Trendlineanalysis — אינדיקטורים ואסטרטגיות

Momentum Swing 1–3 Weeks ✅ Entry (LONG) Conditions Price above EMA9 and SMA20 SMA20 > SMA50 (trend confirmation) MACD above the signal line RSI between 50–65 (healthy momentum) Volume at least 20% above the 20-day average When all conditions align, a LONG signal is generated. ✅ Exit (SELL) Conditions Price closes below EMA9 MACD gives a bearish crossover Or TP/SL levels are hit Position is closed. ✅ Multi-Stage Take Profit TP1: ATR × 1.5 → closes 50% of the position TP2: ATR × 3.0 → closes remaining 50% ✅ Stop Loss ATR × 1.5 dynamic SL ✅ What This Strategy Aims For Catching early trend continuation signals Filtering weak / low-volume breakouts Exiting when momentum fades Eliminating emotional decision-making through rules 📌 Note Backtest performance may vary by symbol and volatility. Proper risk management is strongly recommended.

Hazel nut BB Strategy, volume base- lite version Hazel nut BB Strategy, volume base — lite version Having knowledge and information in financial markets is only useful when a trader operates with a well-defined trading strategy. Trading strategies assist in capital management, profit-taking, and reducing potential losses. This strategy is built upon the core principle of supply and demand dynamics. Alongside this foundation, one of the widely used technical tools — the Bollinger Bands — is employed to structure a framework for profit management and risk control. In this strategy, the interaction of these tools is explained in detail. A key point to note is that for calculating buy and sell volumes, a lower timeframe function is used. When applied with a tick-level resolution, this provides the most precise measurement of buyer/seller flows. However, this comes with a limitation of reduced historical depth. Users should be aware of this trade-off: if precise tick-level data is required, shorter timeframes should be considered to extend historical coverage . The strategy offers multiple configuration options. Nevertheless, it should be treated strictly as a supportive tool rather than a standalone trading system. Decisions must integrate personal analysis and other instruments. For example, in highly volatile assets with narrow ranges, it is recommended to adjust profit-taking and stop-loss percentages to smaller values. ◉ Volume Settings • Buyer and seller volume (up/down volume) are requested from a lower timeframe, with an option to override the automatic resolution. • A global lookback period is applied to calculate moving averages and cumulative sums of buy/sell/delta volumes. • Ratios of buyers/sellers to total volume are derived both on the current bar and across the lookback window. ◉ Bollinger Band • Bands are computed using configurable moving averages (SMA, EMA, RMA, WMA, VWMA). • Inputs allow control of length, standard deviation multiplier, and offset. • The basis, upper, and lower bands are plotted, with a shaded background between them. ◉ Progress & Proximity • Relative position of the price to the Bollinger basis is expressed as percentages (qPlus/qMinus). • “Near band” conditions are triggered when price progress toward the upper or lower band exceeds a user-defined threshold (%). • A signed score (sScore) represents how far the close has moved above or below the basis relative to band width. ◉ Info Table • Optional compact table summarizing: • - Upper/lower band margins • - Buyer/seller volumes with moving averages • - Delta and cumulative delta • - Buyer/seller ratios per bar and across the window • - Money flow values (buy/sell/delta × price) for bar-level and summed periods • The table is neutral-colored and resizable for different chart layouts. ◉ Zone Event Gate • Tracks entry into and exit from “near band” zones. • Arming logic: a side is armed when price enters a band proximity zone. • Trigger logic: on exit, a trade event is generated if cumulative buyer or seller volume dominates over a configurable window. ◉ Trading Logic • Orders are placed only on zone-exit events, conditional on volume dominance. • Position sizing is defined as a fixed percentage of strategy equity. • Long entries occur when leaving the lower zone with buyer dominance; short entries occur when leaving the upper zone with seller dominance. ◉ Exit Rules • Open positions are managed by a strict priority sequence: • 1. Stop-loss (% of entry price) • 2. Take-profit (% of entry price) • 3. Opposite-side event (zone exit with dominance in the other direction) • Stop-loss and take-profit levels are configurable ◉ Notes • This lite version is intended to demonstrate the interaction of Bollinger Bands and volume-based dominance logic. • It provides a framework to observe how price reacts at band boundaries under varying buy/sell pressure, and how zone exits can be systematically converted into entry/exit signals. When configuring this strategy, it is essential to carefully review the settings within the Strategy Tester. Ensure that the chosen parameters and historical data options are correctly aligned with the intended use. Accurate back testing depends on applying proper configurations for historical reference. The figure below illustrates sample result and configuration type.

Linear On MACD Unlocking the Magic of Linear Regression in TradingView In the ever-evolving world of financial markets, traders and investors seek effective tools to gauge price movements, make informed decisions, and achieve their financial goals. One such tool that has proven its worth over time is linear regression, a mathematical concept that has found its way into technical analysis and trading strategies. In this blog post, we will explore the magic behind linear regression, delve into its history, and understand how it's widely used as a technical indicator. The Birth of Linear Regression: From Mathematics to Trading Linear regression is a statistical method that aims to model the relationship between two variables by fitting a linear equation to observed data. The formula for a linear regression line is typically expressed as y = a + bx, where y is the dependent variable, x is the independent variable, a is the intercept, and b is the slope. While the roots of linear regression trace back to the field of statistics, it didn't take long for traders and investors to recognize its potential in the financial world. By applying linear regression to historical price data, traders can identify trends, assess the relationship between variables, and even predict potential future price levels. The Linear On MACD Strategy Let's take a closer look at a powerful example of how linear regression is employed in a trading strategy right within TradingView. The "Linear On MACD" strategy harnesses the potential of linear regression in conjunction with the Moving Average Convergence Divergence (MACD) indicator. The goal of this strategy is to generate buy and sell signals based on the interactions between the predicted stock price and the MACD indicator. Here's a breakdown of the strategy's components: Calculation of Linear Regression: The strategy begins by calculating linear regression coefficients for the historical stock price based on volume. This helps predict potential future price levels. Predicted Stock Price: The linear regression results are then used to plot the predicted stock price on the chart. This provides a visual representation of where the price could trend based on historical data. Buy and Sell Signals: The strategy generates buy signals when certain conditions are met. These conditions include the predicted stock price being between the open and close prices, a rising MACD, and other factors that suggest a potential bullish trend. On the other hand, sell signals are generated based on MACD trends and predicted price levels. Risk Management: The strategy also incorporates risk tolerance levels to determine entry and exit points. This ensures that traders take into account their risk appetite when making trading decisions. Embracing the Magic of Linear Regression As we explore the "Linear On MACD" strategy, we uncover the power of linear regression in aiding traders and investors. Linear regression, a mathematical marvel, seamlessly merges with technical analysis to provide insights into potential price movements. Its historical significance in statistics blends perfectly with the demands of modern financial markets. Whether you're a seasoned trader or a curious investor, the Linear On MACD strategy exemplifies how a robust mathematical concept can be harnessed to make informed trading decisions. By embracing the magic of linear regression, you're tapping into a tool that continues to evolve alongside the financial world it empowers. Disclaimer: The information provided in this blog post is for educational purposes only and does not constitute financial advice. Trading and investing carry risks, and it's important to conduct thorough research and consider seeking professional advice before making any trading decisions.