Trend Vector Pro v2.0Title: Trend Vector Pro v2.0
👨💻 Developed by: Mohammed Bedaiwi
💡 Strategy Overview & Coherence
Trend Vector Pro (TVPro) is a momentum-based trend & reversal strategy that uses a custom smoothed oscillator, an optional ADX filter, and classic Pivot Points to create a single, coherent trading framework.
Instead of stacking random indicators, TVPro is built around these integrated components:
A custom momentum engine (signal generation)
An optional ADX filter (trend quality control)
Daily Pivot Points (context, targets & S/R)
Swing-based “Golden Bar” trailing stops (trade management)
Optional extended bar detection (overextension alerts)
All parts are designed to work together and are documented below to address originality & usefulness requirements.
🔍 Core Components & Justification
1. Custom Momentum Engine (Main Signal Source)
TVPro’s engine is a custom oscillator derived from the bar midpoint ( hl2 ), similar in spirit to the Awesome Oscillator but adapted and fully integrated into the strategy. It measures velocity and acceleration of price, letting the script distinguish between strong impulses, weakening trends, and pure noise.
2. ADX Filter (Trend Strength Validation – Optional)
Uses Average Directional Index (ADX) as a gatekeeper.
Why this matters: This prevents the strategy from firing signals in choppy, non-trending environments (when ADX is below the threshold) and keeps trades focused on periods of clear directional strength.
3. Classic Pivot Points (Context & Targets)
Calculates Daily Pivot Points ( PP, R1-R3, S1-S3 ) via request.security() using prior session data.
Why this matters: Momentum gives the signal, ADX validates the environment, and Pivots add external structure for risk and target planning. This is a designed interaction, not a random mashup.
🧭 Trend State Logic (5-State Bar Coloring)
The strategy uses the momentum's value + slope to define five states, turning the chart into a visual momentum map:
🟢 STRONG BULL (Bright Green): Momentum accelerating UP. → Strong upside impulse.
🌲 WEAK BULL (Dark Green): Momentum decelerating DOWN (while positive). → Pullback/pause zone.
🔴 STRONG BEAR (Bright Red): Momentum accelerating DOWN. → Strong downside impulse.
🍷 WEAK BEAR (Dark Red): Momentum decelerating UP (while negative). → Rally/short-covering zone.
🔵 NEUTRAL / CHOP (Cyan): Momentum is near zero (based on noise threshold). → Consolidation / low volatility.
🎯 Signal Logic Modes
TVPro provides two selectable entry styles, controlled by input:
Reversals Only (Cleaner Mode – Default): Targets trend flips. Entry triggers when the current state is Bullish (or Bearish) and the previous state was not. This reduces noise and over-trading.
All Strong Pulses (Aggressive Mode): Targets acceleration phases. Entry triggers when the bar turns to STRONG BULL or STRONG BEAR after any other state. This mode produces more trades.
📌 Risk Management Tools
Golden Bars – Trailing Stops: Yellow “Trail” Arrows mark confirmed Swing Highs/Lows. These are used as logical trailing stop levels based on market structure.
Extended Bars: Detects when price closes outside a 2-standard-deviation channel, flagging overextension where a pullback is more likely.
Pivot Points: Used as external targets for Take Profit and structural stop placement.
⚙️ Strategy Defaults (Crucial for Publication Compliance)
To keep backtest results realistic and in line with House Rules, TVPro is published with the following fixed default settings:
Order Size: 5% of equity per trade ( default_qty_value = 5 )
Commission: 0.04% per order ( commission_value = 0.04 )
Slippage: 2 ticks ( slippage = 2 )
Initial Capital: 10,000
📘 How to Trade with Trend Vector Pro
Entry: Take Long when a Long signal appears and confirm the bar is Green (Bull state). Short for Red (Bear state).
Stop Loss: Place the initial SL near the latest swing High/Low, or near a relevant Pivot level.
Trade Management: Follow Golden (Trail) Arrows to trail your stop behind structure.
Exits: Exit when: the trailing stop is hit, Price reaches a major Pivot level, or an opposite signal prints.
🛑 Disclaimer
This script is for educational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Always forward-test and use proper risk management before applying any strategy to live trading.
Trendlineanalysis
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 versionHazel 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 MACDUnlocking 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.
