TrinityBar**TrinityBar Strategy Description**
The TrinityBar strategy is a price‐action based trading model that leverages Bill Williams’ bar thirds concept to generate entry signals and execute market orders automatically. Here’s how it works:
1. **Bar Thirds Calculation:**
The strategy calculates the range of both the current fully formed bar and the previous fully formed bar. It then divides each bar’s range into three equal parts (thirds).
- For the current bar, the lower third and upper third levels are computed.
- The same is done for the previous bar.
2. **Bar Type Classification:**
Each bar is classified into one of several types based on where its open and close fall relative to its thirds:
- **Bullish Patterns:**
- *1‑3 Bar:* Opens in the lower third and closes in the upper third.
- *2‑3 Bar:* Opens in the middle third and closes in the upper third.
- *3‑3 Bar:* Both open and close are in the upper third.
- **Bearish Patterns:**
- *3‑1 Bar:* Opens in the upper third and closes in the lower third.
- *2‑1 Bar:* Opens in the middle third and closes in the lower third.
- *1‑1 Bar:* Both open and close are in the lower third.
3. **Signal Generation:**
- **Bullish Signal:** A valid buy is generated when the previous bar exhibits any bullish pattern (1‑3, 2‑3, or 3‑3) and the current bar is either a 1‑3 or a 3‑3 bar.
- **Bearish Signal:** A valid sell is generated when the previous bar shows any bearish pattern (1‑1, 2‑1, or 3‑1) and the current bar is either a 1‑1 or a 3‑1 bar.
4. **Visual Alerts:**
When a valid signal is identified, the strategy plots a small triangle below the bar for a buy signal (labeled “B” in green) and a triangle above the bar for a sell signal (labeled “S” in red).
5. **Trade Execution:**
Once a signal is confirmed:
- If a bullish signal is generated, any short positions are closed, and if there is no existing long position, a market long order is entered.
- Conversely, if a bearish signal occurs, any long positions are closed, and a market short order is entered if not already in a short position.
This strategy is designed to capture significant price expansions by relying solely on price action and bar structure, without relying on lagging indicators. It provides a mechanical, systematic approach that removes emotional bias from trading decisions.
指標和策略
Iron Bot Statistical Trend Filter📌 Iron Bot Statistical Trend Filter
📌 Overview
Iron Bot Statistical Trend Filter is an advanced trend filtering strategy that combines statistical methods with technical analysis.
By leveraging Z-score and Fibonacci levels, this strategy quantitatively analyzes market trends to provide high-precision entry signals.
Additionally, it includes an optional EMA filter to enhance trend reliability.
Risk management is reinforced with Stop Loss (SL) and four Take Profit (TP) levels, ensuring a balanced approach to risk and reward.
📌 Key Features
🔹 1. Statistical Trend Filtering with Z-Score
This strategy calculates the Z-score to measure how much the price deviates from its historical mean.
Positive Z-score: Indicates a statistically high price, suggesting a strong uptrend.
Negative Z-score: Indicates a statistically low price, signaling a potential downtrend.
Z-score near zero: Suggests a ranging market with no strong trend.
By using the Z-score as a filter, market noise is reduced, leading to more reliable entry signals.
🔹 2. Fibonacci Levels for Trend Reversal Detection
The strategy integrates Fibonacci retracement levels to identify potential reversal points in the market.
High Trend Level (Fibo 23.6%): When the price surpasses this level, an uptrend is likely.
Low Trend Level (Fibo 78.6%): When the price falls below this level, a downtrend is expected.
Trend Line (Fibo 50%): Acts as a midpoint, helping to assess market balance.
This allows traders to visually confirm trend strength and turning points, improving entry accuracy.
🔹 3. EMA Filter for Trend Confirmation (Optional)
The strategy includes an optional 200 EMA (Exponential Moving Average) filter for trend validation.
Price above 200 EMA: Indicates a bullish trend (long entries preferred).
Price below 200 EMA: Indicates a bearish trend (short entries preferred).
Enabling this filter reduces false signals and improves trend-following accuracy.
🔹 4. Multi-Level Take Profit (TP) and Stop Loss (SL) Management
To ensure effective risk management, the strategy includes four Take Profit levels and a Stop Loss:
Stop Loss (SL): Automatically closes trades when the price moves against the position by a certain percentage.
TP1 (+0.75%): First profit-taking level.
TP2 (+1.1%): A higher probability profit target.
TP3 (+1.5%): Aiming for a stronger trend move.
TP4 (+2.0%): Maximum profit target.
This system secures profits at different stages and optimizes risk-reward balance.
🔹 5. Automated Long & Short Trading Logic
The strategy is built using Pine Script®’s strategy.entry() and strategy.exit(), allowing fully automated trading.
Long Entry:
Price is above the trend line & high trend level.
Z-score is positive (indicating an uptrend).
(Optional) Price is also above the EMA for stronger confirmation.
Short Entry:
Price is below the trend line & low trend level.
Z-score is negative (indicating a downtrend).
(Optional) Price is also below the EMA for stronger confirmation.
This logic helps filter out unnecessary trades and focus only on high-probability entries.
📌 Trading Parameters
This strategy is designed for flexible capital management and risk control.
💰 Account Size: $5000
📉 Commissions and Slippage: Assumes 94 pips commission per trade and 1 pip slippage.
⚖️ Risk per Trade: Adjustable, with a default setting of 1% of equity.
These parameters help preserve capital while optimizing the risk-reward balance.
📌 Visual Aids for Clarity
To enhance usability, the strategy includes clear visual elements for easy market analysis.
✅ Trend Line (Blue): Indicates market midpoint and helps with entry decisions.
✅ Fibonacci Levels (Yellow): Highlights high and low trend levels.
✅ EMA Line (Green, Optional): Confirms long-term trend direction.
✅ Entry Signals (Green for Long, Red for Short): Clearly marked buy and sell signals.
These features allow traders to quickly interpret market conditions, even without advanced technical analysis skills.
📌 Originality & Enhancements
This strategy is developed based on the IronXtreme and BigBeluga indicators,
combining a unique Z-score statistical method with Fibonacci trend analysis.
Compared to conventional trend-following strategies, it leverages statistical techniques
to provide higher-precision entry signals, reducing false trades and improving overall reliability.
📌 Summary
Iron Bot Statistical Trend Filter is a statistically-driven trend strategy that utilizes Z-score and Fibonacci levels.
High-precision trend analysis
Enhanced accuracy with an optional EMA filter
Optimized risk management with multiple TP & SL levels
Visually intuitive chart design
Fully customizable parameters & leverage support
This strategy reduces false signals and helps traders ride the trend with confidence.
Try it out and take your trading to the next level! 🚀
Stochastic-Dynamic Volatility Band ModelThe Stochastic-Dynamic Volatility Band Model is a quantitative trading approach that leverages statistical principles to model market volatility and generate buy and sell signals. The strategy is grounded in the concepts of volatility estimation and dynamic market regimes, where the core idea is to capture price fluctuations through stochastic models and trade around volatility bands.
Volatility Estimation and Band Construction
The volatility bands are constructed using a combination of historical price data and statistical measures, primarily the standard deviation (σ) of price returns, which quantifies the degree of variation in price movements over a specific period. This methodology is based on the classical works of Black-Scholes (1973), which laid the foundation for using volatility as a core component in financial models. Volatility is a crucial determinant of asset pricing and risk, and it plays a pivotal role in this strategy's design.
Entry and Exit Conditions
The entry conditions are based on the price’s relationship with the volatility bands. A long entry is triggered when the price crosses above the lower volatility band, indicating that the market may have been oversold or is experiencing a reversal to the upside. Conversely, a short entry is triggered when the price crosses below the upper volatility band, suggesting overbought conditions or a potential market downturn.
These entry signals are consistent with the mean reversion theory, which asserts that asset prices tend to revert to their long-term average after deviating from it. According to Poterba and Summers (1988), mean reversion occurs due to overreaction to news or temporary disturbances, leading to price corrections.
The exit condition is based on the number of bars that have elapsed since the entry signal. Specifically, positions are closed after a predefined number of bars, typically set to seven bars, reflecting a short-term trading horizon. This exit mechanism is in line with short-term momentum trading strategies discussed in literature, where traders capitalize on price movements within specific timeframes (Jegadeesh & Titman, 1993).
Market Adaptability
One of the key features of this strategy is its dynamic nature, as it adapts to the changing volatility environment. The volatility bands automatically adjust to market conditions, expanding in periods of high volatility and contracting when volatility decreases. This dynamic adjustment helps the strategy remain robust across different market regimes, as it is capable of identifying both trend-following and mean-reverting opportunities.
This dynamic adaptability is supported by the adaptive market hypothesis (Lo, 2004), which posits that market participants evolve their strategies in response to changing market conditions, akin to the adaptive nature of biological systems.
References:
Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
Bollinger, J. (1980). Bollinger on Bollinger Bands. Wiley.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Pure Price Action Breakout with 1:5 RR
Description of the Price Action Trading Script (Pine Script v6)
Overview
This script is a pure price action-based breakout strategy designed for TradingView. It identifies key breakout levels and executes long and short trades based on market structure. The strategy ensures a minimum risk-to-reward ratio (RR) of 1:5, aiming for high profitability with well-defined stop-loss and take-profit levels.
How the Script Works
1️⃣ Breakout Identification
The script uses a lookback period to find the highest high and lowest low over the last n bars.
A bullish breakout occurs when the price closes above the previous highest high.
A bearish breakout happens when the price closes below the previous lowest low.
2️⃣ Entry & Exit Strategy
Long Entry: If a bullish breakout is detected, the script enters a long position.
Short Entry: If a bearish breakout is detected, the script enters a short position.
The stop-loss is placed at the recent swing low (for long trades) or recent swing high (for short trades).
The target price is calculated based on a risk-to-reward ratio of 1:5, ensuring profitable trades.
3️⃣ Risk Management
The stop-loss prevents excessive losses by exiting trades when the market moves unfavorably.
The strategy ensures that each trade has a reward potential at least 5 times the risk.
Positions are executed based on price action only, without indicators like moving averages or RSI.
4️⃣ Visual Representation
The script plots breakout levels to help traders visualize potential trade setups.
Entry points, stop-loss, and take-profit levels are labeled on the chart for easy tracking.
Key Features & Benefits
✔ Pure Price Action – No lagging indicators, only real-time price movements.
✔ High Risk-to-Reward Ratio (1:5) – Ensures high-profit potential trades.
✔ Real-time Entry & Exit Signals – Provides accurate trade setups.
✔ Dynamic Stop-loss Calculation – Adjusts based on recent market structure.
✔ Customizable Parameters – Lookback periods and risk ratios can be modified.
ICT NY Kill Zone Auto Trading### **ICT NY Kill Zone Auto Trading Strategy (5-Min Chart)**
#### **Overview:**
This strategy is based on Inner Circle Trader (ICT) concepts, focusing on the **New York Kill Zone**. It is designed for trading GBP/USD exclusively on the **5-minute chart**, automatically entering and exiting trades during the US session.
#### **Key Components:**
1. **Time Filter**
- The strategy only operates during the **New York Kill Zone (9:30 AM - 11:00 AM NY Time)**.
- It ensures execution only on the **5-minute timeframe**.
2. **Fair Value Gaps (FVGs) Detection**
- The script identifies areas where price action left an imbalance, known as Fair Value Gaps (FVGs).
- These gaps indicate potential liquidity zones where price may return before continuing in the original direction.
3. **Order Blocks (OBs) Identification**
- **Bullish Order Block:** Occurs when price forms a strong bullish pattern, suggesting further upside movement.
- **Bearish Order Block:** Identified when a strong bearish formation signals potential downside continuation.
4. **Trade Execution**
- **Long Trade:** Entered when a bullish order block forms within the NY Kill Zone and aligns with an FVG.
- **Short Trade:** Entered when a bearish order block forms within the Kill Zone and aligns with an FVG.
5. **Risk Management**
- **Stop Loss:** Fixed at **30 pips** to limit downside risk.
- **Take Profit:** Set at **60 pips**, providing a **2:1 risk-reward ratio**.
6. **Visual Aids**
- The **Kill Zone is highlighted in blue** to help traders visually confirm the active session.
**Objective:**
This script aims to **capitalize on institutional price movements** within the New York session by leveraging ICT concepts such as FVGs and Order Blocks. By automating trade entries and exits, it eliminates emotions and ensures a disciplined trading approach.
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.
BTC Future Gamma-Weighted Momentum Model (BGMM)The BTC Future Gamma-Weighted Momentum Model (BGMM) is a quantitative trading strategy that utilizes the Gamma-weighted average price (GWAP) in conjunction with a momentum-based approach to predict price movements in the Bitcoin futures market. The model combines the concept of weighted price movements with trend identification, where the Gamma factor amplifies the weight assigned to recent prices. It leverages the idea that historical price trends and weighting mechanisms can be utilized to forecast future price behavior.
Theoretical Background:
1. Momentum in Financial Markets:
Momentum is a well-established concept in financial market theory, referring to the tendency of assets to continue moving in the same direction after initiating a trend. Any observed market return over a given time period is likely to continue in the same direction, a phenomenon known as the “momentum effect.” Deviations from a mean or trend provide potential trading opportunities, particularly in highly volatile assets like Bitcoin.
Numerous empirical studies have demonstrated that momentum strategies, based on price movements, especially those correlating long-term and short-term trends, can yield significant returns (Jegadeesh & Titman, 1993). Given Bitcoin’s volatile nature, it is an ideal candidate for momentum-based strategies.
2. Gamma-Weighted Price Strategies:
Gamma weighting is an advanced method of applying weights to price data, where past price movements are weighted by a Gamma factor. This weighting allows for the reinforcement or reduction of the influence of historical prices based on an exponential function. The Gamma factor (ranging from 0.5 to 1.5) controls how much emphasis is placed on recent data: a value closer to 1 applies an even weighting across periods, while a value closer to 0 diminishes the influence of past prices.
Gamma-based models are used in financial analysis and modeling to enhance a model’s adaptability to changing market dynamics. This weighting mechanism is particularly advantageous in volatile markets such as Bitcoin futures, as it facilitates quick adaptation to changing market conditions (Black-Scholes, 1973).
Strategy Mechanism:
The BTC Future Gamma-Weighted Momentum Model (BGMM) utilizes an adaptive weighting strategy, where the Bitcoin futures prices are weighted according to the Gamma factor to calculate the Gamma-Weighted Average Price (GWAP). The GWAP is derived as a weighted average of prices over a specific number of periods, with more weight assigned to recent periods. The calculated GWAP serves as a reference value, and trading decisions are based on whether the current market price is above or below this level.
1. Long Position Conditions:
A long position is initiated when the Bitcoin price is above the GWAP and a positive price movement is observed over the last three periods. This indicates that an upward trend is in place, and the market is likely to continue in the direction of the momentum.
2. Short Position Conditions:
A short position is initiated when the Bitcoin price is below the GWAP and a negative price movement is observed over the last three periods. This suggests that a downtrend is occurring, and a continuation of the negative price movement is expected.
Backtesting and Application to Bitcoin Futures:
The model has been tested exclusively on the Bitcoin futures market due to Bitcoin’s high volatility and strong trend behavior. These characteristics make the market particularly suitable for momentum strategies, as strong upward or downward movements are often followed by persistent trends that can be captured by a momentum-based approach.
Backtests of the BGMM on the Bitcoin futures market indicate that the model achieves above-average returns during periods of strong momentum, especially when the Gamma factor is optimized to suit the specific dynamics of the Bitcoin market. The high volatility of Bitcoin, combined with adaptive weighting, allows the model to respond quickly to price changes and maximize trading opportunities.
Scientific Citations and Sources:
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65–91.
• Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637–654.
• Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), 427–465.
Moving Average Crossover StrategyCertainly! Below is an example of a professional trading strategy implemented in Pine Script for TradingView. This strategy is a simple moving average crossover strategy, which is a common approach used by many traders. It uses two moving averages (a short-term and a long-term) to generate buy and sell signals.
Input Parameters:
shortLength: The length of the short-term moving average.
longLength: The length of the long-term moving average.
Moving Averages:
shortMA: The short-term simple moving average (SMA).
longMA: The long-term simple moving average (SMA).
Conditions:
longCondition: A buy signal is generated when the short-term MA crosses above the long-term MA.
shortCondition: A sell signal is generated when the short-term MA crosses below the long-term MA.
Trade Execution:
The strategy enters a long position when the longCondition is met.
The strategy enters a short position when the shortCondition is met.
Plotting:
The moving averages are plotted on the chart.
Buy and sell signals are plotted as labels on the chart.
How to Use:
Copy the script into TradingView's Pine Script editor.
Adjust the shortLength and longLength parameters to fit your trading style.
Add the script to your chart and apply it to your desired timeframe.
Backtest the strategy to see how it performs on historical data.
This is a basic example, and professional traders often enhance such strategies with additional filters, risk management rules, and other indicators to improve performance.
Volatility Arbitrage Spread Oscillator Model (VASOM)The Volatility Arbitrage Spread Oscillator Model (VASOM) is a systematic approach to capitalizing on price inefficiencies in the VIX futures term structure. By analyzing the differential between front-month and second-month VIX futures contracts, we employ a momentum-based oscillator (Relative Strength Index, RSI) to signal potential market reversion opportunities. Our research builds upon existing financial literature on volatility risk premia and contango/backwardation dynamics in the volatility markets (Zhang & Zhu, 2006; Alexander & Korovilas, 2012).
Volatility derivatives have become essential tools for managing risk and engaging in speculative trades (Whaley, 2009). The Chicago Board Options Exchange (CBOE) Volatility Index (VIX) measures the market’s expectation of 30-day forward-looking volatility derived from S&P 500 option prices (CBOE, 2018). Term structures in VIX futures often exhibit contango or backwardation, depending on macroeconomic and market conditions (Alexander & Korovilas, 2012).
This strategy seeks to exploit the spread between the front-month and second-month VIX futures as a proxy for term structure dynamics. The spread’s momentum, quantified by the RSI, serves as a signal for entry and exit points, aligning with empirical findings on mean reversion in volatility markets (Zhang & Zhu, 2006).
• Entry Signal: When RSI_t falls below the user-defined threshold (e.g., 30), indicating a potential undervaluation in the spread.
• Exit Signal: When RSI_t exceeds a threshold (e.g., 70), suggesting mean reversion has occurred.
Empirical Justification
The strategy aligns with findings that suggest predictable patterns in volatility futures spreads (Alexander & Korovilas, 2012). Furthermore, the use of RSI leverages insights from momentum-based trading models, which have demonstrated efficacy in various asset classes, including commodities and derivatives (Jegadeesh & Titman, 1993).
References
• Alexander, C., & Korovilas, D. (2012). The Hazards of Volatility Investing. Journal of Alternative Investments, 15(2), 92-104.
• CBOE. (2018). The VIX White Paper. Chicago Board Options Exchange.
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
• Zhang, C., & Zhu, Y. (2006). Exploiting Predictability in Volatility Futures Spreads. Financial Analysts Journal, 62(6), 62-72.
• Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.
Bitcoin Liquidity Breakout with ICT StrategiesBitcoin Liquidity Breakout with ICT Strategies
a one of many scripts developed by our engineers .
Check the results for yourself
Bollinger Bounce Reversal Strategy – Visual EditionOverview:
The Bollinger Bounce Reversal Strategy – Visual Edition is designed to capture potential reversal moves at price extremes—often termed “bounce points”—by using a combination of technical indicators. The strategy integrates Bollinger Bands, MACD, and volume analysis, and it provides rich on‑chart visual cues to help traders understand its signals and conditions. Additionally, the strategy enforces a maximum of 5 trades per day and uses fixed risk management parameters. This publication is intended for educational purposes and offers a systematic, transparent approach that you can further adjust to fit your market or risk profile.
How It Works:
Bollinger Bands:
A 20‑period simple moving average (SMA) and a user‑defined standard deviation multiplier (default 2.0) are used to calculate the Bollinger Bands.
When the price reaches or crosses these bands (i.e. falls below the lower band or rises above the upper band), it suggests that the price is in an extreme, potentially oversold or overbought, state.
MACD Filter:
The MACD (calculated with standard lengths, e.g. 12, 26, 9) provides momentum information.
For a bullish (long) signal, the MACD line should be above its signal line; for a bearish (short) signal, the MACD line should be below.
Volume Confirmation:
The strategy uses a 20‑period volume moving average to determine if current volume is strong enough to validate a signal.
A signal is confirmed only if the current volume is at or above a specified multiple (by default, 1.0×) of this moving average, ensuring that the move is supported by increased market participation.
Visual Cues:
Bollinger Bands and Fill: The basis (SMA), upper, and lower Bollinger Bands are plotted, and the area between the upper and lower bands is filled with a semi‑transparent color.
Signal Markers: When a long or short signal is generated, corresponding markers (labels) appear on the chart.
Background Coloring: The chart’s background changes color (green for long signals and red for short signals) on the bars where signals occur.
Information Table: An on‑chart table displays key indicator values (MACD, signal line, volume, average volume) and the number of trades executed that day.
Entry Conditions:
Long Entry:
A long trade is triggered when the previous bar’s close is below the lower Bollinger Band and the current bar’s close crosses above it, combined with a bullish MACD condition and strong volume.
Short Entry:
A short trade is triggered when the previous bar’s close is above the upper Bollinger Band and the current bar’s close crosses below it, with a bearish MACD condition and high volume.
Risk Management:
Daily Trade Limit: The strategy restricts trading to no more than 5 trades per day.
Stop-Loss and Take-Profit:
For each position, a stop loss is set at a fixed percentage away from the entry price (typically 2%), and a take profit is set to target a 1:2 risk-reward ratio (typically 4% from the entry price).
Backtesting Setup:
Initial Capital: $10,000
Commission: 0.1% per trade
Slippage: 1 tick per bar
These realistic parameters help ensure that backtesting results reflect the conditions of an average trader.
Disclaimer:
Past performance is not indicative of future results. This strategy is experimental and provided solely for educational purposes. It is essential to backtest extensively and paper trade before any live deployment. All risk management practices are advisory, and you should adjust parameters to suit your own trading style and risk tolerance.
Conclusion:
By combining Bollinger Bands, MACD, and volume analysis, the Bollinger Bounce Reversal Strategy – Visual Edition provides a clear, systematic method to identify potential reversal opportunities at price extremes. The added visual cues help traders quickly interpret signals and assess market conditions, while strict risk management and a daily trade cap help keep trading disciplined. Adjust and refine the settings as needed to better suit your specific market and risk profile.
The 950 Bar StrategyNQ 9:50 AM Candle Strategy v3 (Trade at 9:55AM) - 1 Contract
Also called the 950 Standard. The 950 Strategy.
This strategy places its trade at 9:55am each day based on the close of the 9:50am candle. Uses 5min timeframe candles. If candle closes red, or bearish, the strategy goes short. If candle closes green, or bullish, the strategy goes long. Brackets are 150tick TP and 200tick SL.
3x Supertrend (for Vietnamese stock market and vn30f1m)The 4Vietnamese 3x Supertrend Strategy is an advanced trend-following trading system developed in Pine Script™ and designed for publication on TradingView as an open-source strategy under the Mozilla Public License 2.0. This strategy leverages three Supertrend indicators with different ATR lengths and multipliers to identify optimal trade entries and exits while dynamically managing risk.
Key Features:
Option to build and hold long term positions with entry stop order. Try this to avoid market complex movement and retain long term investment style's benefits.
Advanced Entry & Exit Optimization: Includes configurable stop-loss mechanisms, pyramiding, and exit conditions tailored for different market scenarios.
Dynamic Risk Management: Implements features like selective stop-loss activation, trade window settings, and closing conditions based on trend reversals and loss management.
This strategy is particularly suited for traders seeking a systematic and rule-based approach to trend trading. By making it open-source, we aim to provide transparency, encourage community collaboration, and help traders refine and optimize their strategies for better performance.
License:
This script is released under the Mozilla Public License 2.0, allowing modifications and redistribution while maintaining open-source integrity.
Happy trading!
Ultimate T3 Fibonacci for BTC Scalping. Look at backtest report!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto trading! This strategy is for BITCOIN on the 30 minute chart since I designed it to be a scalping strategy. I calculated for trading fees, and use a small amount of capital in the backtest report. But feel free to modify the capital and how much per order to see how it changes the results:)
It is called the "Ultimate T3 Fibonacci Indicator by NHBprod" that computes and displays two T3-based moving averages derived from price data. The t3_function calculates the Tilson T3 indicator by applying a series of exponential moving averages to a combined price metric and then blending these results with specific coefficients derived from an input factor.
The script accepts several user inputs that toggle the use of the T3 filter, select the buy signal method, and set parameters like lengths and volume factors for two variations of the T3 calculation. Two T3 lines, T3 and T32, are computed with different parameters, and their colors change dynamically (green/red for T3 and blue/purple for T32) based on whether the lines are trending upward or downward. Depending on the selected signal method, the script generates buy signals either when T32 crosses over T3 or when the closing price is above T3, and similarly, sell signals are generated on the respective conditions for crossing under or closing below. Finally, the indicator plots the T3 lines on the chart, adds visual buy/sell markers, and sets alert conditions to notify users when the respective trading signals occur.
The user has the ability to tune the parameters using TP/SL, date timerames for analyses, and the actual parameters of the T3 function including the buy/sell signal! Lastly, the user has the option of trading this long, short, or both!
Let me know your thoughts and check out the backtest report!
Volatility Momentum Breakout StrategyDescription:
Overview:
The Volatility Momentum Breakout Strategy is designed to capture significant price moves by combining a volatility breakout approach with trend and momentum filters. This strategy dynamically calculates breakout levels based on market volatility and uses these levels along with trend and momentum conditions to identify trade opportunities.
How It Works:
1. Volatility Breakout:
• Methodology:
The strategy computes the highest high and lowest low over a defined lookback period (excluding the current bar to avoid look-ahead bias). A multiple of the Average True Range (ATR) is then added to (or subtracted from) these levels to form dynamic breakout thresholds.
• Purpose:
This method helps capture significant price movements (breakouts) while ensuring that only past data is used, thereby maintaining realistic signal generation.
2. Trend Filtering:
• Methodology:
A short-term Exponential Moving Average (EMA) is applied to determine the prevailing trend.
• Purpose:
Long trades are considered only when the current price is above the EMA, indicating an uptrend, while short trades are taken only when the price is below the EMA, indicating a downtrend.
3. Momentum Confirmation:
• Methodology:
The Relative Strength Index (RSI) is used to gauge market momentum.
• Purpose:
For long entries, the RSI must be above a mid-level (e.g., above 50) to confirm upward momentum, and for short entries, it must be below a similar threshold. This helps filter out signals during overextended conditions.
Entry Conditions:
• Long Entry:
A long position is triggered when the current closing price exceeds the calculated long breakout level, the price is above the short-term EMA, and the RSI confirms momentum (e.g., above 50).
• Short Entry:
A short position is triggered when the closing price falls below the calculated short breakout level, the price is below the EMA, and the RSI confirms momentum (e.g., below 50).
Risk Management:
• Position Sizing:
Trades are sized to risk a fixed percentage of account equity (set here to 5% per trade in the code, with each trade’s stop loss defined so that risk is limited to approximately 2% of the entry price).
• Stop Loss & Take Profit:
A stop loss is placed a fixed ATR multiple away from the entry price, and a take profit target is set to achieve a 1:2 risk-reward ratio.
• Realistic Backtesting:
The strategy is backtested using an initial capital of $10,000, with a commission of 0.1% per trade and slippage of 1 tick per bar—parameters chosen to reflect conditions faced by the average trader.
Important Disclaimers:
• No Look-Ahead Bias:
All breakout levels are calculated using only past data (excluding the current bar) to ensure that the strategy does not “peek” into future data.
• Educational Purpose:
This strategy is experimental and provided solely for educational purposes. Past performance is not indicative of future results.
• User Responsibility:
Traders should thoroughly backtest and paper trade the strategy under various market conditions and adjust parameters to fit their own risk tolerance and trading style before live deployment.
Conclusion:
By integrating volatility-based breakout signals with trend and momentum filters, the Volatility Momentum Breakout Strategy offers a unique method to capture significant price moves in a disciplined manner. This publication provides a transparent explanation of the strategy’s components and realistic backtesting parameters, making it a useful tool for educational purposes and further customization by the TradingView community.
Advanced Multi-Timeframe Trading System (Risk Managed)Description:
This strategy is an original approach that combines two main analytical components to identify potential trade opportunities while simulating realistic trading conditions:
1. Market Trend Analysis via an Approximate Hurst Exponent
• What It Does:
The strategy computes a rough measure of market trending using an approximate Hurst exponent. A value above 0.5 suggests persistent, trending behavior, while a value below 0.5 indicates a tendency toward mean-reversion.
• How It’s Used:
The Hurst exponent is calculated on both the chart’s current timeframe and a higher timeframe (default: Daily) to capture both local and broader market dynamics.
2. Fibonacci Retracement Levels
• What It Does:
Using daily high and low data from a selected timeframe (default: Daily), the script computes key Fibonacci retracement levels.
• How It’s Used:
• The 61.8% level (Golden Ratio) serves as a key threshold:
• A long entry is signaled when the price crosses above this level if the daily Hurst exponent confirms a trending market.
• The 38.2% level is used to identify short-entry opportunities when the price crosses below it and the daily Hurst indicates non-trending conditions.
Signal Logic:
• Long Entry:
When the price crosses above the 61.8% Fibonacci level (Golden Ratio) and the daily Hurst exponent is greater than 0.5, suggesting a trending market.
• Short Entry:
When the price crosses below the 38.2% Fibonacci level and the daily Hurst exponent is less than 0.5, indicating a less trending or potentially reversing market.
Risk Management & Trade Execution:
• Stop-Loss:
Each trade is risk-managed with a stop-loss set at 2% below (for longs) or above (for shorts) the entry price. This ensures that no single trade risks more than a small, sustainable portion of the account.
• Take Profit:
A take profit order targets a risk-reward ratio of 1:2 (i.e., the target profit is twice the amount risked).
• Position Sizing:
Trades are executed with a fixed position size equal to 10% of account equity.
• Trade Frequency Limits:
• Daily Limit: A maximum of 5 trades per day
• Overall Limit: No more than 510 trades during the backtesting period (e.g., since 2019)
These limits are imposed to simulate realistic trading frequency and to avoid overtrading in backtest results.
Backtesting Parameters:
• Initial Capital: $10,000
• Commission: 0.1% per trade
• Slippage: 1 tick per bar
These settings aim to reflect the conditions faced by the average trader and help ensure that the backtesting results are realistic and not misleading.
Chart Overlays & Visual Aids:
• Fibonacci Levels:
The key Fibonacci retracement levels are plotted on the chart, and the zone between the 61.8% and 38.2% levels is highlighted to show a key retracement area.
• Market Trend Background:
The chart background is tinted green when the daily Hurst exponent indicates a trending market (value > 0.5) and red otherwise.
• Information Table:
An on-chart table displays key parameters such as the current Hurst exponent, daily Hurst value, the number of trades executed today, and the global trade count.
Disclaimer:
Past performance is not indicative of future results. This strategy is experimental and provided solely for educational purposes. It is essential that you backtest and paper trade using your own settings before considering any live deployment. The Hurst exponent calculation is an approximation and should be interpreted as a rough gauge of market behavior. Adjust the parameters and risk management settings according to your personal risk tolerance and market conditions.
Additional Notes:
• Originality & Usefulness:
This script is an original mashup that combines trend analysis with Fibonacci retracement methods. The description above explains how these components work together to provide trading signals.
• Realistic Results:
The strategy uses realistic account sizes, commission rates, slippage, and risk management rules to generate backtesting results that are representative of real-world trading.
• Educational Purpose:
This script is intended to support the TradingView community by offering insights into combining multiple analysis techniques in one strategy. It is not a “get-rich-quick” system but rather an educational tool to help traders understand risk management and trade signal logic.
By using this script, you acknowledge that trading involves risk and that you are responsible for testing and adjusting the strategy to fit your own trading environment. This publication is fully open source, and any modifications should include proper attribution if significant portions of the code are reused.
Bollinger Bands Long Strategy
This strategy is designed for identifying and executing long trades based on Bollinger Bands and RSI. It aims to capitalize on potential oversold conditions and subsequent price recovery.
Key Features:
- Bollinger Bands (10,2): The strategy uses Bollinger Bands with a 10-period moving average and a multiplier of 2 to define price volatility.
- RSI Filter: A trade is only triggered when the RSI (14-period) is below 30, ensuring entry during oversold conditions.
- Entry Condition: A long trade is entered immediately when the price crosses below the lower Bollinger Band and the RSI is under 30.
- Exit Condition: The position is exited when the price reaches or crosses above the Bollinger Band basis (20-period moving average).
Best Used For:
- Identifying oversold conditions with a strong potential for a rebound.
- Markets or assets with clear oscillations and volatility e.g., BTC.
**Disclaimer:** This strategy is for educational purposes and should be used with caution. Backtesting and risk management are essential before live trading.
Arpeet MACDOverview
This strategy is based on the zero-lag version of the MACD (Moving Average Convergence Divergence) indicator, which captures short-term trends by quickly responding to price changes, enabling high-frequency trading. The strategy uses two moving averages with different periods (fast and slow lines) to construct the MACD indicator and introduces a zero-lag algorithm to eliminate the delay between the indicator and the price, improving the timeliness of signals. Additionally, the crossover of the signal line and the MACD line is used as buy and sell signals, and alerts are set up to help traders seize trading opportunities in a timely manner.
Strategy Principle
Calculate the EMA (Exponential Moving Average) or SMA (Simple Moving Average) of the fast line (default 12 periods) and slow line (default 26 periods).
Use the zero-lag algorithm to double-smooth the fast and slow lines, eliminating the delay between the indicator and the price.
The MACD line is formed by the difference between the zero-lag fast line and the zero-lag slow line.
The signal line is formed by the EMA (default 9 periods) or SMA of the MACD line.
The MACD histogram is formed by the difference between the MACD line and the signal line, with blue representing positive values and red representing negative values.
When the MACD line crosses the signal line from below and the crossover point is below the zero axis, a buy signal (blue dot) is generated.
When the MACD line crosses the signal line from above and the crossover point is above the zero axis, a sell signal (red dot) is generated.
The strategy automatically places orders based on the buy and sell signals and triggers corresponding alerts.
Advantage Analysis
The zero-lag algorithm effectively eliminates the delay between the indicator and the price, improving the timeliness and accuracy of signals.
The design of dual moving averages can better capture market trends and adapt to different market environments.
The MACD histogram intuitively reflects the comparison of bullish and bearish forces, assisting in trading decisions.
The automatic order placement and alert functions make it convenient for traders to seize trading opportunities in a timely manner, improving trading efficiency.
Risk Analysis
In volatile markets, frequent crossover signals may lead to overtrading and losses.
Improper parameter settings may cause signal distortion and affect strategy performance.
The strategy relies on historical data for calculations and has poor adaptability to sudden events and black swan events.
Optimization Direction
Introduce trend confirmation indicators, such as ADX, to filter out false signals in volatile markets.
Optimize parameters to find the best combination of fast and slow line periods and signal line periods, improving strategy stability.
Combine other technical indicators or fundamental factors to construct a multi-factor model, improving risk-adjusted returns of the strategy.
Introduce stop-loss and take-profit mechanisms to control single-trade risk.
Summary
The MACD Dual Crossover Zero Lag Trading Strategy achieves high-frequency trading by quickly responding to price changes and capturing short-term trends. The zero-lag algorithm and dual moving average design improve the timeliness and accuracy of signals. The strategy has certain advantages, such as intuitive signals and convenient operation, but also faces risks such as overtrading and parameter sensitivity. In the future, the strategy can be optimized by introducing trend confirmation indicators, parameter optimization, multi-factor models, etc., to improve the robustness and profitability of the strategy.
GOLD Volume-Based Entry StrategyShort Description:
This script identifies potential long entries by detecting two consecutive bars with above-average volume and bullish price action. When these conditions are met, a trade is entered, and an optional profit target is set based on user input. This strategy can help highlight momentum-driven breakouts or trend continuations triggered by a surge in buying volume.
How It Works
Volume Moving Average
A simple moving average of volume (vol_ma) is calculated over a user-defined period (default: 20 bars). This helps us distinguish when volume is above or below recent averages.
Consecutive Green Volume Bars
First bar: Must be bullish (close > open) and have volume above the volume MA.
Second bar: Must also be bullish, with volume above the volume MA and higher than the first bar’s volume.
When these two bars appear in sequence, we interpret it as strong buying pressure that could drive price higher.
Entry & Profit Target
Upon detecting these two consecutive bullish bars, the script places a long entry.
A profit target is set at current price plus a user-defined fixed amount (default: 5 USD).
You can adjust this target, or you can add a stop-loss in the script to manage risk further.
Visual Cues
Buy Signal Marker appears on the chart when the second bar confirms the signal.
Green Volume Columns highlight the bars that fulfill the criteria, providing a quick visual confirmation of high-volume bullish bars.
Works fine on 1M-2M-5M-15M-30M. Do not use it on higher TF. Due the lack of historical data on lower TF, the backtest result is limited.
Tick Marubozu StrategyStrategy Concept:
This strategy identifies Marubozu candles on a tick chart (customizable pip size) with high volume to signal strong market momentum.
Bearish Marubozu → Strong selling pressure → Enter a SELL trade
Bullish Marubozu → Strong buying pressure → Enter a BUY trade
Entry Conditions:
Marubozu Definition:
Open price ≈ High for a bearish Marubozu (minimal wick at the top).
Open price ≈ Low for a bullish Marubozu (minimal wick at the bottom).
Customizable body size (in pips).
High Volume Confirmation:
The volume of the Marubozu candle must be above the moving average of volume (e.g., 20-period SMA).
Trade Direction:
Bearish Marubozu with High Volume → SELL
Bullish Marubozu with High Volume → BUY
Exit Conditions:
Time-Based Expiry: Since it's for binary options, the trade duration is pre-defined (e.g., 1-minute expiry).
Reversal Candle: If a strong opposite Marubozu appears, it may indicate a trend shift.
Candle Range Theory StrategyCandle Range Theory StrategyCandle Range Theory Strategy delves into the intricacies of price action analysis, focusing on the behavior of candlestick patterns within specific ranges. Traders employing this strategy aim to identify key support and resistance levels by analyzing the high and low points of significant candlesticks. The core principle lies in understanding that the range of a candle—defined by its opening, closing, high, and low prices—provides valuable insight into market sentiment and potential future movements.
To implement the Candle Range Theory Strategy effectively, one must first recognize the importance of different candle sizes. A long-bodied candle suggests strong momentum, pointing to a bullish or bearish bias, while a small-bodied candle indicates indecision or consolidation, often signaling potential reversals or breakouts. By plotting these candlesticks over a defined time frame, traders can ascertain whether the market is trending or range-bound.
Additionally, traders should consider the context in which these candles form. Analysis of the preceding price action can reveal whether current ranges are extensions of existing trends or indications of market fatigue. In particular, look for patterns such as engulfing candles, pin bars, or inside bars, as they often foreshadow forthcoming price fluctuations.
Moreover, combining the Candle Range Theory with other technical indicators, like moving averages or Fibonacci retracements, can offer a more comprehensive view of potential entry and exit points. By aligning candle patterns with broader market dynamics, traders can optimize their strategies, enhancing their probability of success while minimizing risk.
Lastly, maintaining a disciplined approach is crucial. Setting precise stop-loss and take-profit levels grounded in candle ranges can safeguard one's capital. Adhering to this framework allows traders to navigate the complexities of the market with greater confidence, ultimately leading to more informed and successful trading decisions. Embracing the nuances of Candle Range Theory not only sharpens analytical skills but also enriches one’s trading repertoire, paving the way for sustained profitability in the dynamic world of forex and equities.
Classic Nacked Z-Score ArbitrageThe “Classic Naked Z-Score Arbitrage” strategy employs a statistical arbitrage model based on the Z-score of the price spread between two assets. This strategy follows the premise of pair trading, where two correlated assets, typically from the same market sector, are traded against each other to profit from relative price movements (Gatev, Goetzmann, & Rouwenhorst, 2006). The approach involves calculating the Z-score of the price spread between two assets to determine market inefficiencies and capitalize on short-term mispricing.
Methodology
Price Spread Calculation:
The strategy calculates the spread between the two selected assets (Asset A and Asset B), typically from different sectors or asset classes, on a daily timeframe.
Statistical Basis – Z-Score:
The Z-score is used as a measure of how far the current price spread deviates from its historical mean, using the standard deviation for normalization.
Trading Logic:
• Long Position:
A long position is initiated when the Z-score exceeds the predefined threshold (e.g., 2.0), indicating that Asset A is undervalued relative to Asset B. This signals an arbitrage opportunity where the trader buys Asset B and sells Asset A.
• Short Position:
A short position is entered when the Z-score falls below the negative threshold, indicating that Asset A is overvalued relative to Asset B. The strategy involves selling Asset B and buying Asset A.
Theoretical Foundation
This strategy is rooted in mean reversion theory, which posits that asset prices tend to return to their long-term average after temporary deviations. This form of arbitrage is widely used in statistical arbitrage and pair trading techniques, where investors seek to exploit short-term price inefficiencies between two assets that historically maintain a stable price relationship (Avery & Sibley, 2020).
Further, the Z-score is an effective tool for identifying significant deviations from the mean, which can be seen as a signal for the potential reversion of the price spread (Braucher, 2015). By capturing these inefficiencies, traders aim to profit from convergence or divergence between correlated assets.
Practical Application
The strategy aligns with the Financial Algorithmic Trading and Market Liquidity analysis, emphasizing the importance of statistical models and efficient execution (Harris, 2024). By utilizing a simple yet effective risk-reward mechanism based on the Z-score, the strategy contributes to the growing body of research on market liquidity, asset correlation, and algorithmic trading.
The integration of transaction costs and slippage ensures that the strategy accounts for practical trading limitations, helping to refine execution in real market conditions. These factors are vital in modern quantitative finance, where liquidity and execution risk can erode profits (Harris, 2024).
References
• Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs Trading: Performance of a Relative-Value Arbitrage Rule. The Review of Financial Studies, 19(3), 1317-1343.
• Avery, C., & Sibley, D. (2020). Statistical Arbitrage: The Evolution and Practices of Quantitative Trading. Journal of Quantitative Finance, 18(5), 501-523.
• Braucher, J. (2015). Understanding the Z-Score in Trading. Journal of Financial Markets, 12(4), 225-239.
• Harris, L. (2024). Financial Algorithmic Trading and Market Liquidity: A Comprehensive Analysis. Journal of Financial Engineering, 7(1), 18-34.
John Bob-Trading-BotDeveloped by Ayebale John Bob with the help of his bestie, this innovative strategy combines advanced Smart Money Concepts with practical risk management tools to help traders identify and capitalize on key market moves.
Key Features:
Smart Money Concepts & Fair Value Gaps (FVG):
The strategy monitors price action for fair value gaps, which are visualized as extremely faint horizontal lines on the chart. These FVGs signal potential areas where institutional traders might have entered or exited positions.
Dynamic Entry Signals:
Buy signals are triggered when the price crosses above the 50-bar lowest low or when a bullish FVG is detected. Conversely, sell signals are generated when the price falls below the 50-bar highest high or a bearish FVG is identified. Each signal is visually marked on the chart with clear buy (green) and sell (red) labels.
Multi-Level Order Execution:
Once an entry signal occurs, the strategy places five separate orders, each with its own take-profit (TP) level. The TP levels are calculated dynamically using the Average True Range (ATR) and a set of predefined multipliers. This allows traders to scale out of positions as the market moves favorably.
Dynamic Risk Management:
A stop-loss is automatically set at a distance determined by the ATR, ensuring that risk is managed in accordance with current market volatility.
Real-Time Trade Information Table:
In the bottom-right corner of the chart, a trade information table displays essential details about the current trade:
Side: Displays "BUY NOW" (with a dark green background) for long entries or "SELL NOW" (with a dark red background) for short entries.
Entry Price & Stop-Loss: Shows the entry price (highlighted in green) and the corresponding stop-loss level (highlighted in red).
Take-Profit Levels: Lists the five TP levels, each of which turns green once the market price reaches that target.
Timer: A live timer in minutes counts from the moment the current trade trigger started, helping traders track the duration of their active trades.
Visual Progress Bar:
A histogram-style progress bar is plotted on the chart, visually representing the percentage gain (or loss) relative to the entry price.
This strategy was meticulously designed to incorporate both technical analysis and smart risk management, offering a robust trading solution that adapts to changing market conditions. Whether you're a seasoned trader or just starting out, the AyebaleJohnBob Trading Bot equips you with the tools and visual cues needed to make well-informed trading decisions. Enjoy a seamless blend of strategy and style—crafted with passion by Ayebale John Bob and his bestie!
