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Updated Volume SuperTrend AI (Expo)// This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) creativecommons.org // © Zeiierman //@version=5 indicator("Volume SuperTrend AI (Expo)", overlay=true) // ~~ ToolTips { t1="Number of nearest neighbors in KNN algorithm (k): Increase to consider more neighbors, providing a more balanced view but possibly smoothing out local patterns. Decrease for fewer neighbors to make the algorithm more responsive to recent changes. Number of data points to consider (n): Increase for more historical data, providing a broader context but possibly diluting recent trends. Decrease for less historical data to focus more on recent behavior." t2="Length of weighted moving average for price (KNN_PriceLen): Higher values create a smoother price line, influencing the KNN algorithm to be more stable but less sensitive to short-term price movements. Lower values enhance responsiveness in KNN predictions to recent price changes but may lead to more noise. Length of weighted moving average for SuperTrend (KNN_STLen): Higher values lead to a smoother SuperTrend line, affecting the KNN algorithm to emphasize long-term trends. Lower values make KNN predictions more sensitive to recent SuperTrend changes but may result in more volatility." t3="Length of the SuperTrend (len): Increase for a smoother trend line, ideal for identifying long-term trends but possibly ignoring short-term fluctuations. Decrease for more responsiveness to recent changes but risk of more false signals. Multiplier for ATR in SuperTrend calculation (factor): Increase for wider bands, capturing larger price movements but possibly missing subtle changes. Decrease for narrower bands, more sensitive to small shifts but risk of more noise." t4="Type of moving average for SuperTrend calculation (maSrc): Choose based on desired characteristics. SMA is simple and clear, EMA emphasizes recent prices, WMA gives more weight to recent data, RMA is less sensitive to recent changes, and VWMA considers volume." t5="Color for bullish trend (upCol): Select to visually identify upward trends. Color for bearish trend (dnCol): Select to visually identify downward trends. Color for neutral trend (neCol): Select to visually identify neutral trends." //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} // ~~ Input settings for K and N values k = input.int(3, title = "Neighbors", minval=1, maxval=100,inline="AI", group="AI Settings") n_ = input.int(10, title ="Data", minval=1, maxval=100,inline="AI", group="AI Settings", tooltip=t1) n = math.max(k,n_) //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} // ~~ Input settings for prediction values KNN_PriceLen = input.int(20, title="Price Trend", minval=2, maxval=500, step=10,inline="AITrend", group="AI Trend") KNN_STLen = input.int(100, title="Prediction Trend", minval=2, maxval=500, step=10, inline="AITrend", group="AI Trend", tooltip=t2) aisignals = input.bool(true,title="AI Trend Signals",inline="signal", group="AI Trend") Bullish_col = input.color(color.lime,"",inline="signal", group="AI Trend") Bearish_col = input.color(color.red,"",inline="signal", group="AI Trend") //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} // ~~ Define SuperTrend parameters len = input.int(10, "Length", minval=1,inline="SuperTrend", group="Super Trend Settings") factor = input.float(3.0,step=.1,inline="SuperTrend", group="Super Trend Settings", tooltip=t3) maSrc = input.string("WMA","Moving Average Source", ,inline="", group="Super Trend Settings", tooltip=t4) upCol = input.color(color.lime,"Bullish Color",inline="col", group="Super Trend Coloring") dnCol = input.color(color.red,"Bearish Color",inline="col", group="Super Trend Coloring") neCol = input.color(color.blue,"Neutral Color",inline="col", group="Super Trend Coloring", tooltip=t5) //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} // ~~ Calculate the SuperTrend based on the user's choice vwma = switch maSrc "SMA" => ta.sma(close*volume, len) / ta.sma(volume, len) "EMA" => ta.ema(close*volume, len) / ta.ema(volume, len) "WMA" => ta.wma(close*volume, len) / ta.wma(volume, len) "RMA" => ta.rma(close*volume, len) / ta.rma(volume, len) "VWMA" => ta.vwma(close*volume, len) / ta.vwma(volume, len) atr = ta.atr(len) upperBand = vwma + factor * atr lowerBand = vwma - factor * atr prevLowerBand = nz(lowerBand ) prevUpperBand = nz(upperBand ) lowerBand := lowerBand > prevLowerBand or close < prevLowerBand ? lowerBand : prevLowerBand upperBand := upperBand < prevUpperBand or close > prevUpperBand ? upperBand : prevUpperBand int direction = na float superTrend = na prevSuperTrend = superTrend if na(atr ) direction := 1 else if prevSuperTrend == prevUpperBand direction := close > upperBand ? -1 : 1 else direction := close < lowerBand ? 1 : -1 superTrend := direction == -1 ? lowerBand : upperBand //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} // ~~ Collect data points and their corresponding labels price = ta.wma(close,KNN_PriceLen) sT = ta.wma(superTrend,KNN_STLen) data = array.new_float(n) labels = array.new_int(n) for i = 0 to n - 1 data.set(i, superTrend ) label_i = price > sT ? 1 : 0 labels.set(i, label_i) //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} // ~~ Define a function to compute distance between two data points distance(x1, x2) => math.abs(x1 - x2) //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} // ~~ Define the weighted k-nearest neighbors (KNN) function knn_weighted(data, labels, k, x) => n1 = data.size() distances = array.new_float(n1) indices = array.new_int(n1) // Compute distances from the current point to all other points for i = 0 to n1 - 1 x_i = data.get(i) dist = distance(x, x_i) distances.set(i, dist) indices.set(i, i) // Sort distances and corresponding indices in ascending order // Bubble sort method for i = 0 to n1 - 2 for j = 0 to n1 - i - 2 if distances.get(j) > distances.get(j + 1) tempDist = distances.get(j) distances.set(j, distances.get(j + 1)) distances.set(j + 1, tempDist) tempIndex = indices.get(j) indices.set(j, indices.get(j + 1)) indices.set(j + 1, tempIndex) // Compute weighted sum of labels of the k nearest neighbors weighted_sum = 0. total_weight = 0. for i = 0 to k - 1 index = indices.get(i) label_i = labels.get(index) weight_i = 1 / (distances.get(i) + 1e-6) weighted_sum += weight_i * label_i total_weight += weight_i weighted_sum / total_weight //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} // ~~ Classify the current data point current_superTrend = superTrend label_ = knn_weighted(data, labels, k, current_superTrend) //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} // ~~ Plot col = label_ == 1?upCol:label_ == 0?dnCol:neCol plot(current_superTrend, color=col, title="Volume Super Trend AI") upTrend = plot(superTrend==lowerBand?current_superTrend:na, title="Up Volume Super Trend AI", color=col, style=plot.style_linebr) Middle = plot((open + close) / 2, display=display.none, editable=false) downTrend = plot(superTrend==upperBand?current_superTrend:na, title="Down Volume Super Trend AI", color=col, style=plot.style_linebr) fill_col = color.new(col,90) fill(Middle, upTrend, fill_col, fillgaps=false,title="Up Volume Super Trend AI") fill(Middle, downTrend, fill_col, fillgaps=false, title="Down Volume Super Trend AI") //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~} // ~~ Ai Super Trend Signals Start_TrendUp = col==upCol and (col !=upCol or col ==neCol) and aisignals Start_TrendDn = col==dnCol and (col !=dnCol or col ==neCol) and aisignals // ~~ Add buy and sell signals plotshape(Start_TrendUp, title="Buy Signal", location=location.belowbar, color=Bullish_col, style=shape.labelup, text="BUY") plotshape(Start_TrendDn, title="Sell Signal", location=location.abovebar, color=Bearish_col, style=shape.labeldown, text="SELL")
Chỉ báo Pine Script™
bởi jfmarier
AstroTrading_OrderBlocksThe AstroTrading Order Blocks indicator is a tool that helps identify potential support and resistance levels by establishing relationships between price action and candle data. This indicator uses the open, close, high and low values of past candles to analyze their interaction with current candles. Users can add this indicator to their charts to better understand market behavior. Key Features: Candle Information Analysis: The indicator detects whether the previous candle was green or red. The open, close, high and low levels of past candles are analyzed and compared to the current candle. Conditions: Red Line Condition: If the previous candle is green, the high of the current candle is between the open and close of the previous candle and the current candle is red, a red line is formed. Green Line Condition: If the previous candle was red, the low of the current candle is between the open and close of the previous candle, and the current candle is green, a green line is formed. Visual Expressions on the Chart: When the red line condition is triggered, red lines and the “🐻Bear OB🐻” sign are displayed on the chart. When the green line condition is triggered, green lines and the “🐂Bull OB🐂” sign are displayed on the chart. Usage: This indicator helps to identify support and resistance levels in technical analysis. Traders can evaluate potential buying or selling opportunities by analyzing past price movements. Warnings: Users are advised to use the indicator with caution and conduct their own research. The indicator should only be used to identify support and resistance levels and should not be used in conjunction with other technical analysis tools. Summary of the Code: This indicator is designed to work on the TradingView platform and performs the following functions: Analyzes previous candle data and compares it with the current candle data. Plots support and resistance levels on the chart according to the conditions. It displays the relevant symbols with red and green lines.
Chỉ báo Pine Script™
bởi bugracomez
VWAP on Straddle & Strangle From Sandeep// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org // © AlgoTest //@version=5 indicator("VWAP on Straddle & Strangle", overlay = false) var bool first = true var strike_gap = map.new() if first first := false strike_gap.put("NIFTY", 50) strike_gap.put("BANKNIFTY", 100) strike_gap.put("FINNIFTY", 50) strike_gap.put("MIDCPNIFTY", 25) strike_gap.put("SENSEX", 100) strike_gap.put("CRUDEOIL",50) spot = input.string( "NIFTY" , title = "Spot Symbol", options = , group = "Index") tooltip_day = "Enter the day of the expiry. Add 0 infront, if day is in single digit. For eg : 05 instead of 5" tooltip_month = "Enter the month of the expiry. Add 0 infront, if month is in single digit. For eg : 06 instead of 6" tooltip_year = "Enter the year of the expiry. Use last digit of the year. For eg : 24 instead of 2024" _day = input.string( "11" , title = "Expiry Day", tooltip = tooltip_day, group="Expiry Date") _month = input.string( "07" , title = "Expiry Month", tooltip = tooltip_month, group="Expiry Date") _year = input.string( "24" , title = "Expiry Year", tooltip = tooltip_year, group="Expiry Date") tooltip_ = "You can select any Strike, you can also have VWAP on Straddle, Strangle" strike_ce = input.int(24300, "Call Strike", tooltip = tooltip_, group = "Select Strike") strike_pe = input.int(24300, "Put Strike", tooltip = tooltip_, group = "Select Strike") var string symbol_CE = "" var string symbol_PE = "" if(spot == "SENSEX") symbol_CE := spot+"_"+_year+_month+_day+"_C_"+str.tostring(strike_ce) symbol_PE := spot+"_"+_year+_month+_day+"_P_"+str.tostring(strike_pe) if(spot != "SENSEX") symbol_CE := spot+_year+_month+_day+"C"+str.tostring(strike_ce) symbol_PE := spot+_year+_month+_day+"P"+str.tostring(strike_pe) = request.security( symbol_CE, timeframe.period , ) = request.security( symbol_PE, timeframe.period , ) call_volume = request.security( symbol_CE, timeframe.period , volume ) put_volume = request.security( symbol_PE, timeframe.period , volume ) straddle_open = call_open + put_open straddle_close = call_close + put_close straddle_high = math.max(straddle_open, straddle_close) straddle_low = math.min(straddle_open, straddle_close) straddle_volume = call_volume + put_volume var float sumPriceVolume = 0.0 var float sumVolume = 0.0 var float vwap = 0.0 if (dayofweek != dayofweek ) sumPriceVolume := 0.0 sumVolume := 0.0 vwap := 0.0 sumPriceVolume += straddle_close * straddle_volume sumVolume += straddle_volume vwap := sumPriceVolume / sumVolume plotcandle ( straddle_open , straddle_high , straddle_low , straddle_close , title = "Straddle" , color = straddle_close > straddle_open ? color.green : color.red ) // vwap = ta.vwap(straddle_close) plot ( vwap , title = "VWAP on Straddle" , color = color.blue , linewidth = 2 ) entry = straddle_close < vwap and straddle_close >= vwap exit = straddle_close >= vwap and straddle_close < vwap plotshape(exit, title = "Exit", text = 'Exit', style = shape.labeldown, location = location.top, color= color.red, textcolor = color.white, size = size.tiny) plotshape(entry, title = "Entry", text = 'Entry', style = shape.labelup, location = location.bottom, color= color.green, textcolor = color.white, size = size.tiny) alertcondition(exit, "Exit", "Exit") alertcondition(entry, "Entry", "Entry")
Chỉ báo Pine Script™
bởi sandeepmittal88
Adaptive ema Cloud v1 Trend & Trade Signals"adaptive ema cloud v1 trend & trade signals" is a comprehensive technical indicator aimed at assisting traders in identifying market trends, trade entry points, and potential take profit (tp) and stop-loss (sl) levels. this indicator combines adaptive exponential moving average (ema) clouds with standard deviation bands to create a visual trend and signal system, enabling users to better analyze price action. key features: adaptive ema cloud: calculates a dynamic ema-based cloud using a simple moving average (sma) line, with upper and lower deviation bands based on standard deviations. users can adjust the standard deviation multiplier to modify the cloud's width. trend direction detection: the indicator determines trend direction by comparing the close price to the ema cloud and signals bullish or bearish trends when the price crosses key levels. take profit (tp) and stop-loss (sl) points: adaptive tp and sl levels are calculated based on the deviation bands, providing users with suggested exit points when a trade is triggered. peak and valley detection: detects peaks and valleys in price, aiding traders in spotting potential support and resistance areas. gradient-based cloud fill: dynamically fills the cloud with a gradient color based on trend strength, helping users visually gauge trend intensity. trade tracking: tracks recent trades and records them in an internal memory, allowing users to view the last 20 trade outcomes, including whether tp or sl was hit. how to use: trend signals: look for green arrows (bullish trend) or red arrows (bearish trend) to identify potential entries based on trend crossovers. tp/sl management: tp and sl levels are automatically calculated and displayed, with alerts available to notify users when these levels are reached. adjustable settings: customize period length, standard deviation multiplier, and color preferences to match trading preferences and chart style. inputs- period: defines the look-back period for ema calculations. standard deviation multiplier: adjusts cloud thickness by setting the multiplier for tp and sl bands. gauge size: scales the gradient intensity for trend cloud visualization. up/down colors: allows users to set custom colors for bullish and bearish bars. alert conditions: this script has built-in alerts for trend changes, tp, and sl levels, providing users with automated notifications of important trading signals.
Chỉ báo Pine Script™
bởi hormuzdengineer
SK_Pivot_StrategyKey Changes: Market Hours Checkbox: Added useMarketHours input to enable or disable the market hours filter. isMarketHour() Function: Added to determine if the current time is within market hours. Condition Modification: Included isMarketHour() in the conditions for longConditionMet and shortConditionMet to ensure signals are generated only during market hours if the filter is enabled. These modifications ensure that your strategy only triggers signals during market hours when the filter is enabled.
Chiến lược Pine Script™
bởi millerno2
Universal Trend and Valuation System [QuantAlgo]Universal Trend and Valuation System 📊🧬 The Universal Trend and Valuation System by QuantAlgo is an advanced indicator designed to assess asset valuation and trends across various timeframes and asset classes. This system integrates multiple advanced statistical indicators and techniques with Z-score calculations to help traders and investors identify overbought/sell and oversold/buy signals. By evaluating valuation and trend strength together, this tool empowers users to make data-driven decisions, whether they aim to follow trends, accumulate long-term positions, or identify turning points in mean-reverting markets. 💫 Conceptual Foundation and Innovation The Universal Trend and Valuation System by QuantAlgo provides a unique framework for assessing market valuation and trend dynamics through a blend of Z-score analysis and trend-following algorithm. Unlike traditional indicators that only reflect price direction, this system incorporates multi-layered data to reveal the relative value of an asset, helping users determine whether it’s overvalued, undervalued, or approaching a trend reversal. By combining high quality trend-following tools, such as Dynamic Score Supertrend, DEMA RSI, and EWMA, it evaluates trend stability and momentum quality, while Z-scores of performance ratios like Sharpe, Sortino, and Omega standardize deviations from historical trends, enabling traders and investors to spot extreme conditions. This dual approach allows users to better identify accumulation (undervaluation) and distribution (overvaluation) phases, enhancing strategies like Dollar Cost Averaging (DCA) and overall timing for entries and exits. 📊 Technical Composition and Calculation The Universal Trend-Following Valuation System is composed of several trend-following and valuation indicators that create a dynamic dual scoring model: Risk-Adjusted Ratios (Sharpe, Sortino, Omega): These ratios assess trend quality by analyzing an asset’s risk-adjusted performance. Sharpe and Sortino provide insight into trend consistency and risk/reward, while Omega evaluates profitability potential, helping traders and investors assess how favorable a trend or an asset is relative to its associated risk. Dynamic Z-Scores: Z-scores are applied to various metrics like Price, RSI, and RoC, helping to identify statistical deviations from the mean, which indicate potential extremes in valuation. By combining these Z-scores, the system produces a cumulative score that highlights when an asset may be overbought or oversold. Aggregated Trend-Following Indicators: The model consolidates multiple high quality indicators to highlight probable trend shifts. This helps confirm the direction and strength of market moves, allowing users to spot reversals or entry points with greater clarity. 📈 Key Indicators and Features The Universal Trend and Valuation System combines various technical and statistical tools to deliver a well-rounded analysis of market trends and valuation: The indicator utilizes trend-following indicators like RSI with DEMA smoothing and Dynamic Score Supertrend to minimize market noise, providing clearer and more stable trend signals. Sharpe, Sortino, and Omega ratios are calculated to assess risk-adjusted performance and volatility, adding a layer of analysis for evaluating trend quality. Z-scores are applied to these ratios, as well as Price and Rate of Change (RoC), to detect deviations from historical trends, highlighting extreme valuation levels. The system also incorporates multi-layered visualization with gradient color coding to signal valuation states across different market conditions. These adaptive visual cues, combined with threshold-based alerts for overbought and oversold zones, help traders and investors track probable trend reversals or continuations and identify accumulation or distribution zones, adding reliability to both trend-following and mean-reversion strategies. ⚡️ Practical Applications and Examples ✅ Add the Indicator: Add the Universal Trend-Following Valuation System to your favourites and to your chart. 👀 Monitor Trend Shifts and Valuation Levels: Watch the average Z score, trend probability state and gradient colors to identify overbought and oversold conditions. During undervaluation, consider using a DCA strategy to gradually accumulate positions (buy), while overvaluation may signal distribution or profit-taking phases (sell). 🔔 Set Alerts: Configure alerts for significant trend or valuation changes, ensuring you can act on market movements promptly, even when you’re not actively monitoring the charts. 🌟 Summary and Usage Tips The Universal Trend and Valuation System by QuantAlgo is a highly adaptable tool, designed to support both trend-following and valuation analysis across different market environments. By combining valuation metrics with high quality trend-following indicators, it helps traders and investors identify the relative value of an asset based on historical norms, providing more reliable overbought/sell and oversold/buy signals. The tool’s flexibility across asset types and timeframes makes it ideal for both short-term trading and long-term investment strategies like DCA, allowing users to capture meaningful trends while minimizing noise.
Chỉ báo Pine Script™
bởi QuantAlgo
ES Trend-following Strategy with ExitsES Trend-following Strategy with Exits This Pine Script strategy is designed for trend-following in the ES futures market using the 9 EMA and 50 EMA crossovers, along with RSI and VWAP filters. Features Entry Signals: Long: When the 9 EMA crosses above the 50 EMA, price is above VWAP, and RSI is within the oversold/overbought range. Short: When the 9 EMA crosses below the 50 EMA, price is below VWAP, and RSI is within the oversold/overbought range. Exit Signals: The exit signals provided in this strategy are based on the 9 EMA crossing back below the 50 EMA for long positions, or above the 50 EMA for short positions. However, these exit signals may not be 100% accurate every time. To ensure you are exiting at the right time, always confirm that the 9 EMA has crossed the 50 EMA before closing your position. If the crossover hasn't occurred, it’s better to wait for it to happen, as exiting too early might result in missed profits. Note: The exit logic will be refined and updated in future versions to provide more effective and reliable trend reversal signals. Alerts: Alerts are triggered for entry and exit signals for both long and short positions. This strategy helps capture strong trends, and the exit logic will be improved for better trend reversal confirmation in future updates. Note: This script is for educational purposes and should be used at your own discretion. Always test strategies thoroughly before live trading.
Chỉ báo Pine Script™
bởi rajatdutt5
IG10x TrendLineBlocksAa cutting-edge tool designed to help traders identify and capitalize on key market movements with accuracy and confidence. By combining advanced trend detection with automated alerts, this script ensures you stay ahead of the market and make well-timed, profitable decisions. With real-time notifications of trend changes, you can catch both bullish and bearish shifts as they happen. The built-in visual tools make it easy to assess market conditions at a glance, letting you know when the market is primed for entry or exit. Say goodbye to guesswork and hello to clarity, precision, and increased profitability.
Chỉ báo Pine Script™
bởi TraderShrewd08
Madhan_HMT_Ultimate_StrategyThis indicator is a trend-following strategy designed to identify buy and sell signals based on price action relative to dynamic channels and smoothing mechanisms. It uses two separate sets of parameters that adjust to market conditions, with each set of parameters acting as an independent trend filter. The indicator creates arrows on the chart to signal potential trade entries, with these arrows appearing when the price crosses certain thresholds established by the indicator's internal calculation. The strategy can be customized with various parameters, including: Stop loss and take profit levels based on multiple options: ATR (Average True Range), fixed points, or percentage-based values. Trading mode options that allow the user to choose whether the strategy trades both long and short positions, or restricts trades to only one direction (long or short). The indicator visually represents the entry levels, stop loss, and take profit levels, with backgrounds filling to highlight potential risk and reward areas. By adjusting the parameters, traders can tailor the indicator to suit different market conditions and their risk tolerance.
Chiến lược Pine Script™
bởi MadhanaGopal_P