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UCS_Squeeze_Timing-V1There is an important information the Squeeze indicator is missing, which is the Pre Squeeze entry. While the Bollinger band begins to curves out of the KC, The breakout usually happens. There are many instances that the Squeeze indicator will fire, after the Major move, I cant blame the indicator, thats the nature (lagging) of all indicators, and we have to live with it. Therefore pre-squeeze-fire Entry can be critical in timing your entry. Timing it too early could result in stoploss if it turns against you, ( or serious burn on options premium), because we never know when the squeeze will fire with the TTM squeeze, But now We know. Its a little timing tool. Managing position is critical when playing options. I will code the timing signal when I get some time. Updated Versions -
Pine Script® göstergesi
UDAY_C_Santhakumar tarafından
8
RSI ADX Bollinger Analysis High-level purpose and design philosophy This indicator — RSI-ADX-Bollinger Analysis — is a compact, educational market-analysis toolkit that blends momentum (RSI), trend strength (ADX), volatility structure (Bollinger Bands) and simple volumetrics to provide traders a snapshot of market condition and trade idea quality. The design philosophy is explicit and layered: use each component to answer a different question about price action (momentum, conviction, volatility, participation), then combine answers to form a more robust, explainable signal. The mashup is intended for analysis and learning, not automatic execution: it surfaces the why behind signals so traders can test, learn and apply rules with risk management. ________________________________________ What each indicator contributes (component-by-component) RSI (Relative Strength Index) — role and behavior: RSI measures short-term momentum by comparing recent gains to recent losses. A high RSI (near or above the overbought threshold) indicates strong recent buying pressure and potential exhaustion if price is extended. A low RSI (near or below the oversold threshold) indicates strong recent selling pressure and potential exhaustion or a value area for mean-reversion. In this dashboard RSI is used as the primary momentum trigger: it helps identify whether price is locally over-extended on the buy or sell side. ADX (Average Directional Index) — role and behavior: ADX measures trend strength independently of direction. When ADX rises above a chosen threshold (e.g., 25), it signals that the market is trending with conviction; ADX below the threshold suggests range or weak trend. Because patterns and momentum signals perform differently in trending vs. ranging markets, ADX is used here as a filter: only when ADX indicates sufficient directional strength does the system treat RSI+BB breakouts as meaningful trade candidates. Bollinger Bands — role and behavior: Bollinger Bands (20-period basis ± N standard deviations) show volatility envelope and relative price position vs. a volatility-adjusted mean. Price outside the upper band suggests pronounced extension relative to recent volatility; price outside the lower band suggests extended weakness. A band expansion (increasing width) signals volatility breakout potential; contraction signals range-bound conditions and potential squeeze. In this dashboard, Bollinger Bands provide the volatility/structural context: RSI extremes plus price beyond the band imply a stronger, volatility-backed move. Volume split & basic MA trend — role and behavior: Buy-like and sell-like volume (simple heuristic using close>open or closeopen) or sell-like (close1.2 for validation and compare win rate and expectancy. 4. TF alignment: Accept signals only when higher timeframe (e.g., 4h) trend agrees — compare results. 5. Parameter sensitivity: Vary RSI threshold (70/30 vs 80/20), Bollinger stddev (2 vs 2.5), and ADX threshold (25 vs 30) and measure stability of results. These exercises teach both statistical thinking and the specific failure modes of the mashup. ________________________________________ Limitations, failure modes and caveats (explicit & teachable) • ADX and Bollinger measures lag during fast-moving news events — signals can be late or wrong during earnings, macro shocks, or illiquid sessions. • Volume classification by open/close is a heuristic; it does not equal TAPEDATA, footprint or signed volume. Use it as supportive evidence, not definitive proof. • RSI can remain overbought or oversold for extended stretches in persistent trends — relying solely on RSI extremes without ADX or BB context invites large drawdowns. • Small-cap or low-liquidity instruments yield noisy band behavior and unreliable volume ratios. Being explicit about these limitations is a strong point in a TradingView description — it demonstrates transparency and educational intent. ________________________________________ Originality & mashup justification (text you can paste) This script intentionally combines classical momentum (RSI), volatility envelope (Bollinger Bands) and trend-strength (ADX) because each indicator answers a different and complementary question: RSI answers is price locally extreme?, Bollinger answers is price outside normal volatility?, and ADX answers is the market moving with conviction?. Volume participation then acts as a practical check for real market involvement. This combination is not a simple “indicator mashup”; it is a designed ensemble where each element reduces the others’ failure modes and together produce a teachable, testable signal framework. The script’s purpose is educational and analytical — to show traders how to interpret the interplay of momentum, volatility, and trend strength. ________________________________________ TradingView publication guidance & compliance checklist To satisfy TradingView rules about mashups and descriptions, include the following items in your script description (without exposing source code): 1. Purpose statement: One or two lines describing the script’s objective (educational multi-indicator market overview and idea filter). 2. Component list: Name the major modules (RSI, Bollinger Bands, ADX, volume heuristic, SMA trend checks, signal tracking) and one-sentence reason for each. 3. How they interact: A succinct non-code explanation: “RSI finds momentum extremes; Bollinger confirms volatility expansion; ADX confirms trend strength; all three must align for a BUY/SELL.” 4. Inputs: List adjustable inputs (RSI length and thresholds, BB length & stddev, ADX threshold & smoothing, volume MA, table position/size). 5. Usage instructions: Short workflow (check TF alignment → confirm participation → define stop & R:R → backtest). 6. Limitations & assumptions: Explicitly state volume is approximated, ADX has lag, and avoid promising guaranteed profits. 7. Non-promotional language: No external contact info, ads, claims of exclusivity or guaranteed outcomes. 8. Trademark clause: If you used trademark symbols, remove or provide registration proof. 9. Risk disclaimer: Add the copy-ready disclaimer below. This matches TradingView’s request for meaningful descriptions that explain originality and inter-component reasoning. ________________________________________ Copy-ready short publication description (paste into TradingView) Advanced RSI-ADX-Bollinger Market Overview — educational multi-indicator dashboard. This script combines RSI (momentum extremes), Bollinger Bands (volatility envelope and band expansion), ADX (trend strength), simple SMA trend bias and a basic buy/sell volume heuristic to surface high-quality idea candidates. Signals require alignment of momentum, volatility expansion and rising ADX; volume participation is displayed to support signal confidence. Inputs are configurable (RSI length/levels, BB length/stddev, ADX length/threshold, volume MA, display options). This tool is intended for analysis and learning — not for automated execution. Users should back test and apply robust risk management. Limitations: volume classification here is a heuristic (close>open), ADX and BB measures lag in fast news events, and results vary by instrument liquidity. ________________________________________ Copy-ready risk & misuse disclaimer (paste into description or help file) This script is provided for educational and analytical purposes only and does not constitute financial or investment advice. It does not guarantee profits. Indicators are heuristics and may give false or late signals; always back test and paper-trade before using real capital. The author is not responsible for trading losses resulting from the use or misuse of this indicator. Use proper position sizing and risk controls. ________________________________________ Risk Disclaimer: This tool is provided for education and analysis only. It is not financial advice and does not guarantee returns. Users assume all risk for trades made based on this script. Back test thoroughly and use proper risk management.
Pine Script® göstergesi
aiTrendview-Charting-System tarafından
CNS - Multi-Timeframe Bollinger Band OscillatorMy hope is to optimize the settings for this indicator and reintroduce it as a "strategy" with suggested position entry and exit points shown in the price pane. I’ve been having good results setting the “Bollinger Band MA Length” in the Input tab to between 5 and 10. You can use the standard 20 period, but your results will not be as granular. This indicator has proven very good at finding local tops and bottoms by combining data from multiple timeframes. Use BB timeframes that are lower than the timeframe you are viewing in your price pane. The default settings work best on the weekly timeframe, but can be adjusted for most timeframes including intraday. Be cognizant that the indicator, like other oscillators, does occasionally produce divergences at tops and bottoms. Any feedback is appreciated. Overview This indicator is an oscillator that measures the normalized position of the price relative to Bollinger Bands across multiple timeframes. It takes the price's position within the Bollinger Bands (calculated on different timeframes) and averages those positions to create a single value that oscillates between 0 and 1. This value is then plotted as the oscillator, with reference lines and colored regions to help interpret the price's relative strength or weakness. How It Works Bollinger Band Calculation: The indicator uses a custom function f_getBBPosition() to calculate the position of the price within Bollinger Bands for a given timeframe. Price Position Normalization: For each timeframe, the function normalizes the price's position between the upper and lower Bollinger Bands. It calculates three positions based on the high, low, and close prices of the requested timeframe: pos_high = (High - Lower Band) / (Upper Band - Lower Band) pos_low = (Low - Lower Band) / (Upper Band - Lower Band) pos_close = (Close - Lower Band) / (Upper Band - Lower Band) If the upper band is not greater than the lower band or if the data is invalid (e.g., na), it defaults to 0.5 (the midline). The average of these three positions (avg_pos) represents the normalized position for that timeframe, ranging from 0 (at the lower band) to 1 (at the upper band). Multi-Timeframe Averaging: The indicator fetches Bollinger Band data from four customizable timeframes (default: 30min, 60min, 240min, daily) using request.security() with lookahead=barmerge.lookahead_on to get the latest available data. It calculates the normalized position (pos1, pos2, pos3, pos4) for each timeframe using f_getBBPosition(). These four positions are then averaged to produce the final avg_position:avg_position = (pos1 + pos2 + pos3 + pos4) / 4 This average is the oscillator value, which is plotted and typically oscillates between 0 and 1. Moving Averages: Two optional moving averages (MA1 and MA2) of the avg_position can be enabled, calculated using simple moving averages (ta.sma) with customizable lengths (default: 5 and 10). These can be potentially used for MA crossover strategies. What Is Being Averaged? The oscillator (avg_position) is the average of the normalized price positions within the Bollinger Bands across the four selected timeframes. Specifically:It averages the avg_pos values (pos1, pos2, pos3, pos4) calculated for each timeframe. Each avg_pos is itself an average of the normalized positions of the high, low, and close prices relative to the Bollinger Bands for that timeframe. This multi-timeframe averaging smooths out short-term fluctuations and provides a broader perspective on the price's position within the volatility bands. Interpretation 0.0 The price is at or below the lower Bollinger Band across all timeframes (indicating potential oversold conditions). 0.15: A customizable level (green band) which can be used for exiting short positions or entering long positions. 0.5: The midline, where the price is at the average of the Bollinger Bands (neutral zone). 0.85: A customizable level (orange band) which can be used for exiting long positions or entering short positions. 1.0: The price is at or above the upper Bollinger Band across all timeframes (indicating potential overbought conditions). The colored regions and moving averages (if enabled) help identify trends or crossovers for trading signals. Example If the 30min timeframe shows the close at the upper band (position = 1.0), the 60min at the midline (position = 0.5), the 240min at the lower band (position = 0.0), and the daily at the upper band (position = 1.0), the avg_position would be:(1.0 + 0.5 + 0.0 + 1.0) / 4 = 0.625 This value (0.625) would plot in the orange region (between 0.85 and 0.5), suggesting the price is relatively strong but not at an extreme. Notes The use of lookahead=barmerge.lookahead_on ensures the indicator uses the latest available data, making it more real-time, though its effectiveness depends on the chart timeframe and TradingView's data feed. The indicator’s sensitivity can be adjusted by changing bb_length ("Bollinger Band MA Length" in the Input tab), bb_mult ("Bollinger Band Standard Deviation," also in the Input tab), or the selected timeframes.
Pine Script® göstergesi
chicagonativeson tarafından
Multi-Timeframe Bollinger Band PositionBeta version. My hope is to optimize the settings for this indicator and reintroduce it as a "strategy" with suggested position entry and exit points shown in the price pane. Any feedback is appreciated. Overview This indicator is an oscillator that measures the normalized position of the price relative to Bollinger Bands across multiple timeframes. It takes the price's position within the Bollinger Bands (calculated on different timeframes) and averages those positions to create a single value that oscillates between 0 and 1. This value is then plotted as the oscillator, with reference lines and colored regions to help interpret the price's relative strength or weakness. How It Works Bollinger Band Calculation: The indicator uses a custom function f_getBBPosition() to calculate the position of the price within Bollinger Bands for a given timeframe. Price Position Normalization: For each timeframe, the function normalizes the price's position between the upper and lower Bollinger Bands. It calculates three positions based on the high, low, and close prices of the requested timeframe: pos_high = (High - Lower Band) / (Upper Band - Lower Band) pos_low = (Low - Lower Band) / (Upper Band - Lower Band) pos_close = (Close - Lower Band) / (Upper Band - Lower Band) If the upper band is not greater than the lower band or if the data is invalid (e.g., na), it defaults to 0.5 (the midline). The average of these three positions (avg_pos) represents the normalized position for that timeframe, ranging from 0 (at the lower band) to 1 (at the upper band). Multi-Timeframe Averaging: The indicator fetches Bollinger Band data from four customizable timeframes (default: 30min, 60min, 240min, daily) using request.security() with lookahead=barmerge.lookahead_on to get the latest available data. It calculates the normalized position (pos1, pos2, pos3, pos4) for each timeframe using f_getBBPosition(). These four positions are then averaged to produce the final avg_position:avg_position = (pos1 + pos2 + pos3 + pos4) / 4 This average is the oscillator value, which is plotted and typically oscillates between 0 and 1. Moving Averages: Two optional moving averages (MA1 and MA2) of the avg_position can be enabled, calculated using simple moving averages (ta.sma) with customizable lengths (default: 5 and 10). These can be potentially used for MA crossover strategies. What Is Being Averaged? The oscillator (avg_position) is the average of the normalized price positions within the Bollinger Bands across the four selected timeframes. Specifically:It averages the avg_pos values (pos1, pos2, pos3, pos4) calculated for each timeframe. Each avg_pos is itself an average of the normalized positions of the high, low, and close prices relative to the Bollinger Bands for that timeframe. This multi-timeframe averaging smooths out short-term fluctuations and provides a broader perspective on the price's position within the volatility bands. Interpretation: 0.0 The price is at or below the lower Bollinger Band across all timeframes (indicating potential oversold conditions). 0.15: A customizable level (green band) which can be used for exiting short positions or entering long positions. 0.5: The midline, where the price is at the average of the Bollinger Bands (neutral zone). 0.85: A customizable level (orange band) which can be used for exiting long positions or entering short positions. 1.0: The price is at or above the upper Bollinger Band across all timeframes (indicating potential overbought conditions). The colored regions and moving averages (if enabled) help identify trends or crossovers for trading signals. Example: If the 30min timeframe shows the close at the upper band (position = 1.0), the 60min at the midline (position = 0.5), the 240min at the lower band (position = 0.0), and the daily at the upper band (position = 1.0), the avg_position would be:(1.0 + 0.5 + 0.0 + 1.0) / 4 = 0.625 This value (0.625) would plot in the orange region (between 0.85 and 0.5), suggesting the price is relatively strong but not at an extreme. Notes: The use of lookahead=barmerge.lookahead_on ensures the indicator uses the latest available data, making it more real-time, though its effectiveness depends on the chart timeframe and TradingView's data feed. The indicator’s sensitivity can be adjusted by changing bb_length ("Bollinger Band MA Length" in the Input tab), bb_mult ("Bollinger Band Standard Deviation," also in the Input tab), or the selected timeframes.
Pine Script® göstergesi
chicagonativeson tarafından
[blackcat] L1 Bollinger Bands Width WatcherOVERVIEW The Bollinger Bands Width Watcher is an advanced tool designed to monitor the width of Bollinger Bands, providing insights into market volatility and potential trend reversals. This indicator calculates both absolute and relative widths of the bands, plotting them on the chart for easy visualization. It also generates buy and sell signals based on crossover events, helping traders make informed decisions 📊✅. Today, this article introduces the final member of the Bollinger Bands trio—Bollinger Bands Width (BBW). Derived from the renowned Bollinger Bands, this indicator measures price volatility and identifies trading signals. First, let’s delve into what Bollinger Bands are. They consist of three lines associated with the price of a security: The middle line is typically a 20-day Simple Moving Average (SMA). The upper and lower bands represent two standard deviations above and below the middle band. The Bollinger Bands Width measures the distance between these upper and lower bands. Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band Calculating BBW involves subtracting the lower band from the upper band and dividing by the middle band to obtain the BBW value. However, interpreting BBW values alone isn't enough to determine if they're narrow or wide. Different instruments or timeframes might define narrowness differently. To gauge the significance of band narrowing accurately, analyzing past BBW fluctuations alongside price movements is essential. One prominent theory involving Bollinger Bands is the "squeeze." A squeeze setup comprises two phases: Low volatility, where bands narrow, and prices move sideways. Increased volatility, where prices breach either the upper or lower band, initiating a new trend. During a bullish squeeze, BBW diminishes, and breaking above the upper band signals a new uptrend. Conversely, in a bearish squeeze, BBW declines, and falling below the lower band indicates a new downtrend. While BBW excels at spotting squeezes, traders must exercise caution. Even with a squeeze setup, a robust market trend might not materialize. Validating breakouts necessitates personal judgment and additional confirmation techniques. Now, let's explore key parameters and settings: Length: Defines the period for computing the base SMA, defaulting to 20 days. Source: Specifies the data source per candle, defaulting to the closing price. Standard Deviation: Sets the number of standard deviations from the SMA for the upper and lower bands, defaulting to 2. FEATURES Calculates Bollinger Bands Width using customizable parameters: Smoothing Length: Number of bars used for calculating the moving average and standard deviation. Source Price: Defaults to closing prices but can be adjusted. Standard Deviation Multiplier: Controls the width of the bands. Plots two types of Bollinger Bands Width: Absolute width relative to the basis (Yellow Line). Relative width compared to the close price (Fuchsia Line). Fills the area between the two plotted lines for better visual context 🌈 Generates buy ('Buy') and sell ('Sell') labels based on crossover events 🏷️ Provides alerts for crossover signals to notify users of potential trade opportunities 🔔 HOW TO USE Add the indicator to your TradingView chart by selecting it from the indicators list. Adjust the Smoothing Length, Source Price, and Standard Deviation Multiplier as needed ⚙️. Observe the plotted Bollinger Bands Width lines and filled areas for insights into market volatility. Monitor the chart for buy and sell labels indicating potential trade opportunities. Set up alerts based on the generated signals to receive notifications when conditions are met 📲. LIMITATIONS The indicator may generate false signals in highly volatile or ranging markets 🌪️. Users should combine this indicator with other forms of analysis for more reliable trading decisions. The effectiveness of the indicator may vary depending on the asset and timeframe being analyzed. NOTES Ensure that you have sufficient historical data available for accurate calculations. Test the indicator thoroughly on demo accounts before applying it to live trading 🔍. Customize the appearance and parameters as needed to fit your trading strategy.
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blackcat1402 tarafından
Enhanced Bollinger Bands Strategy with SL/TP// Title: Enhanced Bollinger Bands Strategy with SL/TP // Description: // This strategy is based on the classic Bollinger Bands indicator and incorporates Stop Loss (SL) and Take Profit (TP) levels for automated trading. It identifies potential long and short entry points based on price crossing the lower and upper Bollinger Bands, respectively. The strategy allows users to customize several parameters to suit different market conditions and risk tolerances. // Key Features: // * **Bollinger Bands:** Uses Simple Moving Average (SMA) as the basis and calculates upper and lower bands based on a user-defined standard deviation multiplier. // * **Customizable Parameters:** Offers extensive customization, including SMA length, standard deviation multiplier, Stop Loss (SL) in pips, and Take Profit (TP) in pips. // * **Long/Short Position Control:** Allows users to independently enable or disable long and short positions. // * **Stop Loss and Take Profit:** Implements Stop Loss and Take Profit levels based on pip values to manage risk and secure profits. Entry prices are set to the band levels on signals. // * **Visualizations:** Provides options to display Bollinger Bands and entry signals on the chart for easy analysis. // Strategy Logic: // 1. **Bollinger Bands Calculation:** The strategy calculates the Bollinger Bands using the specified SMA length and standard deviation multiplier. // 2. **Entry Conditions:** // * **Long Entry:** Enters a long position when the closing price crosses above the lower Bollinger Band and the `Enable Long Positions` setting is enabled. // * **Short Entry:** Enters a short position when the closing price crosses below the upper Bollinger Band and the `Enable Short Positions` setting is enabled. // 3. **Exit Conditions:** // * **Stop Loss:** Exits the position if the price reaches the Stop Loss level, calculated based on the input `Stop Loss (Pips)`. // * **Take Profit:** Exits the position if the price reaches the Take Profit level, calculated based on the input `Take Profit (Pips)`. // Input Parameters: // * **SMA Length (length):** The length of the Simple Moving Average used to calculate the Bollinger Bands (default: 20). // * **Standard Deviation Multiplier (mult):** The multiplier applied to the standard deviation to determine the width of the Bollinger Bands (default: 2.0). // * **Enable Long Positions (enableLong):** A boolean value to enable or disable long positions (default: true). // * **Enable Short Positions (enableShort):** A boolean value to enable or disable short positions (default: true). // * **Pip Value (pipValue):** The value of a pip for the traded instrument. This is crucial for accurate Stop Loss and Take Profit calculations (default: 0.0001 for most currency pairs). **Important: Adjust this value to match the specific instrument you are trading.** // * **Stop Loss (Pips) (slPips):** The Stop Loss level in pips (default: 10). // * **Take Profit (Pips) (tpPips):** The Take Profit level in pips (default: 20). // * **Show Bollinger Bands (showBands):** A boolean value to show or hide the Bollinger Bands on the chart (default: true). // * **Show Entry Signals (showSignals):** A boolean value to show or hide entry signals on the chart (default: true). // How to Use: // 1. Add the strategy to your TradingView chart. // 2. Adjust the input parameters to optimize the strategy for your chosen instrument and timeframe. Pay close attention to the `Pip Value`. // 3. Backtest the strategy over different periods to evaluate its performance. // 4. Use the `Enable Long Positions` and `Enable Short Positions` settings to customize the strategy for specific market conditions (e.g., only long positions in an uptrend). // Important Notes and Disclaimers: // * **Backtesting Results:** Past performance is not indicative of future results. Backtesting results can be affected by various factors, including market volatility, slippage, and transaction costs. // * **Risk Management:** This strategy is provided for informational and educational purposes only and should not be considered financial advice. Always use proper risk management techniques when trading. Adjust Stop Loss and Take Profit levels according to your risk tolerance. // * **Slippage:** The strategy takes into account slippage by specifying a slippage parameter on the `strategy` declaration. However, real-world slippage may vary. // * **Market Conditions:** The performance of this strategy can vary significantly depending on market conditions. It may perform well in trending markets but poorly in ranging or choppy markets. // * **Pip Value Accuracy:** **Ensure the `Pip Value` is correctly set for the specific instrument you are trading. Incorrect pip value will result in incorrect stop loss and take profit placement.** This is critical. // * **Broker Compatibility:** The strategy's performance may vary depending on your broker's execution policies and fees. // * **Disclaimer:** I am not a financial advisor, and this script is not financial advice. Use this strategy at your own risk. I am not responsible for any losses incurred while using this strategy.
Pine Script® stratejisi
Salaryman_G tarafından
Dynamic RSI Bollinger Bands with Waldo Cloud TradingView Indicator Description: Dynamic RSI Bollinger Bands with Waldo Cloud Title: Dynamic RSI Bollinger Bands with Waldo Cloud Short Title: Dynamic RSI BB Waldo Overview: Introducing an experimental indicator, the Dynamic RSI Bollinger Bands with Waldo Cloud, designed for adventurous traders looking to explore new dimensions in technical analysis. This indicator overlays on your chart, providing a unique perspective by integrating the Relative Strength Index (RSI) with Bollinger Bands, creating a dynamic trading tool that adapts to market conditions through the lens of momentum and volatility. What is it? This innovative indicator combines the traditional Bollinger Bands with the RSI in a way that hasn't been commonly explored. Here's a breakdown: RSI Integration: The RSI is calculated with customizable length settings, and its values are used not just for momentum analysis but as the basis for the Bollinger Bands. This means the position and width of the bands are directly influenced by the RSI, offering a visual representation of momentum within the context of price volatility. Dynamic Bollinger Bands: Instead of using price directly, the Bollinger Bands are calculated using a scaled version of the RSI. This scaling is done to fit the RSI values into the price range, ensuring the bands are relevant to the actual price movement. The standard deviation for these bands is also scaled accordingly, providing a unique volatility measure that's momentum-driven. Waldo Cloud: Named after a visual representation concept, the 'Waldo Cloud' refers to the colored area between the Bollinger Bands, which changes based on various conditions: Purple when RSI is overbought. Blue when RSI is oversold. Green for bullish conditions, defined by the fast-moving average crossing above the slow one, RSI is bullish, and the price is above the slow MA. Red for bearish conditions, when the fast MA crosses below the slow MA, the RSI is bearish, and the price is below the slow MA. Gray for neutral market conditions. Moving Averages: Two simple moving averages (Fast MA and Slow MA) are included, which can be toggled on or off, offering additional trend analysis through crossovers. How to Use It: Given its experimental nature, this indicator should be used with caution and in conjunction with other analysis methods: Identifying Market Conditions: Use the color of the Waldo Cloud to gauge market sentiment. A green cloud might suggest a good time to consider long positions, while a red cloud could indicate potential shorting opportunities. Purple and blue clouds highlight extreme conditions that might precede reversals. Volatility and Momentum: The dynamic nature of the Bollinger Bands based on RSI provides insight into how momentum is affecting price volatility. When the bands are wide, it might indicate high momentum and potential trend continuation or reversal, depending on the RSI's position relative to its overbought/oversold levels. Trend Confirmation: The moving average crossovers can act as confirmation signals. For instance, a bullish crossover (fast MA over slow MA) within a green cloud might strengthen a buy signal, whereas a bearish crossover in a red cloud might reinforce a sell decision. Customization: Adjust the RSI length, overbought/oversold levels, and moving average lengths to suit different trading styles or market conditions. Experiment with these settings to find what works best for your strategy. Combining with Other Indicators: Since this is an experimental tool, it's advisable to use it alongside established indicators like traditional Bollinger Bands, MACD, or trend lines to validate signals. Conclusion: The Dynamic RSI Bollinger Bands with Waldo Cloud is an experimental venture into combining momentum with volatility visually and interactively. It's designed for traders who are open to exploring new methods of market analysis. Remember, due to its experimental status, this indicator should be part of a broader trading strategy, and backtesting or paper trading is recommended before applying it in live trading scenarios. Keep an eye on how the market reacts to the signals provided by this indicator and always consider risk management practices.
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RSI_Trading_Concepts tarafından
2
Waldo Cloud Bollinger Bands Waldo Cloud Bollinger Bands Indicator Description for TradingView Title: Waldo Cloud Bollinger Bands Short Title: Waldo Cloud BB Overview: The Waldo Cloud Bollinger Bands indicator is a sophisticated tool designed for traders looking to combine the volatility analysis of Bollinger Bands with the momentum insights of the Relative Strength Index (RSI) and moving average crossovers. This indicator overlays on your chart, providing a visual representation that helps in identifying potential trading opportunities based on price action, momentum, and trend direction. Concept: This indicator merges three key technical analysis concepts: Bollinger Bands: These are used to measure market volatility. The bands consist of a central moving average (basis) with an upper and lower band that are standard deviations away from this average. In this indicator, you can customize the type of moving average used for the basis (SMA, EMA, SMMA, WMA, VWMA), the length of the period, the source price, and the standard deviation multiplier, offering flexibility to adapt to different market conditions. Relative Strength Index (RSI): The RSI is incorporated to provide insight into the momentum of price movements. Users can adjust the RSI length and overbought/oversold levels and even choose the price source for RSI calculation, allowing for tailored momentum analysis. The RSI values influence the cloud color between the Bollinger Bands, signaling market conditions. Moving Average Crossovers: Two moving averages with customizable lengths and types are used to identify trend direction through crossovers. A fast MA (default 20 periods) and a slow MA (default 50 periods) are plotted when enabled, helping to signal potential bullish or bearish market conditions when they cross over each other. Functionality: Bollinger Bands Calculation: The basis of the Bollinger Bands is calculated using a user-defined moving average type, with a customizable length, source, and standard deviation multiplier. The upper and lower bands are then plotted around this basis. RSI Calculation: The RSI is computed using a user-specified source, length, and overbought/oversold levels. This RSI value is used to determine the color of the cloud between the Bollinger Bands, which visually represents market sentiment: Purple when RSI is overbought. Blue when RSI is oversold. Green for bullish conditions (when the fast MA crosses above the slow MA, RSI is bullish, and the price is above the slow MA). Red for bearish conditions (when the fast MA crosses below the slow MA, RSI is bearish, and the price is below the slow MA). Gray for neutral conditions. Trend Analysis: The indicator uses two moving averages to help determine the trend direction. When the fast MA crosses over the slow MA, it suggests a potential change in trend direction, which, combined with RSI conditions, provides a more comprehensive trading signal. Customization: Users can select the type of moving average for all calculations through the "Global MA Type" setting, ensuring consistency in how trends and volatility are interpreted. The Bollinger Bands settings allow for adjustments in length, source, standard deviation, and offset, giving traders control over how volatility is measured. RSI settings include the ability to change the RSI source, length, and overbought/oversold thresholds, which can be fine-tuned to match trading strategies. The option to show or hide moving averages provides clarity on the chart, focusing on either the Bollinger Bands or including the MA crossovers for trend analysis. Usage: This indicator is ideal for traders who incorporate both volatility and momentum in their trading decisions. By observing the color changes in the cloud, along with the position of the price relative to the moving averages, traders can gauge potential entry and exit points. For instance, a green cloud with a price above the slow MA might suggest a strong buying opportunity, while a red cloud with a price below might indicate selling pressure. Conclusion: The Waldo Cloud Bollinger Bands indicator offers a unique blend of volatility, momentum, and trend analysis, providing traders with a multi-faceted view of market conditions. Its customization options make it adaptable to various trading styles and market environments, making it a valuable addition to any trader's toolkit on Trading View.
Pine Script® göstergesi
RSI_Trading_Concepts tarafından
EMA & Bollinger BandsThis indicator combines three main functionalities into a single script: 1. Exponential Moving Average (EMA): - Purpose: Calculates and plots the EMA of a chosen price source. - Inputs: - EMA Length: The period for the EMA calculation. - EMA Source: The price series (such as close) used for the EMA. - EMA Offset: Allows shifting the EMA line left or right on the chart. - Output: A blue-colored EMA line plotted on the chart. 2. Smoothing MA on EMA: - Purpose: Applies a secondary moving average (MA) on the previously calculated EMA. There is also an option to overlay Bollinger Bands on this smoothed MA. - Inputs: - Smoothing MA Type: Options include "None", "SMA", "SMA + Bollinger Bands", "EMA", "SMMA (RMA)", "WMA", and "VWMA". - Selecting "None" disables this feature. - Choosing "SMA + Bollinger Bands" will additionally plot Bollinger Bands around the smoothed MA. - Smoothing MA Length: The period used to calculate the smoothing MA. - BB StdDev for Smoothing MA: The standard deviation multiplier for the Bollinger Bands (applies only when "SMA + Bollinger Bands" is selected). - Calculation Details: - The chosen MA type is applied to the EMA value. - If Bollinger Bands are enabled, the script computes the standard deviation of the EMA over the smoothing period, multiplies it by the specified multiplier, and then plots an upper and lower band around the smoothing MA. - Output: - A yellow-colored smoothing MA line. - Optionally, green-colored upper and lower Bollinger Bands with a filled background if the "SMA + Bollinger Bands" option is selected. 3. Bollinger Bands on Price: - Purpose: Independently calculates and plots traditional Bollinger Bands based on a moving average of a selected price source. - Inputs: - BB Length: The period for calculating the moving average that serves as the basis of the Bollinger Bands. - BB Basis MA Type: The type of moving average to use (options include SMA, EMA, SMMA (RMA), WMA, and VWMA). - BB Source: The price series (such as close) used for the Bollinger Bands calculation. - BB StdDev: The multiplier for the standard deviation used to calculate the upper and lower bands. - BB Offset: Allows shifting the Bollinger Bands left or right on the chart. - Calculation Details: - The script computes a basis line using the selected MA type on the chosen price source. - The standard deviation of the price over the specified period is then multiplied by the provided multiplier to determine the distance for the upper and lower bands. - Output: - A basis line (typically drawn in a blue tone), an upper band (red), and a lower band (teal). - The area between the upper and lower bands is filled with a semi-transparent blue background for easier visualization. --- How It Works Together - Integration: The script is divided into clearly labeled sections for each functionality. All parts are drawn on the same chart (overlay mode enabled), providing a comprehensive view of market trends. - Customization: Users can adjust parameters for the EMA, the smoothing MA (and its optional Bollinger Bands), as well as the traditional Bollinger Bands independently. This allows for flexible customization depending on the trader's strategy or visual preference. - Utility: Combining these three analyses into one indicator enables traders to view: - The immediate trend via the EMA. - A secondary smoothed trend that might help reduce noise. - A volatility measure through Bollinger Bands on both the price and the smoothed EMA. --- This combined indicator is useful for technical analysis by providing both trend-following (EMA and smoothing MA) and volatility indicators (Bollinger Bands) in one streamlined tool.
Pine Script® göstergesi
sam123q12 tarafından
Volume Delta with Bollinger Bands [EMA]TL;DR This indicator displays a “Volume Delta” candle chart based on a lower timeframe approximation of up vs. down volume. Bollinger Bands (using an EMA and a configurable standard deviation multiplier) highlight when Volume Delta exceeds typical volatility thresholds. Green bars will darken when Volume Delta is above the upper Bollinger band, and red bars will darken when Volume Delta is below the lower Bollinger band. You can optionally include wicks in the Bollinger calculations. Note : TradingView uses tick-based volume data, so these values may not precisely match true market orders. What Is Volume Delta ? • Volume Delta is a metric that identifies buying vs. selling activity in a market by distinguishing between orders transacting at the ask (buy volume) and orders transacting at the bid (sell volume). • A positive Volume Delta indicates more buy volume during a bar, while a negative Volume Delta indicates more sell volume. How TradingView Calculates Volume Delta • TradingView relies on tick data to approximate up/down volume. This may not perfectly capture true order-flow distribution, particularly on higher timeframes or illiquid symbols. • While it can provide useful insights into volume flow, keep in mind the underlying data’s limitations. Key Features of This Indicator 1. Automatic or Custom Lower Timeframe Data • The script can automatically select a lower timeframe for Volume Delta, or you can manually specify one in the settings. 2. Bollinger Bands on Volume Delta • Uses an EMA of the Volume Delta (or a wick-based average) and calculates a standard deviation. • The upper and lower bands highlight when activity deviates from typical volatility. 3. Configurable Wick Inclusion • Decide whether to use only the “close” (lastVolume) of the Volume Delta bar or the average of its wicks ((maxVolume + minVolume) / 2) for Bollinger calculations. 4. Dynamic Bar Colors • Positive Volume Delta bars turn dark green if they exceed the upper Bollinger band, otherwise lighter green . • Negative Volume Delta bars turn dark red if they fall below the lower Bollinger band, otherwise lighter red . How To Use 1. Add the Indicator to Your Chart • Apply it to any symbol and timeframe in TradingView. • Configure the lower timeframe for Volume Delta if desired. 2. Adjust Bollinger Settings • Bollinger Length defines the EMA and standard deviation period. • Bollinger Multiplier sets how far the bands lie from the EMA. 3. Choose Whether To Use Wicks • Toggle to use the average of high/low for a potentially more volatile reading. • Turn it off to rely solely on the Volume Delta “close.” 4. Interpret the Signals • Dark Green Above the Upper Band : Suggests strong buying pressure above normal. • Lighter Green : Positive but within typical volatility bounds. • Dark Red Below the Lower Band : Suggests strong selling pressure below normal. • Lighter Red : Negative but within typical volatility. Important Caveats • TradingView Volume Data : Tick-based and aggregated data may not reflect actual order-flow precisely. • Context Matters : Combine Volume Delta with other forms of analysis (price action, support/resistance, etc.) to form a more comprehensive strategy.
Pine Script® göstergesi
itisfilipe tarafından
Accurate Bollinger Bands mcbw_ [True Volatility Distribution]The Bollinger Bands have become a very important technical tool for discretionary and algorithmic traders alike over the last decades. It was designed to give traders an edge on the markets by setting probabilistic values to different levels of volatility. However, some of the assumptions that go into its calculations make it unusable for traders who want to get a correct understanding of the volatility that the bands are trying to be used for. Let's go through what the Bollinger Bands are said to show, how their calculations work, the problems in the calculations, and how the current indicator I am presenting today fixes these. --> If you just want to know how the settings work then skip straight to the end or click on the little (i) symbol next to the values in the indicator settings window when its on your chart <-- --------------------------- What Are Bollinger Bands --------------------------- The Bollinger Bands were formed in the 1980's, a time when many retail traders interacted with their symbols via physically printed charts and computer memory for personal computer memory was measured in Kb (about a factor of 1 million smaller than today). Bollinger Bands are designed to help a trader or algorithm see the likelihood of price expanding outside of its typical range, the further the lines are from the current price implies the less often they will get hit. With a hands on understanding many strategies use these levels for designated levels of breakout trades or to assist in defining price ranges. --------------------------- How Bollinger Bands Work --------------------------- The calculations that go into Bollinger Bands are rather simple. There is a moving average that centers the indicator and an equidistant top band and bottom band are drawn at a fixed width away. The moving average is just a typical moving average (or common variant) that tracks the price action, while the distance to the top and bottom bands is a direct function of recent price volatility. The way that the distance to the bands is calculated is inspired by formulas from statistics. The standard deviation is taken from the candles that go into the moving average and then this is multiplied by a user defined value to set the bands position, I will call this value 'the multiple'. When discussing Bollinger Bands, that trading community at large normally discusses 'the multiple' as a multiplier of the standard deviation as it applies to a normal distribution (gaußian probability). On a normal distribution the number of standard deviations away (which trades directly use as 'the multiple') you are directly corresponds to how likely/unlikely something is to happen: 1 standard deviation equals 68.3%, meaning that the price should stay inside the 1 standard deviation 68.3% of the time and be outside of it 31.7% of the time; 2 standard deviation equals 95.5%, meaning that the price should stay inside the 2 standard deviation 95.5% of the time and be outside of it 4.5% of the time; 3 standard deviation equals 99.7%, meaning that the price should stay inside the 3 standard deviation 99.7% of the time and be outside of it 0.3% of the time. Therefore when traders set 'the multiple' to 2, they interpret this as meaning that price will not reach there 95.5% of the time. ---------------- The Problem With The Math of Bollinger Bands ---------------- In and of themselves the Bollinger Bands are a great tool, but they have become misconstrued with some incorrect sense of statistical meaning, when they should really just be taken at face value without any further interpretation or implication. In order to explain this it is going to get a bit technical so I will give a little math background and try to simplify things. First let's review some statistics topics (distributions, percentiles, standard deviations) and then with that understanding explore the incorrect logic of how Bollinger Bands have been interpreted/employed. ---------------- Quick Stats Review ---------------- . (If you are comfortable with statistics feel free to skip ahead to the next section) . -------- I: Probability distributions -------- When you have a lot of data it is helpful to see how many times different results appear in your dataset. To visualize this people use "histograms", which just shows how many times each element appears in the dataset by stacking each of the same elements on top of each other to form a graph. You may be familiar with the bell curve (also called the "normal distribution", which we will be calling it by). The normal distribution histogram looks like a big hump around zero and then drops off super quickly the further you get from it. This shape (the bell curve) is very nice because it has a lot of very nifty mathematical properties and seems to show up in nature all the time. Since it pops up in so many places, society has developed many different shortcuts related to it that speed up all kinds of calculations, including the shortcut that 1 standard deviation = 68.3%, 2 standard deviations = 95.5%, and 3 standard deviations = 99.7% (these only apply to the normal distribution). Despite how handy the normal distribution is and all the shortcuts we have for it are, and how much it shows up in the natural world, there is nothing that forces your specific dataset to look like it. In fact, your data can actually have any possible shape. As we will explore later, economic and financial datasets *rarely* follow the normal distribution. -------- II: Percentiles -------- After you have made the histogram of your dataset you have built the "probability distribution" of your own dataset that is specific to all the data you have collected. There is a whole complicated framework for how to accurately calculate percentiles but we will dramatically simplify it for our use. The 'percentile' in our case is just the number of data points we are away from the "middle" of the data set (normally just 0). Lets say I took the difference of the daily close of a symbol for the last two weeks, green candles would be positive and red would be negative. In this example my dataset of day by day closing price difference is: week 1: week 2: sorting all of these value into a single dataset I have: I can separate the positive and negative returns and explore their distributions separately: negative return distribution = positive return distribution = Taking the 25th% percentile of these would just be taking the value that is 25% towards the end of the end of these returns. Or akin the 100%th percentile would just be taking the vale that is 100% at the end of those: negative return distribution (50%) = -5 positive return distribution (50%) = +4 negative return distribution (100%) = -10 positive return distribution (100%) = +20 Or instead of separating the positive and negative returns we can also look at all of the differences in the daily close as just pure price movement and not account for the direction, in this case we would pool all of the data together by ignoring the negative signs of the negative reruns combined return distribution = In this case the 50%th and 100%th percentile of the combined return distribution would be: combined return distribution (50%) = 4 combined return distribution (100%) = 10 Sometimes taking the positive and negative distributions separately is better than pooling them into a combined distribution for some purposes. Other times the combined distribution is better. Most financial data has very different distributions for negative returns and positive returns. This is encapsulated in sayings like "Price takes the stairs up and the elevator down". -------- III: Standard Deviation -------- The formula for the standard deviation (refereed to here by its shorthand 'STDEV') can be intimidating, but going through each of its elements will illuminate what it does. The formula for STDEV is equal to: square root ( (sum ) / N ) Going back the the dataset that you might have, the variables in the formula above are: 'mean' is the average of your entire dataset 'x' is just representative of a single point in your dataset (one point at a time) 'N' is the total number of things in your dataset. Going back to the STDEV formula above we can see how each part of it works. Starting with the '(x - mean)' part. What this does is it takes every single point of the dataset and measure how far away it is from the mean of the entire dataset. Taking this value to the power of two: '(x - mean) ^ 2', means that points that are very far away from the dataset mean get 'penalized' twice as much. Points that are very close to the dataset mean are not impacted as much. In practice, this would mean that if your dataset had a bunch of values that were in a wide range but always stayed in that range, this value ('(x - mean) ^ 2') would end up being small. On the other hand, if your dataset was full of the exact same number, but had a couple outliers very far away, this would have a much larger value since the square par of '(x - mean) ^ 2' make them grow massive. Now including the sum part of 'sum ', this just adds up all the of the squared distanced from the dataset mean. Then this is divided by the number of values in the dataset ('N'), and then the square root of that value is taken. There is nothing inherently special or definitive about the STDEV formula, it is just a tool with extremely widespread use and adoption. As we saw here, all the STDEV formula is really doing is measuring the intensity of the outliers. --------------------------- Flaws of Bollinger Bands --------------------------- The largest problem with Bollinger Bands is the assumption that price has a normal distribution. This is assumption is massively incorrect for many reasons that I will try to encapsulate into two points: Price return do not follow a normal distribution, every single symbol on every single timeframe has is own unique distribution that is specific to only itself. Therefore all the tools, shortcuts, and ideas that we use for normal distributions do not apply to price returns, and since they do not apply here they should not be used. A more general approach is needed that allows each specific symbol on every specific timeframe to be treated uniquely. The distributions of price returns on the positive and negative side are almost never the same. A more general approach is needed that allows positive and negative returns to be calculated separately. In addition to the issues of the normal distribution assumption, the standard deviation formula (as shown above in the quick stats review) is essentially just a tame measurement of outliers (a more aggressive form of outlier measurement might be taking the differences to the power of 3 rather than 2). Despite this being a bit of a philosophical question, does the measurement of outlier intensity as defined by the STDEV formula really measure what we want to know as traders when we're experiencing volatility? Or would adjustments to that formula better reflect what we *experience* as volatility when we are actively trading? This is an open ended question that I will leave here, but I wanted to pose this question because it is a key part of what how the Bollinger Bands work that we all assume as a given. Circling back on the normal distribution assumption, the standard deviation formula used in the calculation of the bands only encompasses the deviation of the candles that go into the moving average and have no knowledge of the historical price action. Therefore the level of the bands may not really reflect how the price action behaves over a longer period of time. ------------ Delivering Factually Accurate Data That Traders Need------------ In light of the problems identified above, this indicator fixes all of these issue and delivers statistically correct information that discretionary and algorithmic traders can use, with truly accurate probabilities. It takes the price action of the last 2,000 candles and builds a huge dataset of distributions that you can directly select your percentiles from. It also allows you to have the positive and negative distributions calculated separately, or if you would like, you can pool all of them together in a combined distribution. In addition to this, there is a wide selection of moving averages directly available in the indicator to choose from. Hedge funds, quant shops, algo prop firms, and advanced mechanical groups all employ the true return distributions in their work. Now you have access to the same type of data with this indicator, wherein it's doing all the lifting for you. ------------------------------ Indicator Settings ------------------------------ . ---- Moving average ---- Select the type of moving average you would like and its length ---- Bands ---- The percentiles that you enter here will be pulled directly from the return distribution of the last 2,000 candles. With the typical Bollinger Bands, traders would select 2 standard deviations and incorrectly think that the levels it highlights are the 95.5% levels. Now, if you want the true 95.5% level, you can just enter 95.5 into the percentile value here. Each of the three available bands takes the true percentile you enter here. ---- Separate Positive & Negative Distributions---- If this box is checked the positive and negative distributions are treated indecently, completely separate from each other. You will see that the width of the top and bottom bands will be different for each of the percentiles you enter. If this box is unchecked then all the negative and positive distributions are pooled together. You will notice that the width of the top and bottom bands will be the exact same. ---- Distribution Size ---- This is the number of candles that the price return is calculated over. EG: to collect the price return over the last 33 candles, the difference of price from now to 33 candles ago is calculated for the last 2,000 candles, to build a return distribution of 2000 points of price differences over 33 candles. NEGATIVE NUMBERS(<0) == exact number of candles to include; EG: setting this value to -20 will always collect volatility distributions of 20 candles POSITIVE NUMBERS(>0) == number of candles to include as a multiple of the Moving Average Length value set above; EG: if the Moving Average Length value is set to 22, setting this value to 2 will use the last 22*2 = 44 candles for the collection of volatility distributions MORE candles being include will generally make the bands WIDER and their size will change SLOWER over time. I wish you focus, dedication, and earnest success on your journey. Happy trading :)
Pine Script® göstergesi
mcbw_ tarafından
MegaGas Bollinger Bands with Divergence and Circle SignalsIndicator: MegaGas Bollinger Bands with Divergence and Circle Signals This script provides a powerful combination of Bollinger Bands, RSI Divergence detection, and signal visualization tools. Designed with flexibility and precision in mind, it aims to assist traders in identifying trend reversals, volatility zones, and divergence-based trading opportunities. The script is well-suited for swing trading, momentum trading, and even scalping when adapted to lower timeframes. How It Works: Bollinger Bands: Bollinger Bands are used to detect price volatility and overbought/oversold conditions. The script calculates: Basis Line: A 34-period Simple Moving Average (SMA) as the core trend line. Upper Bands: Bands positioned 1x and 2x the standard deviation above the SMA. Lower Bands: Bands positioned 1x and 2x the standard deviation below the SMA. These levels provide dynamic support and resistance zones, highlighting breakout and reversion opportunities. RSI Divergence Detection: The indicator detects bullish divergence (when RSI forms a higher low while price forms a lower low) and bearish divergence (when RSI forms a lower high while price forms a higher high). These divergences often precede significant reversals or momentum shifts. Bullish divergence is displayed with blue triangles (up). Bearish divergence is displayed with orange triangles (down). Buy and Sell Signals: Circle Signals are generated when price crosses key Bollinger Bands levels: A green circle appears when the price crosses above the lower band (potential buy signal). A red circle appears when the price crosses below the upper band (potential sell signal). These signals help identify potential entry and exit points for trades, particularly in trend-following or mean-reversion strategies. Trend Reference (Moving Average): A 50-period Simple Moving Average (SMA) is included as a trend reference, helping traders gauge the overall market direction. Use this to confirm divergence signals and avoid trades against the prevailing trend. Why This Indicator Is Unique: This script integrates multiple tools in a meaningful way, emphasizing contextual trading signals. Unlike standalone Bollinger Bands or RSI indicators, it introduces: Advanced Divergence Analysis: Enhancing traditional RSI with divergence-based alerts. Dynamic Signal Filtering: Preventing repetitive signals by introducing state-based logic for circles and divergence signals. Trend Alignment: Combining Bollinger Bands with an SMA to filter trades based on the prevailing trend. How to Use: Setup: Apply the indicator to any chart and timeframe. For swing trading, higher timeframes like 4H or 1D are recommended. Adjust the RSI, Bollinger Bands, and Moving Average lengths to match your strategy and asset. Signals: Look for divergence signals (triangles) as early warnings of trend reversals. Confirm these with price action or other tools. Use circle signals (green/red) to time potential entries/exits around Bollinger Band extremes. Confirmation: Combine divergence and circle signals with the SMA line to avoid counter-trend trades. For example, take bullish signals when the price is above the SMA and bearish signals when it is below. Chart Clarity: The script is published with a clean chart for clarity. It visualizes all signals with distinct shapes (triangles and circles) and colors, ensuring they are easily recognizable. Bollinger Bands and the SMA are plotted with transparency to avoid clutter. Originality: This script is a thoughtful blend of Bollinger Bands and RSI divergence detection, carefully designed to provide traders with actionable insights. It introduces state-based logic to manage repetitive signals and seamlessly integrates trend filtering, making it a valuable tool for both novice and experienced traders.
Pine Script® göstergesi
megagas tarafından
PTS - Bollinger Bands with Trailing StopPTS - Bollinger Bands with Trailing Stop Strategy Overview The "PTS - Bollinger Bands with Trailing Stop" strategy is designed to capitalize on strong bullish market movements by combining the Bollinger Bands indicator with a dynamic trailing stop based on the Average True Range (ATR). This strategy aims to enter long positions during upward breakouts and protect profits through an adaptive exit mechanism. Key Features 1. Bollinger Bands Indicator Basis Moving Average Type: Choose from SMA, EMA, SMMA, WMA, or VWMA for the Bollinger Bands' basis line. Length: Adjustable period for calculating the moving average and standard deviation (default is 20). Standard Deviation Multiplier (StdDev): Determines the width of the bands (default is 2.0). Source: Select the price data used for calculations (default is the closing price). 2. Entry Conditions The strategy enters a long position when the closing price crosses above the upper Bollinger Band , indicating strong upward momentum. 3. Exit Conditions Trailing Stop Loss: Implemented using ATR to adjust dynamically with market volatility. ATR Length: Period for calculating ATR (default is 14). ATR Multiplier for Trailing Stop: Determines how closely the trailing stop follows the price (default is 2.0). Close Below Lower Bollinger Band: The strategy exits the long position if the closing price crosses below the lower Bollinger Band . 4. Risk Management Commission and Slippage: Commission is set at 0.1%; slippage is set to 3. Position Sizing: Uses 100% of equity per trade (adjustable). 5. Date Range Filter Specify the time period during which the strategy is active. Start Date: January 1, 2018. End Date: December 31, 2069. Customizable Inputs BB Length: Adjust the period for Bollinger Bands calculation. BB StdDev: Modify the standard deviation multiplier. Basis MA Type: Select the moving average type. Source: Choose the price data source. Offset: Shift the Bollinger Bands on the chart. ATR Length: Set the period for ATR calculation. ATR Multiplier for Trailing Stop: Adjust the trailing stop sensitivity. Thought for 1m 7s Description for the "PTS - Bollinger Bands with Trailing Stop" Strategy PTS - Bollinger Bands with Trailing Stop Strategy Overview The "PTS - Bollinger Bands with Trailing Stop" strategy is designed to capitalize on strong bullish market movements by combining the Bollinger Bands indicator with a dynamic trailing stop based on the Average True Range (ATR). This strategy aims to enter long positions during upward breakouts and protect profits through an adaptive exit mechanism. Key Features 1. Bollinger Bands Indicator Basis Moving Average Type: Choose from SMA, EMA, SMMA, WMA, or VWMA for the Bollinger Bands' basis line. Length: Adjustable period for calculating the moving average and standard deviation (default is 20). Standard Deviation Multiplier (StdDev): Determines the width of the bands (default is 2.0). Source: Select the price data used for calculations (default is the closing price). 2. Entry Conditions The strategy enters a long position when the closing price crosses above the upper Bollinger Band , indicating strong upward momentum. 3. Exit Conditions Trailing Stop Loss: Implemented using ATR to adjust dynamically with market volatility. ATR Length: Period for calculating ATR (default is 14). ATR Multiplier for Trailing Stop: Determines how closely the trailing stop follows the price (default is 2.0). Close Below Lower Bollinger Band: The strategy exits the long position if the closing price crosses below the lower Bollinger Band . 4. Risk Management Commission and Slippage: Commission is set at 0.1%; slippage is set to 3. Position Sizing: Uses 100% of equity per trade (adjustable). 5. Date Range Filter Specify the time period during which the strategy is active. Start Date: January 1, 2018. End Date: December 31, 2069. Customizable Inputs BB Length: Adjust the period for Bollinger Bands calculation. BB StdDev: Modify the standard deviation multiplier. Basis MA Type: Select the moving average type. Source: Choose the price data source. Offset: Shift the Bollinger Bands on the chart. ATR Length: Set the period for ATR calculation. ATR Multiplier for Trailing Stop: Adjust the trailing stop sensitivity. How the Strategy Works 1. Initialization Calculates Bollinger Bands and ATR based on selected parameters. 2. Entry Logic Opens a long position when the closing price exceeds the upper Bollinger Band. 3. Exit Logic Uses a trailing stop loss based on ATR. Exits if the closing price drops below the lower Bollinger Band. 4. Date Filtering Executes trades only within the specified date range. Advantages Adaptive Risk Management: Trailing stop adjusts to market volatility. Simplicity: Clear entry and exit signals. Customizable Parameters: Tailor the strategy to different assets or conditions. Considerations Aggressive Position Sizing: Using 100% equity per trade is high-risk. Market Conditions: Best in trending markets; may produce false signals in sideways markets. Backtesting: Always test on historical data before live trading. Disclaimer This strategy is intended for educational and informational purposes only. Trading involves significant risk, and past performance is not indicative of future results. Assess your financial situation and consult a financial advisor if necessary. Usage Instructions 1. Apply the Strategy: Add it to your TradingView chart. 2. Configure Inputs: Adjust parameters to suit your style and asset. 3. Analyze Backtest Results: Use the Strategy Tester. 4. Optimize Parameters: Experiment with input values. 5. Risk Management: Evaluate position sizing and incorporate risk controls. Final Notes The "PTS - Bollinger Bands with Trailing Stop" strategy provides a framework to leverage momentum breakouts while managing risk through adaptive trailing stops. Customize and test thoroughly to align with your trading objectives.
Pine Script® stratejisi
posido tarafından
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