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Indicatori e strategie

Heikin Ashi Colored Regular OHLC CandlesHeikin Ashi Colored Regular OHLC Candles In the world of trading, Heikin Ashi candles are a popular tool for smoothing out price action and identifying trends more clearly. However, Heikin Ashi candles do not reflect the actual open, high, low, and close prices of a market. They are calculated values that change the chart’s structure. This can make it harder to see precise price levels or use standard price-based tools effectively. To get the best of both worlds, we can apply the color logic of Heikin Ashi candles to regular OHLC candles. This means we keep the true market data, but show the trend visually in the same smooth way Heikin Ashi candles do. Why use this approach Heikin Ashi color logic filters out noise and helps provide a clearer view of the current trend direction. Since we are still plotting real OHLC candles, we do not lose important price information such as actual highs, lows, or closing prices. This method offers a hybrid view that combines the accuracy of real price levels with the visual benefits of Heikin Ashi trend coloring. It also helps maintain visual consistency for traders who are used to Heikin Ashi signals but want to see real price action. Advantages for scalping Scalping requires fast decisions. Even small price noise can lead to hesitation or bad entries. Coloring regular candles based on Heikin Ashi direction helps reduce that noise and makes short-term trends easier to read. It allows for faster confirmation of momentum without switching away from real prices. Since the candles are not modified, scalpers can still place tight stop-losses and targets based on actual price structure. This approach also avoids clutter, keeping the chart clean and focused. How it works We calculate the Heikin Ashi values in the background. If the Heikin Ashi close is higher than the Heikin Ashi open, the trend is considered bullish and the candle is colored green. If the close is lower than the open, it is bearish and the candle is red. If they are equal, the candle is gray or neutral. We then use these colors to paint the real OHLC candles, which are unchanged in shape or position.
Indicatore Pine Script®
di LuxmiAI
G10 FX Basket ComparisonDescription: This indicator shows how individual FX major currencies (including CNY) have performed relative to each other. It calculates each currency's performance against a "Trade Weighted" basket of other major currencies. I created this because I couldn't find it, and I wanted an easy way to see currency behaviour and flows. Purpose: It lets you see the relative strength and weakness of each currency, similar to how the DXY measures USD strength, but for all the major currencies. Each basket and currency weights are based on Trade Weighted values from literature/economics. This way you can maybe decide which crosses / pairs to trade. Can helps you visualise how events (economic, news or otherwise) affect currency flows. Features: Relative Performance: Focuses on how a currency's value has changed over time, rather than its absolute level. Normalization: Adjusts currency values to a starting date, making it easy to compare their performance. Adjustable Start Date: You can set the anchor date to choose the starting point for calculating relative performance. Customizable Weights: The indicator allows you to use custom weights for each currency basket should you wish.
Indicatore Pine Script®
di Things
1
Imaneshun V2The "Imaneshun V2" golden strategy in forex, inspired by ImanTrading, is an evolved categorical trading approach centered on price action classification. It segments market behavior into consolidation and directional phases, adaptable across all timeframes. Imaneshun V2 emphasizes disciplined entries based on current candle structure and price action. It avoids fixed risk-reward ratios, prioritizing personal win rate development. Trade journaling remains essential for strategy refinement. Traders assess recent candle patterns to set stop-loss and target levels strategically. Imaneshun V2 discourages entries without clear price action confirmation, using visual cues to distinguish optimal setups. Developed through iterative experimentation, it remains free of external biases. The strategy maintains simplicity but requires deep understanding via comprehensive video guides. Early avoidance of rigid rules ensures flexibility in application. Scalping is a key application, though it supports diverse trading styles. Robust risk management focuses on avoiding unaffordable losses. Imaneshun V2 acknowledges trading’s inherent challenges, demanding persistence. It rejects guaranteed-profit claims, promoting realistic expectations. Enhanced market structure analysis improves trade decision-making. Extensive backtesting is critical to validate setups. Imaneshun V2 avoids exaggerated promises, focusing on practical execution. Continuous learning and adaptation are vital for mastery. It’s tailored for disciplined traders dedicated to sustained growth.
Indicatore Pine Script®
di playoff-yoyo-stick
imaneshunThe "Imaneshun" golden strategy in forex, inspired by ImanTrading, is a categorical trading approach focusing on price action classification. It divides market behavior into consolidation and directional phases, adaptable across all timeframes. The strategy emphasizes disciplined trade entry based on current candle structure and price action. It avoids reliance on fixed risk-reward ratios, stressing the importance of personal win rate. Journaling and recording trades are critical for refining the approach. Imaneshun requires traders to assess recent candle patterns to set stop-loss and target levels. It discourages trading without clear price action confirmation, using visual reminders of good versus bad setups. The strategy was developed through experimentation, free from external influences. It prioritizes simplicity but demands thorough understanding via detailed video guides. Traders must avoid rigid rules early on to maintain flexibility. Scalping is one application, though it’s versatile for other styles. Risk management is central, with a focus on not risking unaffordable losses. The strategy underscores the difficulty of trading, requiring persistence. It’s not a guaranteed path to profit, emphasizing realistic expectations. Imaneshun integrates market structure analysis for better trade decisions. Traders are encouraged to backtest extensively to validate setups. The approach avoids overhyped claims, focusing on practical execution. Continuous learning and adaptation are key to mastering Imaneshun. It’s designed for disciplined traders committed to long-term growth. (www.imantrading.org)
Indicatore Pine Script®
di playoff-yoyo-stick
1
TradeNeon - Level of InterestLevel of Interest (LOI) is part of the TradeNeon Software Portfolio – built for professional futures traders. LOI highlights key price zones of institutional interest based on proprietary models and displays them directly on your chart. It helps you cut through market noise, improve timing, and focus on what truly matters: structure, context, and key decision points. Use LOI to navigate markets with clarity and confidence. Level of Interest – See what matters. Use the daily, professional market analyses to your advantage. Our experts with years of experience identify precise price areas of institutional interest. Maximize your trading potential and use these high-quality trading locations for your individual strategies. tradeneon-academy.com/level-of-interest/
Indicatore Pine Script®
di l4am
Envelope//@version=5 indicator("FX 5分足 EMA+MACD+RSI 手法アラート", overlay=true) // === EMA設定 === ema20 = ta.ema(close, 20) ema50 = ta.ema(close, 50) ema100 = ta.ema(close, 100) plot(ema20, color=color.yellow, title="EMA 20") plot(ema50, color=color.orange, title="EMA 50") plot(ema100, color=color.red, title="EMA 100") // === MACD設定 === = ta.macd(close, 12, 26, 9) macdBuy = ta.crossover(macdLine, signalLine) macdSell = ta.crossunder(macdLine, signalLine) // === RSI設定 === rsi = ta.rsi(close, 14) // === ロング条件 === longCondition = ema20 > ema50 and ema50 > ema100 and close >= ema20 and macdBuy and rsi > 50 and rsi < 70 plotshape(longCondition, location=location.belowbar, color=color.green, style=shape.labelup, text="Buy", title="Buy Signal") // === ショート条件 === shortCondition = ema20 < ema50 and ema50 < ema100 and close <= ema20 and macdSell and rsi < 50 and rsi > 30 plotshape(shortCondition, location=location.abovebar, color=color.red, style=shape.labeldown, text="Sell", title="Sell Signal") // === アラート設定 === alertcondition(longCondition, title="Buy Alert", message="Buy Signal: EMA + MACD + RSI conditions met") alertcondition(shortCondition, title="Sell Alert", message="Sell Signal: EMA + MACD + RSI conditions met")
Indicatore Pine Script®
di tosimasa
Pro-Indicatorn trial version Gold, Forex & Crypto Trading Indicator for TradingView – Automated, Powerful, Highly Customizable Developed for modern traders on TradingView, this indicator is a comprehensive automated trading tool designed for Gold (XAUUSD), Forex, and Crypto markets, packed with advanced features: 🔹 Automatic Buy/Sell Signals Detects precise trade entries based on price action and liquidity zones – ideal for both short-term and mid-term trading. Signals are clearly displayed directly on your TradingView chart. 🔹 Auto Stop Loss & Lot Size Calculation Automatically calculates the optimal stop loss and accurate position size (lot) based on your risk settings, helping you manage risk professionally and effortlessly. 🔹 Automatic Liquidity Zone Detection Scans and highlights liquidity areas on lower timeframes and on M15/M30, supporting smart money trading strategies. 🔹 Advanced Noise Filtering Mode for Higher Signal Accuracy Applies customizable signal filtering methods, allowing users to fine-tune based on their trading style – including EMA conditions, candle body size, market structure, and more. 🔹 Automatically Draws Key Technical Elements: Gap/FVG (Fair Value Gap) Fractals Trading Sessions Killzones (high-probability trading timeframes) ...and many more powerful tools for price action and SMC-based analysis.
Indicatore Pine Script®
di linhdan2025
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Indicatore Pine Script®
di Tradedioschi
Test OHLCV LibraryThis indicator, "Test OHLCV Library," serves as a practical example of how to use the OHLCVData library to fetch historical candle data from a specific timeframe (like 4H) in a way that is largely impervious to the chart's currently selected time frame. Here's a breakdown of its purpose and how it addresses request.security limitations: Indicator Purpose: The main goal of this indicator is to demonstrate and verify that the OHLCVData library can reliably provide confirmed historical OHLCV data for a user-specified timeframe (e.g., 4H), and that a collection of these data points (the last 10 completed candles) remains consistent even when the user switches the chart's time frame (e.g., from 5-second to Daily). It does this by: Importing the OHLCVData library. Using the library's getTimeframeData function on every bar of the chart. Checking the isTargetBarClosed flag returned by the library to identify the exact moment a candle in the target timeframe (e.g., 4H) has closed. When isTargetBarClosed is true, it captures the confirmed OHLCV data provided by the library for that moment and stores it in a persistent var array. It maintains a list of the last 10 captured historical 4H candle opens in this array. It displays these last 10 confirmed opens in a table. It uses the isAdjustedToChartTF flag from the library to show a warning if the chart's time frame is higher than the target timeframe, indicating that the data fetched by request.security is being aligned to that higher resolution. Circumventing request.security Limitations: The primary limitation of request.security that this setup addresses is the challenge of getting a consistent, non-repainting collection of historical data points from a different timeframe when the chart's time frame is changed. The Problem: Standard request.security calls, while capable of fetching data from other timeframes, align that data to the bars of the current chart. When you switch the chart's time frame, the set of chart bars changes, and the way the requested data aligns to these new bars changes. If you simply collected data on every chart bar where request.security returned a non-na value, the resulting collection would differ depending on the chart's resolution. Furthermore, using request.security without lookahead=barmerge.lookahead_off or an offset ( ) can lead to repainting on historical bars, where values change as the script recalculates. How the Library/Indicator Setup Helps: Confirmed Data: The OHLCVData library uses lookahead=barmerge.lookahead_off and, more importantly, provides the isTargetBarClosed flag. This flag is calculated using a reliable method (checking for a change in the target timeframe's time series) that accurately identifies the precise chart bar corresponding to the completion of a candle in the target timeframe (e.g., a 4H candle), regardless of the chart's time frame. Precise Capture: The indicator only captures and stores the OHLCV data into its var array when this isTargetBarClosed flag is true. This means it's capturing the confirmed, finalized data for the target timeframe candle at the exact moment it closes. Persistent Storage: The var array in the indicator persists its contents across the bars of the chart's history. As the script runs through the historical bars, it selectively adds confirmed 4H candle data points to this array only when the trigger is met. Impervious Collection: Because the array is populated based on the completion of the target timeframe candles (detected reliably by the library) rather than simply collecting data on every chart bar, the final contents of the array (the list of the last 10 confirmed 4H opens) will be the same regardless of the chart's time frame. The table then displays this static collection. In essence, this setup doesn't change how request.security fundamentally works or aligns data to the chart's bars. Instead, it uses the capabilities of request.security (fetching data from another timeframe) and Pine Script's execution model (bar-by-bar processing, var persistence) in a specific way, guided by the library's logic, to build a historical collection of data points that represent the target timeframe's candles and are independent of the chart's display resolution.
Indicatore Pine Script®
di viorel8
MACD+ADX Enhanced IndicatorThe MACD+ADX Enhanced Indicator is a Pine Script v5 trading indicator that combines MACD, ADX, trend confirmation, and OBV signals to identify buy and sell opportunities. It dynamically adjusts MACD parameters based on VIX volatility, making it adaptable to varying market conditions. The indicator displays buy (green triangles/bars) and sell (red triangles/bars) signals on the chart, along with an SMA trend line, while MACD and ADX are shown in separate panels.Blessing every one can retire before 40 years old, and everyone should be retire 40 years old, don't work as a machine
Indicatore Pine Script®
di Charity_Quant
S&P 500 Top 25 - EPS AnalysisEarnings Surprise Analysis Framework for S&P 500 Components: A Technical Implementation The "S&P 500 Top 25 - EPS Analysis" indicator represents a sophisticated technical implementation designed to analyze earnings surprises among major market constituents. Earnings surprises, defined as the deviation between actual reported earnings per share (EPS) and analyst estimates, have been consistently documented as significant market-moving events with substantial implications for price discovery and asset valuation (Ball and Brown, 1968; Livnat and Mendenhall, 2006). This implementation provides a comprehensive framework for quantifying and visualizing these deviations across multiple timeframes. The methodology employs a parameterized approach that allows for dynamic analysis of up to 25 top market capitalization components of the S&P 500 index. As noted by Bartov et al. (2002), large-cap stocks typically demonstrate different earnings response coefficients compared to their smaller counterparts, justifying the focus on market leaders. The technical infrastructure leverages the TradingView Pine Script language (version 6) to construct a real-time analytical framework that processes both actual and estimated EPS data through the platform's request.earnings() function, consistent with approaches described by Pine (2022) in financial indicator development documentation. At its core, the indicator calculates three primary metrics: actual EPS, estimated EPS, and earnings surprise (both absolute and percentage values). This calculation methodology aligns with standardized approaches in financial literature (Skinner and Sloan, 2002; Ke and Yu, 2006), where percentage surprise is computed as: (Actual EPS - Estimated EPS) / |Estimated EPS| × 100. The implementation rigorously handles potential division-by-zero scenarios and missing data points through conditional logic gates, ensuring robust performance across varying market conditions. The visual representation system employs a multi-layered approach consistent with best practices in financial data visualization (Few, 2009; Tufte, 2001). The indicator presents time-series plots of the four key metrics (actual EPS, estimated EPS, absolute surprise, and percentage surprise) with customizable color-coding that defaults to industry-standard conventions: green for actual figures, blue for estimates, red for absolute surprises, and orange for percentage deviations. As demonstrated by Padilla et al. (2018), appropriate color mapping significantly enhances the interpretability of financial data visualizations, particularly for identifying anomalies and trends. The implementation includes an advanced background coloring system that highlights periods of significant earnings surprises (exceeding ±3%), a threshold identified by Kinney et al. (2002) as statistically significant for market reactions. Additionally, the indicator features a dynamic information panel displaying current values, historical maximums and minimums, and sample counts, providing important context for statistical validity assessment. From an architectural perspective, the implementation employs a modular design that separates data acquisition, processing, and visualization components. This separation of concerns facilitates maintenance and extensibility, aligning with software engineering best practices for financial applications (Johnson et al., 2020). The indicator processes individual ticker data independently before aggregating results, mitigating potential issues with missing or irregular data reports. Applications of this indicator extend beyond merely observational analysis. As demonstrated by Chan et al. (1996) and more recently by Chordia and Shivakumar (2006), earnings surprises can be successfully incorporated into systematic trading strategies. The indicator's ability to track surprise percentages across multiple companies simultaneously provides a foundation for sector-wide analysis and potentially improves portfolio management during earnings seasons, when market volatility typically increases (Patell and Wolfson, 1984). References: Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 6(2), 159-178. Bartov, E., Givoly, D., & Hayn, C. (2002). The rewards to meeting or beating earnings expectations. Journal of Accounting and Economics, 33(2), 173-204. Bernard, V. L., & Thomas, J. K. (1989). Post-earnings-announcement drift: Delayed price response or risk premium? Journal of Accounting Research, 27, 1-36. Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681-1713. Chordia, T., & Shivakumar, L. (2006). Earnings and price momentum. Journal of Financial Economics, 80(3), 627-656. Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273. Johnson, J. A., Scharfstein, B. S., & Cook, R. G. (2020). Financial software development: Best practices and architectures. Wiley Finance. Ke, B., & Yu, Y. (2006). The effect of issuing biased earnings forecasts on analysts' access to management and survival. Journal of Accounting Research, 44(5), 965-999. Kinney, W., Burgstahler, D., & Martin, R. (2002). Earnings surprise "materiality" as measured by stock returns. Journal of Accounting Research, 40(5), 1297-1329. Livnat, J., & Mendenhall, R. R. (2006). Comparing the post-earnings announcement drift for surprises calculated from analyst and time series forecasts. Journal of Accounting Research, 44(1), 177-205. Padilla, L., Kay, M., & Hullman, J. (2018). Uncertainty visualization. Handbook of Human-Computer Interaction. Patell, J. M., & Wolfson, M. A. (1984). The intraday speed of adjustment of stock prices to earnings and dividend announcements. Journal of Financial Economics, 13(2), 223-252. Skinner, D. J., & Sloan, R. G. (2002). Earnings surprises, growth expectations, and stock returns or don't let an earnings torpedo sink your portfolio. Review of Accounting Studies, 7(2-3), 289-312. Tufte, E. R. (2001). The visual display of quantitative information (Vol. 2). Graphics Press.
Indicatore Pine Script®
di EdgeTools