term trend. • Both channels display upper and lower boundaries and a midline. • Optional logarithmic scale for price adjustment. • Real-time R² values to assess regression accuracy. 2. MA Ribbon: • Up to 4 different moving averages simultaneously. • Supports SMA, EMA, SMMA (RMA), WMA, VWMA. • Each MA can be individually enabled/disabled, with customizable period, source, and color. • Helps identify trend direction and dynamic support/resistance levels. 3. Visualization: • Channels are filled with semi-transparent colors for clarity. • Midline for quick trend direction assessment. • Label displays R² values of the channels in real time. 4. Suitable For: • Short-term and long-term traders seeking a combination of linear regression analysis and classic trend-following tools. • Useful for identifying overbought/oversold zones and potential trend reversal points. Summary: DLRC + MA combines statistical precision of linear regression with intuitive trend visualization via a MA ribbon. It provides quick insight into market direction, volatility, and potential turning points, all in one chart overlay.
Pine Script®指标
由Jarass1080提供
Aggregated Scores Oscillator [Alpha Extract]A sophisticated risk-adjusted performance measurement system that combines Omega Ratio and Sortino Ratio methodologies to create a comprehensive market assessment oscillator. Utilizing advanced statistical band calculations with expanding and rolling window analysis, this indicator delivers institutional-grade overbought/oversold detection based on risk-adjusted returns rather than traditional price movements. The system's dual-ratio aggregation approach provides superior signal accuracy by incorporating both upside potential and downside risk metrics with dynamic threshold adaptation for varying market conditions. 🔶 Advanced Statistical Framework Implements dual statistical methodologies using expanding and rolling window calculations to create adaptive threshold bands that evolve with market conditions. The system calculates cumulative statistics alongside rolling averages to provide both historical context and current market regime sensitivity with configurable window parameters for optimal performance across timeframes. 🔶 Dual Ratio Integration System Combines Omega Ratio analysis measuring excess returns versus deficit returns with Sortino Ratio calculations focusing on downside deviation for comprehensive risk-adjusted performance assessment. The system applies configurable smoothing to both ratios before aggregation, ensuring stable signal generation while maintaining sensitivity to regime changes. // Omega Ratio Calculation Excess_Return = sum((Daily_Return > Target_Return ? Daily_Return - Target_Return : 0), Period) Deficit_Return = sum((Daily_Return < Target_Return ? Target_Return - Daily_Return : 0), Period) Omega_Ratio = Deficit_Return ≠ 0 ? (Excess_Return / Deficit_Return) : na // Sortino Ratio Framework Downside_Deviation = sqrt(sum((Daily_Return < Target_Return ? (Daily_Return - Target_Return)² : 0), Period) / Period) Sortino_Ratio = (Mean_Return / Downside_Deviation) * sqrt(Annualization_Factor) // Aggregated Score Aggregated_Score = SMA(Omega_Ratio, Omega_SMA) + SMA(Sortino_Ratio, Sortino_SMA) 🔶 Dynamic Band Calculation Engine Features sophisticated threshold determination using both expanding historical statistics and rolling window analysis to create adaptive overbought/oversold levels. The system incorporates configurable multipliers and sensitivity adjustments to optimize signal timing across varying market volatility conditions with automatic band convergence logic. 🔶 Signal Generation Framework Generates overbought conditions when aggregated score exceeds adjusted upper threshold and oversold conditions below lower threshold, with neutral zone identification for range-bound markets. The system provides clear binary signal states with background zone highlighting and dynamic oscillator coloring for intuitive market condition assessment. 🔶 Enhanced Visual Architecture Provides modern dark theme visualization with neon color scheme, dynamic oscillator line coloring based on signal states, and gradient band fills for comprehensive market condition visualization. The system includes zero-line reference, statistical band plots, and background zone highlighting with configurable transparency levels. snapshot 🔶 Risk-Adjusted Performance Analysis Utilizes target return parameters for customizable risk assessment baselines, enabling traders to evaluate performance relative to specific return objectives. The system's focus on downside deviation through Sortino analysis provides superior risk-adjusted signals compared to traditional volatility-based oscillators that treat upside and downside movements equally. 🔶 Multi-Timeframe Adaptability Features configurable calculation periods and rolling windows to optimize performance across various timeframes from intraday to long-term analysis. The system's statistical foundation ensures consistent signal quality regardless of timeframe selection while maintaining sensitivity to market regime changes through adaptive band calculations. 🔶 Performance Optimization Framework Implements efficient statistical calculations with optimized variable management and configurable smoothing parameters to balance responsiveness with signal stability. The system includes automatic band adjustment mechanisms and rolling window management for consistent performance across extended analysis periods. This indicator delivers sophisticated risk-adjusted market analysis by combining proven statistical ratios in a unified oscillator framework. Unlike traditional overbought/oversold indicators that rely solely on price movements, the ASO incorporates risk-adjusted performance metrics to identify genuine market extremes based on return quality rather than price volatility alone. The system's adaptive statistical bands and dual-ratio methodology provide institutional-grade signal accuracy suitable for systematic trading approaches across cryptocurrency, forex, and equity markets with comprehensive visual feedback and configurable risk parameters for optimal strategy integration.
Pine Script®指标
由AlphaExtract提供
2
Volume Based Sampling [BackQuant]Volume Based Sampling What this does This indicator converts the usual time-based stream of candles into an event-based stream of “synthetic” bars that are created only when enough trading activity has occurred . You choose the activity definition: Volume bars : create a new synthetic bar whenever the cumulative number of shares/contracts traded reaches a threshold. Dollar bars : create a new synthetic bar whenever the cumulative traded dollar value (price × volume) reaches a threshold. The script then keeps an internal ledger of these synthetic opens, highs, lows, closes, and volumes, and can display them as candles, plot a moving average calculated over the synthetic closes, mark each time a new sample is formed, and optionally overlay the native time-bars for comparison. Why event-based sampling matters Markets do not release information on a clock: activity clusters during news, opens/closes, and liquidity shocks. Event-based bars normalize for that heteroskedastic arrival of information: during active periods you get more bars (finer resolution); during quiet periods you get fewer bars (coarser resolution). Research shows this can reduce microstructure pathologies and produce series that are closer to i.i.d. and more suitable for statistical modeling and ML. In particular: Volume and dollar bars are a common event-time alternative to time bars in quantitative research and are discussed extensively in Advances in Financial Machine Learning (AFML). These bars aim to homogenize information flow by sampling on traded size or value rather than elapsed seconds. The Volume Clock perspective models market activity in “volume time,” showing that many intraday phenomena (volatility, liquidity shocks) are better explained when time is measured by traded volume instead of seconds. Related market microstructure work on flow toxicity and liquidity highlights that the risk dealers face is tied to information intensity of order flow, again arguing for activity-based clocks. How the indicator works (plain English) Choose your bucket type Volume : accumulate volume until it meets a threshold. Dollar Bars : accumulate close × volume until it meets a dollar threshold. Pick the threshold rule Dynamic threshold : by default, the script computes a rolling statistic (mean or median) of recent activity to set the next bucket size. This adapts bar size to changing conditions (e.g., busier sessions produce more frequent synthetic bars). Fixed threshold : optionally override with a constant target (e.g., exactly 100,000 contracts per synthetic bar, or $5,000,000 per dollar bar). Build the synthetic bar While a bucket fills, the script tracks: o_s: first price of the bucket (synthetic open) h_s: running maximum price (synthetic high) l_s: running minimum price (synthetic low) c_s: last price seen (synthetic close) v_s: cumulative native volume inside the bucket d_samples: number of native bars consumed to complete the bucket (a proxy for “how fast” the threshold filled) Emit a new sample Once the bucket meets/exceeds the threshold, a new synthetic bar is finalized and stored. If overflow occurs (e.g., a single native bar pushes you past the threshold by a lot), the code will emit multiple synthetic samples to account for the extra activity. Maintain a rolling history efficiently A ring buffer can overwrite the oldest samples when you hit your Max Stored Samples cap, keeping memory usage stable. Compute synthetic-space statistics The script computes an SMA over the last N synthetic closes and basic descriptors like average bars per synthetic sample, mean and standard deviation of synthetic returns, and more. These are all in event time , not clock time. Inputs and options you will actually use Data Settings Sampling Method : Volume or Dollar Bars. Rolling Lookback : window used to estimate the dynamic threshold from recent activity. Filter : Mean or Median for the dynamic threshold. Median is more robust to spikes. Use Fixed? / Fixed Threshold : override dynamic sizing with a constant target. Max Stored Samples : cap on synthetic history to keep performance snappy. Use Ring Buffer : turn on to recycle storage when at capacity. Indicator Settings SMA over last N samples : moving average in synthetic space . Because its index is sample count, not minutes, it adapts naturally: more updates in busy regimes, fewer in quiet regimes. Visuals Show Synthetic Bars : plot the synthetic OHLC candles. Candle Color Mode : Green/Red: directional close vs open Volume Intensity: opacity scales with synthetic size Neutral: single color Adaptive: graded by how large the bucket was relative to threshold Mark new samples : drop a small marker whenever a new synthetic bar prints. Comparison & Research Show Time Bars : overlay the native time-based candles to visually compare how the two sampling schemes differ. How to read it, step by step Turn on “Synthetic Bars” and optionally overlay “Time Bars.” You will see that during high-activity bursts, synthetic bars print much faster than time bars. Watch the synthetic SMA . Crosses in synthetic space can be more meaningful because each update represents a roughly comparable amount of traded information. Use the “Avg Bars per Sample” in the info table as a regime signal. Falling average bars per sample means activity is clustering, often coincident with higher realized volatility. Try Dollar Bars when price varies a lot but share count does not; they normalize by dollar risk taken in each sample. Volume Bars are ideal when share count is a better proxy for information flow in your instrument. Quant finance background and citations Event time vs. clock time : Easley, López de Prado, and O’Hara advocate measuring intraday phenomena on a volume clock to better align sampling with information arrival. This framing helps explain volatility bursts and liquidity droughts and motivates volume-based bars. Flow toxicity and dealer risk : The same authors show how adverse selection risk changes with the intensity and informativeness of order flow, further supporting activity-based clocks for modeling and risk management. AFML framework : In Advances in Financial Machine Learning , event-driven bars such as volume, dollar, and imbalance bars are presented as superior sampling units for many ML tasks, yielding more stationary features and fewer microstructure distortions than fixed time bars. ( Alpaca ) Practical use cases 1) Regime-aware moving averages The synthetic SMA in event time is not fooled by quiet periods: if nothing of consequence trades, it barely updates. This can make trend filters less sensitive to calendar drift and more sensitive to true participation. 2) Breakout logic on “equal-information” samples The script exposes simple alerts such as breakout above/below the synthetic SMA . Because each bar approximates a constant amount of activity, breakouts are conditioned on comparable informational mass, not arbitrary time buckets. 3) Volatility-adaptive backtests If you use synthetic bars as your base data stream, most signal rules become self-paced : entry and exit opportunities accelerate in fast markets and slow down in quiet regimes, which often improves the realism of slippage and fill modeling in research pipelines (pair this indicator with strategy code downstream). 4) Regime diagnostics Avg Bars per Sample trending down: activity is dense; expect larger realized ranges. Return StdDev (synthetic) rising: noise or trend acceleration in event time; re-tune risk. Interpreting the info panel Method : your sampling choice and current threshold. Total Samples : how many synthetic bars have been formed. Current Vol/Dollar : how much of the next bucket is already filled. Bars in Bucket : native bars consumed so far in the current bucket. Avg Bars/Sample : lower means higher trading intensity. Avg Return / Return StdDev : return stats computed over synthetic closes . Research directions you can build from here Imbalance and run bars Extend beyond pure volume or dollar thresholds to imbalance bars that trigger on directional order flow imbalance (e.g., buy volume minus sell volume), as discussed in the AFML ecosystem. These often further homogenize distributional properties used in ML. alpaca.markets Volume-time indicators Re-compute classical indicators (RSI, MACD, Bollinger) on the synthetic stream. The premise is that signals are updated by traded information , not seconds, which may stabilize indicator behavior in heteroskedastic regimes. Liquidity and toxicity overlays Combine synthetic bars with proxies of flow toxicity to anticipate spread widening or volatility clustering. For instance, tag synthetic bars that surpass multiples of the threshold and test whether subsequent realized volatility is elevated. Dollar-risk parity sampling for portfolios Use dollar bars to align samples across assets by notional risk, enabling cleaner cross-asset features and comparability in multi-asset models (e.g., correlation studies, regime clustering). AFML discusses the benefits of event-driven sampling for cross-sectional ML feature engineering. Microstructure feature set Compute duration in native bars per synthetic sample , range per sample , and volume multiple of threshold as inputs to state classifiers or regime HMMs . These features are inherently activity-aware and often predictive of short-horizon volatility and trend persistence per the event-time literature. ( Alpaca ) Tips for clean usage Start with dynamic thresholds using Median over a sensible lookback to avoid outlier distortion, then move to Fixed thresholds when you know your instrument’s typical activity scale. Compare time bars vs synthetic bars side by side to develop intuition for how your market “breathes” in activity time. Keep Max Stored Samples reasonable for performance; the ring buffer avoids memory creep while preserving a rolling window of research-grade data.
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