Bollinger Band Touch with SMI and MACD AngleThis strategy is intended for short timeframes to enter and exit when price touches lower and upper bollinger bands with confluence on RSI and MACD
Bande e canali
Mean Reversion V-FThis strategy workings on high volatile stock or crypto assets
It using 5 dynamic band's to get in the long position.
In same time depends on the band increases the units of the asset to get in the next position.
The unit's of the asset can be adjusted. Make sure to adjust the unit for different asset.
The bands are determined of main SMA.
There is no stop loss.
Take profit is trialing - HMA or % or average price + take profit - note if you use % trailing back test is not realistic but is working on real time.
Deviations can be adjust depends on the asset volatility.
IU EMA Channel StrategyIU EMA Channel Strategy
Overview:
The IU EMA Channel Strategy is a simple yet effective trend-following strategy that uses two Exponential Moving Averages (EMAs) based on the high and low prices. It provides clear entry and exit signals by identifying price crossovers relative to the EMAs while incorporating a built-in Risk-to-Reward Ratio (RTR) for effective risk management.
Inputs ( Settings ):
- RTR (Risk-to-Reward Ratio): Define the ratio for risk-to-reward (default = 2).
- EMA Length: Adjust the length of the EMA channels (default = 100).
How the Strategy Works
1. EMA Channels:
- High-based EMA: EMA calculated on the high price.
- Low-based EMA: EMA calculated on the low price.
The area between these two EMAs creates a "channel" that visually highlights potential support and resistance zones.
2. Entry Rules:
- Long Entry: When the price closes above the high-based EMA (crossover).
- Short Entry: When the price closes below the low-based EMA (crossunder).
These entries ensure trades are taken in the direction of momentum.
3. Stop Loss (SL) and Take Profit (TP):
- Stop Loss:
- For long positions, the SL is set at the previous bar's low.
- For short positions, the SL is set at the previous bar's high.
- Take Profit:
- TP is automatically calculated using the Risk-to-Reward Ratio (RTR) you define.
- Example: If RTR = 2, the TP will be 2x the risk distance.
4. Exit Rules:
- Positions are closed at either the stop loss or the take profit level.
- The strategy manages exits automatically to enforce disciplined risk management.
Visual Features
1. EMA Channels:
- The high and low EMAs are dynamically color-coded:
- Green: Price is above the EMA (bullish condition).
- Red: Price is below the EMA (bearish condition).
- The area between the EMAs is shaded for better visual clarity.
2. Stop Loss and Take Profit Zones:
- SL and TP levels are plotted for both long and short positions.
- Zones are filled with:
- Red: Stop Loss area.
- Green: Take Profit area.
Be sure to manage your risk and position size properly.
Custom Strategy: ETH Martingale 2.0Strategic characteristics
ETH Little Martin 2.0 is a self-developed trading strategy based on the Martingale strategy, mainly used for trading ETH (Ethereum). The core idea of this strategy is to place orders in the same direction at a fixed price interval, and then use Martin's multiple investment principle to reduce losses, but this is also the main source of losses.
Parameter description:
1 Interval： The minimum spacing for taking profit, stop loss, and opening/closing of orders. Different targets have different spacing. Taking ETH as an example, it is generally recommended to have a spacing of 2% for fluctuations in the target.
2 Base Price： This is the price at which you triggered the first order. Similarly, I am using ETH as an example. If you have other targets, I suggest using the initial value of a price that can be backtesting. The Base Price is only an initial order price and has no impact on subsequent orders.
3 Initial Order Amount： Users can set an initial order amount to control the risk of each transaction. If the stop loss is reached, we will double the amount based on this value. This refers to the value of the position held, not the number of positions held.
4 Loss Multiplier： The strategy will increase the next order amount based on the set multiple after the stop loss, in order to make up for the previous losses through a larger position. Note that after taking profit, it will be reset to 1 times the Initial Order Amount.
5. Long Short Operation: The first order of the strategy is a multiple entry, and in subsequent orders, if the stop loss is reached, a reverse order will be opened. The position value of a one-way order is based on the Loss Multiplier multiple investment, so it is generally recommended that the Loss Multiplier default to 2.
Improvement direction
Although this strategy already has a certain trading logic, there are still some improvement directions that can be considered:
1. Dynamic adjustment of spacing: Currently, the spacing is fixed, and it can be considered to dynamically adjust the spacing based on market volatility to improve the adaptability of the strategy. Try using dynamic spacing, which may be more suitable for the actual market situation.
2. Filtering criteria: Orders and no orders can be optimized separately. The biggest problem with this strategy is that it will result in continuous losses during fluctuations, and eventually increase the investment amount. You can consider filtering out some fluctuations or only focusing on trend trends.
3. Risk management: Add more risk management measures, such as setting a maximum loss limit to avoid huge losses caused by continuous stop loss.
4. Optimize the stop loss multiple: Currently, the stop loss multiple is fixed, and it can be considered to dynamically adjust the multiple according to market conditions to reduce risk.
DCA Strategy with Mean Reversion and Bollinger BandDCA Strategy with Mean Reversion and Bollinger Band
The Dollar-Cost Averaging (DCA) Strategy with Mean Reversion and Bollinger Bands is a sophisticated trading strategy that combines the principles of DCA, mean reversion, and technical analysis using Bollinger Bands. This strategy aims to capitalize on market corrections by systematically entering positions during periods of price pullbacks and reversion to the mean.
Key Concepts and Principles
1. Dollar-Cost Averaging (DCA)
DCA is an investment strategy that involves regularly purchasing a fixed dollar amount of an asset, regardless of its price. The idea behind DCA is that by spreading out investments over time, the impact of market volatility is reduced, and investors can avoid making large investments at inopportune times. The strategy reduces the risk of buying all at once during a market high and can smooth out the cost of purchasing assets over time.
In the context of this strategy, the Investment Amount (USD) is set by the user and represents the amount of capital to be invested in each buy order. The strategy executes buy orders whenever the price crosses below the lower Bollinger Band, which suggests a potential market correction or pullback. This is an effective way to average the entry price and avoid the emotional pitfalls of trying to time the market perfectly.
2. Mean Reversion
Mean reversion is a concept that suggests prices will tend to return to their historical average or mean over time. In this strategy, mean reversion is implemented using the Bollinger Bands, which are based on a moving average and standard deviation. The lower band is considered a potential buy signal when the price crosses below it, indicating that the asset has become oversold or underpriced relative to its historical average. This triggers the DCA buy order.
Mean reversion strategies are popular because they exploit the natural tendency of prices to revert to their mean after experiencing extreme deviations, such as during market corrections or panic selling.
3. Bollinger Bands
Bollinger Bands are a technical analysis tool that consists of three lines:
Middle Band: The moving average, usually a 200-period Exponential Moving Average (EMA) in this strategy. This serves as the "mean" or baseline.
Upper Band: The middle band plus a certain number of standard deviations (multiplier). The upper band is used to identify overbought conditions.
Lower Band: The middle band minus a certain number of standard deviations (multiplier). The lower band is used to identify oversold conditions.
In this strategy, the Bollinger Bands are used to identify potential entry points for DCA trades. When the price crosses below the lower band, this is seen as a potential opportunity for mean reversion, suggesting that the asset may be oversold and could reverse back toward the middle band (the EMA). Conversely, when the price crosses above the upper band, it indicates overbought conditions and signals potential market exhaustion.
4. Time-Based Entry and Exit
The strategy has specific entry and exit points defined by time parameters:
Open Date: The date when the strategy begins opening positions.
Close Date: The date when all positions are closed.
This time-bound approach ensures that the strategy is active only during a specified window, which can be useful for testing specific market conditions or focusing on a particular time frame.
5. Position Sizing
Position sizing is determined by the Investment Amount (USD), which is the fixed amount to be invested in each buy order. The quantity of the asset to be purchased is calculated by dividing the investment amount by the current price of the asset (investment_amount / close). This ensures that the amount invested remains constant despite fluctuations in the asset's price.
6. Closing All Positions
The strategy includes an exit rule that closes all positions once the specified close date is reached. This allows for controlled exits and limits the exposure to market fluctuations beyond the strategy's timeframe.
7. Background Color Based on Price Relative to Bollinger Bands
The script uses the background color of the chart to provide visual feedback about the price's relationship with the Bollinger Bands:
Red background indicates the price is above the upper band, signaling overbought conditions.
Green background indicates the price is below the lower band, signaling oversold conditions.
This provides an easy-to-interpret visual cue for traders to assess the current market environment.
Postscript: Configuring Initial Capital for Backtesting
To ensure the backtest results align with the actual investment scenario, users must adjust the Initial Capital in the TradingView strategy properties. This is done by calculating the Initial Capital as the product of the Total Closed Trades and the Investment Amount (USD). For instance:
If the user is investing 100 USD per trade and has 10 closed trades, the Initial Capital should be set to 1,000 USD.
Similarly, if the user is investing 200 USD per trade and has 24 closed trades, the Initial Capital should be set to 4,800 USD.
This adjustment ensures that the backtesting results reflect the actual capital deployed in the strategy and provides an accurate representation of potential gains and losses.
Conclusion
The DCA strategy with Mean Reversion and Bollinger Bands is a systematic approach to investing that leverages the power of regular investments and technical analysis to reduce market timing risks. By combining DCA with the insights offered by Bollinger Bands and mean reversion, this strategy offers a structured way to navigate volatile markets while targeting favorable entry points. The clear entry and exit rules, coupled with time-based constraints, make it a robust and disciplined approach to long-term investing.
Bollinger Breakout Strategy with Direction Control [4H crypto]Bollinger Breakout Strategy with Direction Control - User Guide
This strategy leverages Bollinger Bands, RSI, and directional filters to identify potential breakout trading opportunities. It is designed for traders looking to capitalize on significant price movements while maintaining control over trade direction (long, short, or both). Here’s how to use this strategy effectively:
How the Strategy Works
Indicators Used:
Bollinger Bands:
A volatility-based indicator with an upper and lower band around a simple moving average (SMA). The bands expand or contract based on market volatility.
RSI (Relative Strength Index):
Measures momentum to determine overbought or oversold conditions. In this strategy, RSI is used to confirm breakout strength.
Trade Direction Control:
You can select whether to trade:
Long only: Buy positions.
Short only: Sell positions.
Both: Trade in both directions depending on conditions.
Breakout Conditions:
Long Trade:
The price closes above the upper Bollinger Band.
RSI is above the midline (50), confirming upward momentum.
The "Trade Direction" setting allows either "Long" or "Both."
Short Trade:
The price closes below the lower Bollinger Band.
RSI is below the midline (50), confirming downward momentum.
The "Trade Direction" setting allows either "Short" or "Both."
Risk Management:
Stop-Loss:
Long trades: Set at 2% below the entry price.
Short trades: Set at 2% above the entry price.
Take-Profit:
Calculated using a Risk/Reward Ratio (default is 2:1).
Adjust this in the strategy settings.
Inputs and Customization
Key Parameters:
Bollinger Bands Length: Default is 20. Adjust based on the desired sensitivity.
Multiplier: Default is 2.0. Higher values widen the bands; lower values narrow them.
RSI Length: Default is 14, which is standard for RSI.
Risk/Reward Ratio: Default is 2.0. Increase for more aggressive profit targets, decrease for conservative exits.
Trade Direction:
Options: "Long," "Short," or "Both."
Example: Set to "Long" in a bullish market to focus only on buy trades.
How to Use This Strategy
Adding the Strategy:
Paste the script into TradingView’s Pine Editor and add it to your chart.
Setting Parameters:
Adjust the Bollinger Band settings, RSI, and Risk/Reward Ratio to fit the asset and timeframe you're trading.
Analyzing Signals:
Green line (Upper Band): Signals breakout potential for long trades.
Red line (Lower Band): Signals breakout potential for short trades.
Blue line (Basis): Central Bollinger Band (SMA), helpful for understanding price trends.
Testing the Strategy:
Use the Strategy Tester in TradingView to backtest performance on your chosen asset and timeframe.
Optimizing for Assets:
Forex pairs, cryptocurrencies (like BTC), or stocks with high volatility are ideal for this strategy.
Works best on higher timeframes like 4H or Daily.
Best Practices
Combine with Volume: Confirm breakouts with increased volume for higher reliability.
Avoid Sideways Markets: Use additional trend filters (like ADX) to avoid trades in low-volatility conditions.
Optimize Parameters: Regularly adjust the Bollinger Bands multiplier and RSI settings to match the asset's behavior.
By utilizing this strategy, you can effectively trade breakouts while maintaining flexibility in trade direction. Adjust the parameters to match your trading style and market conditions for optimal results!
Fibonacci Levels Strategy with High/Low Criteria-AYNETThis code represents a TradingView strategy that uses Fibonacci levels in conjunction with high/low price criteria over specified lookback periods to determine buy (long) and sell (short) conditions. Below is an explanation of each main part of the code:
Explanation of Key Sections
User Inputs for Higher Time Frame and Candle Settings
Users can select a higher time frame (timeframe) for analysis and specify whether to use the "Current" or "Last" higher time frame (HTF) candle for calculating Fibonacci levels.
The currentlast setting allows flexibility between using real-time or the most recent closed higher time frame candle.
Lookback Periods for High/Low Criteria
Two lookback periods, lowestLookback and highestLookback, allow users to set the number of bars to consider when finding the lowest and highest prices, respectively.
This determines the criteria for entering trades based on how recent highs or lows compare to current prices.
Fibonacci Levels Configuration
Fibonacci levels (0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, and 100%) are configurable. These are used to calculate price levels between the high and low of the higher time frame candle.
Each level represents a retracement or extension relative to the high/low range of the HTF candle, providing important price levels for decision-making.
HTF Candle Calculation
HTF candle data is calculated based on the higher time frame selected by the user, using the newbar check to reset htfhigh, htflow, and htfopen values.
The values are updated with each new HTF bar or as prices move within the same HTF bar to track the highest high and lowest low accurately.
Set Fibonacci Levels Array
Using the calculated HTF candle's high, low, and open, the Fibonacci levels are computed by interpolating these values according to the user-defined Fibonacci levels.
A fibLevels array stores these computed values.
Plotting Fibonacci Levels
Each Fibonacci level is plotted on the chart with a different color, providing visual indicators for potential support/resistance levels.
High/Low Price Criteria Calculation
The lowest and highest prices over the specified lookback periods (lowestLookback and highestLookback) are calculated and plotted on the chart. These serve as dynamic levels to trigger long or short entries.
Trade Signal Conditions
longCondition: A long (buy) signal is generated when the price crosses above both the lowest price criteria and the 50% Fibonacci level.
shortCondition: A short (sell) signal is generated when the price crosses below both the highest price criteria and the 50% Fibonacci level.
Executing Trades
Based on the longCondition and shortCondition, trades are entered with the strategy.entry() function, using the labels "Long" and "Short" for tracking on the chart.
Strategy Use
This strategy allows traders to utilize Fibonacci retracement levels and recent highs/lows to identify trend continuation or reversal points, potentially providing entry points aligned with larger market structure. Adjusting the lowestLookback and highestLookback along with Fibonacci levels enables a customizable approach to suit different trading styles and market conditions.
Honest Volatility Grid [Honestcowboy]The Honest Volatility Grid is an attempt at creating a robust grid trading strategy but without standard levels.
Normal grid systems use price levels like 1.01;1.02;1.03;1.04... and place an order at each of these levels. In this program instead we create a grid using keltner channels using a long term moving average.
🟦 IS THIS EVEN USEFUL?
The idea is to have a more fluid style of trading where levels expand and follow price and do not stick to precreated levels. This however also makes each closed trade different instead of using fixed take profit levels. In this strategy a take profit level can even be a loss. It is useful as a strategy because it works in a different way than most strategies, making it a good tool to diversify a portfolio of trading strategies.
🟦 STRATEGY
There are 10 levels below the moving average and 10 above the moving average. For each side of the moving average the strategy uses 1 to 3 orders maximum (3 shorts at top, 3 longs at bottom). For instance you buy at level 2 below moving average and you increase position size when level 6 is reached (a cheaper price) in order to spread risks.
By default the strategy exits all trades when the moving average is reached, this makes it a mean reversion strategy. It is specifically designed for the forex market as these in my experience exhibit a lot of ranging behaviour on all the timeframes below daily.
There is also a stop loss at the outer band by default, in case price moves too far from the mean.
What are the risks?
In case price decides to stay below the moving average and never reaches the outer band one trade can create a very substantial loss, as the bands will keep following price and are not at a fixed level.
Explanation of default parameters
By default the strategy uses a starting capital of 25000$, this is realistic for retail traders.
Lot sizes at each level are set to minimum lot size 0.01, there is no reason for the default to be risky, if you want to risk more or increase equity curve increase the number at your own risk.
Slippage set to 20 points: that's a normal 2 pip slippage you will find on brokers.
Fill limit assumtion 20 points: so it takes 2 pips to confirm a fill, normal forex spread.
Commission is set to 0.00005 per contract: this means that for each contract traded there is a 5$ or whatever base currency pair has as commission. The number is set to 0.00005 because pinescript does not know that 1 contract is 100000 units. So we divide the number by 100000 to get a realistic commission.
The script will also multiply lot size by 100000 because pinescript does not know that lots are 100000 units in forex.
Extra safety limit
Normally the script uses strategy.exit() to exit trades at TP or SL. But because these are created 1 bar after a limit or stop order is filled in pinescript. There are strategy.orders set at the outer boundaries of the script to hedge against that risk. These get deleted bar after the first order is filled. Purely to counteract news bars or huge spikes in price messing up backtest.
🟦 VISUAL GOODIES
I've added a market profile feature to the edge of the grid. This so you can see in which grid zone market has been the most over X bars in the past. Some traders may wish to only turn on the strategy whenever the market profile displays specific characteristics (ranging market for instance).
These simply count how many times a high, low, or close price has been in each zone for X bars in the past. it's these purple boxes at the right side of the chart.
🟦 Script can be fully automated to MT5
There are risk settings in lot sizes or % for alerts and symbol settings provided at the bottom of the indicator. The script will send alert to MT5 broker trying to mimic the execution that happens on tradingview. There are always delays when using a bridge to MT5 broker and there could be errors so be mindful of that. This script sends alerts in format so they can be read by tradingview.to which is a bridge between the platforms.
Use the all alert function calls feature when setting up alerts and make sure you provide the right webhook if you want to use this approach.
Almost every setting in this indicator has a tooltip added to it. So if any setting is not clear hover over the (?) icon on the right of the setting.
Bollinger Bands + RSI StrategyThe Bollinger Bands + RSI strategy combines volatility and momentum indicators to spot trading opportunities in intraday settings. Here’s a concise summary:
Components:
Bollinger Bands: Measures market volatility. The lower band signals potential buying opportunities when the price is considered oversold.
Relative Strength Index (RSI): Evaluates momentum to identify overbought or oversold conditions. An RSI below 30 indicates oversold, suggesting a buy, and above 70 indicates overbought, suggesting a sell.
Strategy Execution:
Buy Signal : Triggered when the price falls below the lower Bollinger Band while the RSI is also below 30.
Sell Signal : Activated when the price exceeds the upper Bollinger Band with an RSI above 70.
Exit Strategy : Exiting a buy position is considered when the RSI crosses back above 50, capturing potential rebounds.
Advantages:
Combines price levels with momentum for more reliable signals.
Clearly defined entry and exit points help minimize emotional trading.
Considerations:
Can produce false signals in very volatile or strongly trending markets.
Best used in markets without a strong prevailing trend.
This strategy aids traders in making decisions based on technical indicators, enhancing their ability to profit from short-term price movements.
VWAP Stdev Bands Strategy (Long Only)The VWAP Stdev Bands Strategy (Long Only) is designed to identify potential long entry points in trending markets by utilizing the Volume Weighted Average Price (VWAP) and standard deviation bands. This strategy focuses on capturing upward price movements, leveraging statistical measures to determine optimal buy conditions.
Key Features:
VWAP Calculation: The strategy calculates the VWAP, which represents the average price a security has traded at throughout the day, weighted by volume. This is an essential indicator for determining the overall market trend.
Standard Deviation Bands: Two bands are created above and below the VWAP, calculated using specified standard deviations. These bands act as dynamic support and resistance levels, providing insight into price volatility and potential reversal points.
Trading Logic:
Long Entry Condition: A long position is triggered when the price crosses below the lower standard deviation band and then closes above it, signaling a potential price reversal to the upside.
Profit Target: The strategy allows users to set a predefined profit target, closing the long position once the specified target is reached.
Time Gap Between Orders: A customizable time gap can be specified to prevent multiple orders from being placed in quick succession, allowing for a more controlled trading approach.
Visualization: The VWAP and standard deviation bands are plotted on the chart with distinct colors, enabling traders to visually assess market conditions. The strategy also provides optional plotting of the previous day's VWAP for added context.
Use Cases:
Ideal for traders looking to engage in long-only positions within trending markets.
Suitable for intraday trading strategies or longer-term approaches based on market volatility.
Customization Options:
Users can adjust the standard deviation values, profit target, and time gap to tailor the strategy to their specific trading style and market conditions.
Note: As with any trading strategy, it is important to conduct thorough backtesting and analysis before live trading. Market conditions can change, and past performance does not guarantee future results.
PTS - Bollinger Bands with Trailing StopPTS - Bollinger Bands with Trailing Stop Strategy
Overview
The "PTS - Bollinger Bands with Trailing Stop" strategy is designed to capitalize on strong bullish market movements by combining the Bollinger Bands indicator with a dynamic trailing stop based on the Average True Range (ATR). This strategy aims to enter long positions during upward breakouts and protect profits through an adaptive exit mechanism.
Key Features
1. Bollinger Bands Indicator
Basis Moving Average Type: Choose from SMA, EMA, SMMA, WMA, or VWMA for the Bollinger Bands' basis line. Length: Adjustable period for calculating the moving average and standard deviation (default is 20). Standard Deviation Multiplier (StdDev): Determines the width of the bands (default is 2.0). Source: Select the price data used for calculations (default is the closing price).
2. Entry Conditions
The strategy enters a long position when the closing price crosses above the upper Bollinger Band , indicating strong upward momentum.
3. Exit Conditions
Trailing Stop Loss: Implemented using ATR to adjust dynamically with market volatility. ATR Length: Period for calculating ATR (default is 14). ATR Multiplier for Trailing Stop: Determines how closely the trailing stop follows the price (default is 2.0). Close Below Lower Bollinger Band: The strategy exits the long position if the closing price crosses below the lower Bollinger Band .
4. Risk Management
Commission and Slippage: Commission is set at 0.1%; slippage is set to 3. Position Sizing: Uses 100% of equity per trade (adjustable).
5. Date Range Filter
Specify the time period during which the strategy is active. Start Date: January 1, 2018. End Date: December 31, 2069.
Customizable Inputs
BB Length: Adjust the period for Bollinger Bands calculation. BB StdDev: Modify the standard deviation multiplier. Basis MA Type: Select the moving average type. Source: Choose the price data source. Offset: Shift the Bollinger Bands on the chart. ATR Length: Set the period for ATR calculation. ATR Multiplier for Trailing Stop: Adjust the trailing stop sensitivity.
Thought for 1m 7s
Description for the "PTS - Bollinger Bands with Trailing Stop" Strategy
PTS - Bollinger Bands with Trailing Stop Strategy
Overview
The "PTS - Bollinger Bands with Trailing Stop" strategy is designed to capitalize on strong bullish market movements by combining the Bollinger Bands indicator with a dynamic trailing stop based on the Average True Range (ATR). This strategy aims to enter long positions during upward breakouts and protect profits through an adaptive exit mechanism.
Key Features
1. Bollinger Bands Indicator
Basis Moving Average Type: Choose from SMA, EMA, SMMA, WMA, or VWMA for the Bollinger Bands' basis line. Length: Adjustable period for calculating the moving average and standard deviation (default is 20). Standard Deviation Multiplier (StdDev): Determines the width of the bands (default is 2.0). Source: Select the price data used for calculations (default is the closing price).
2. Entry Conditions
The strategy enters a long position when the closing price crosses above the upper Bollinger Band , indicating strong upward momentum.
3. Exit Conditions
Trailing Stop Loss: Implemented using ATR to adjust dynamically with market volatility. ATR Length: Period for calculating ATR (default is 14). ATR Multiplier for Trailing Stop: Determines how closely the trailing stop follows the price (default is 2.0). Close Below Lower Bollinger Band: The strategy exits the long position if the closing price crosses below the lower Bollinger Band .
4. Risk Management
Commission and Slippage: Commission is set at 0.1%; slippage is set to 3. Position Sizing: Uses 100% of equity per trade (adjustable).
5. Date Range Filter
Specify the time period during which the strategy is active. Start Date: January 1, 2018. End Date: December 31, 2069.
Customizable Inputs
BB Length: Adjust the period for Bollinger Bands calculation. BB StdDev: Modify the standard deviation multiplier. Basis MA Type: Select the moving average type. Source: Choose the price data source. Offset: Shift the Bollinger Bands on the chart. ATR Length: Set the period for ATR calculation. ATR Multiplier for Trailing Stop: Adjust the trailing stop sensitivity.
How the Strategy Works
1. Initialization
Calculates Bollinger Bands and ATR based on selected parameters.
2. Entry Logic
Opens a long position when the closing price exceeds the upper Bollinger Band.
3. Exit Logic
Uses a trailing stop loss based on ATR. Exits if the closing price drops below the lower Bollinger Band.
4. Date Filtering
Executes trades only within the specified date range.
Advantages
Adaptive Risk Management: Trailing stop adjusts to market volatility. Simplicity: Clear entry and exit signals. Customizable Parameters: Tailor the strategy to different assets or conditions.
Considerations
Aggressive Position Sizing: Using 100% equity per trade is high-risk. Market Conditions: Best in trending markets; may produce false signals in sideways markets. Backtesting: Always test on historical data before live trading.
Disclaimer
This strategy is intended for educational and informational purposes only. Trading involves significant risk, and past performance is not indicative of future results. Assess your financial situation and consult a financial advisor if necessary.
Usage Instructions
1. Apply the Strategy: Add it to your TradingView chart. 2. Configure Inputs: Adjust parameters to suit your style and asset. 3. Analyze Backtest Results: Use the Strategy Tester. 4. Optimize Parameters: Experiment with input values. 5. Risk Management: Evaluate position sizing and incorporate risk controls.
Final Notes
The "PTS - Bollinger Bands with Trailing Stop" strategy provides a framework to leverage momentum breakouts while managing risk through adaptive trailing stops. Customize and test thoroughly to align with your trading objectives.
Harmony Signal Flow By ArunThis Pine Script strategy, titled "Harmony Signal Flow By Arun," uses the Relative Strength Index (RSI) indicator to generate buy and sell signals based on custom thresholds. The script incorporates stop-loss and target management and restricts new trades until the previous position closes. Here's a detailed description:
Custom RSI Metric:
The strategy calculates a 5-period RSI based on the closing price, aiming for a more responsive measure of price momentum.
RSI thresholds are defined:
Lower threshold (30): Indicates oversold conditions, triggering a potential buy.
Upper threshold (70): Indicates overbought conditions, prompting a possible sell.
Entry Conditions:
Buy Signal: The strategy initiates a buy order when the RSI crosses above the lower threshold (30), indicating a shift from oversold conditions.
Sell Signal: A sell order is triggered when the RSI crosses below the upper threshold (70), suggesting an overbought reversal.
Only one order (buy or sell) can be active at a time, ensuring that a new trade begins only when there’s no existing position.
Stop-Loss and Target Management:
For each trade, stop-loss and target conditions are applied to manage risk and secure profits.
For Buy Positions:
Stop-loss is set 100 points below the entry price.
Target is set 150 points above the entry price.
For Sell Positions:
Stop-loss is set 100 points above the entry price.
Target is 150 points below the entry price.
The strategy closes the trade when either the stop-loss or target is met, marking the trade as "closed" and allowing a new trade entry.
Trade Sequencing:
A new trade (buy or sell) is only permitted after the previous position hits either its stop-loss or target, preventing overlapping trades and ensuring clear trade sequences.
This sequential approach enhances risk management by ensuring only one active position at any time.
End-of-Day Closure:
All open positions are closed automatically at 3:25 PM (Indian market time) to avoid overnight exposure, ensuring the strategy remains strictly intraday.
The flag for trade entry is reset at the end of each day, enabling fresh trades the next day.
Chart Indicators:
The script plots buy and sell signals directly on the chart with visible labels.
It also displays the custom RSI metric with horizontal lines for the lower and upper thresholds, providing visual cues for entry and exit points.
Summary
This strategy is a momentum-based intraday trading approach that uses the RSI for identifying potential reversals and manages trades through predefined stop-loss and target levels. By enforcing trade sequencing and closing positions at the end of the trading day, it prioritizes risk management and seeks to capitalize on short-term trends while avoiding overnight market risks.
[ETH] Optimized Trend Strategy - Lorenzo SuperScalpStrategy Title: Optimized Trend Strategy - Lorenzo SuperScalp
Description:
The Optimized Trend Strategy is a comprehensive trading system tailored for Ethereum (ETH) and optimized for the 15-minute timeframe but adaptable to various timeframes. This strategy utilizes a combination of technical indicators—RSI, Bollinger Bands, and MACD—to identify and act on price trends efficiently, providing traders with actionable buy and sell signals based on market conditions.
Key Features:
Multi-Indicator Approach:
RSI (Relative Strength Index): Identifies overbought and oversold conditions to time market entries and exits.
Bollinger Bands: Acts as a dynamic support and resistance level, helping to pinpoint precise entry and exit zones.
MACD (Moving Average Convergence Divergence): Detects momentum changes through bullish and bearish crossovers.
Signal Conditions:
Buy Signal:
RSI is below 45 (indicating an oversold condition).
Price is near or below the lower Bollinger Band.
MACD bullish crossover occurs.
Sell Signal:
RSI is above 55 (indicating an overbought condition).
Price is near or above the upper Bollinger Band.
MACD bearish crossunder occurs.
Trade Execution Logic:
Long Trades: Opened when a buy signal flashes. If there’s an open short position, it is closed before opening a long.
Short Trades: Opened when a sell signal flashes. If there’s an open long position, it is closed before opening a short.
The strategy also ensures a minimum number of bars between consecutive trades to avoid rapid trading in choppy conditions.
Pyramiding Support:
Up to 3 consecutive trades in the same direction are allowed, enabling traders to scale into positions based on strong signals.
Visual Indicators:
RSI Levels: Dotted lines at 45 and 55 for quick reference to oversold and overbought levels.
Buy and Sell Signals: Visual markers on the chart indicate where trades are executed, ensuring clarity on entry and exit points.
Best Used For:
Swing Trading & Scalping: While optimized for the 15-minute timeframe, this strategy works across various timeframes, making it suitable for both short-term scalping and swing trading.
Crypto Trading: Tailored for Ethereum but effective for other cryptocurrencies due to its dynamic indicator setup.
Keltner Channel Strategy by Kevin DaveyKeltner Channel Strategy Description
The Keltner Channel Strategy is a volatility-based trading approach that uses the Keltner Channel, a technical indicator derived from the Exponential Moving Average (EMA) and Average True Range (ATR). The strategy helps identify potential breakout or mean-reversion opportunities in the market by plotting upper and lower bands around a central EMA, with the channel width determined by a multiplier of the ATR.
Components:
1. Exponential Moving Average (EMA):
The EMA smooths price data by placing greater weight on recent prices, allowing traders to track the market’s underlying trend more effectively than a simple moving average (SMA). In this strategy, a 20-period EMA is used as the midline of the Keltner Channel.
2. Average True Range (ATR):
The ATR measures market volatility over a 14-period lookback. By calculating the average of the true ranges (the greatest of the current high minus the current low, the absolute value of the current high minus the previous close, or the absolute value of the current low minus the previous close), the ATR captures how much an asset typically moves over a given period.
3. Keltner Channel:
The upper and lower boundaries are set by adding or subtracting 1.5 times the ATR from the EMA. These boundaries create a dynamic range that adjusts with market volatility.
Trading Logic:
• Long Entry Condition: The strategy enters a long position when the closing price falls below the lower Keltner Channel, indicating a potential buying opportunity at a support level.
• Short Entry Condition: The strategy enters a short position when the closing price exceeds the upper Keltner Channel, signaling a potential selling opportunity at a resistance level.
The strategy plots the upper and lower Keltner Channels and the EMA on the chart, providing a visual representation of support and resistance levels based on market volatility.
Scientific Support for Volatility-Based Strategies:
The use of volatility-based indicators like the Keltner Channel is supported by numerous studies on price momentum and volatility trading. Research has shown that breakout strategies, particularly those leveraging volatility bands such as the Keltner Channel or Bollinger Bands, can be effective in capturing trends and reversals in both trending and mean-reverting markets  .
Who is Kevin Davey?
Kevin Davey is a highly respected algorithmic trader, author, and educator, known for his systematic approach to building and optimizing trading strategies. With over 25 years of experience in the markets, Davey has earned a reputation as an expert in quantitative and rule-based trading. He is particularly well-known for winning several World Cup Trading Championships, where he consistently demonstrated high returns with low risk.
Bollinger Bands Mean Reversion by Kevin Davey Bollinger Bands Mean Reversion Strategy Description
The Bollinger Bands Mean Reversion Strategy is a popular trading approach based on the concept of volatility and market overreaction. The strategy leverages Bollinger Bands, which consist of an upper and lower band plotted around a central moving average, typically using standard deviations to measure volatility. When the price moves beyond these bands, it signals potential overbought or oversold conditions, and the strategy seeks to exploit a reversion back to the mean (the central band).
Strategy Components:
1. Bollinger Bands:
The bands are calculated using a 20-period Simple Moving Average (SMA) and a multiple (usually 2.0) of the standard deviation of the asset’s price over the same period. The upper band represents the SMA plus two standard deviations, while the lower band is the SMA minus two standard deviations. The distance between the bands increases with higher volatility and decreases with lower volatility.
2. Mean Reversion:
Mean reversion theory suggests that, over time, prices tend to move back toward their historical average. In this strategy, a buy signal is triggered when the price falls below the lower Bollinger Band, indicating a potential oversold condition. Conversely, the position is closed when the price rises back above the upper Bollinger Band, signaling an overbought condition.
Entry and Exit Logic:
Buy Condition: The strategy enters a long position when the price closes below the lower Bollinger Band, anticipating a mean reversion to the central band (SMA).
Sell Condition: The long position is exited when the price closes above the upper Bollinger Band, implying that the market is likely overbought and a reversal could occur.
This approach uses mean reversion principles, aiming to capitalize on short-term price extremes and volatility compression, often seen in sideways or non-trending markets. Scientific studies have shown that mean reversion strategies, particularly those based on volatility indicators like Bollinger Bands, can be effective in capturing small but frequent price reversals  .
Scientific Basis for Bollinger Bands:
Bollinger Bands, developed by John Bollinger, are widely regarded in both academic literature and practical trading as an essential tool for volatility analysis and mean reversion strategies. Research has shown that Bollinger Bands effectively identify relative price highs and lows, and can be used to forecast price volatility and detect potential breakouts . Studies in financial markets, such as those by Fernández-Rodríguez et al. (2003), highlight the efficacy of Bollinger Bands in detecting overbought or oversold conditions in various assets .
Who is Kevin Davey?
Kevin Davey is an award-winning algorithmic trader and highly regarded expert in developing and optimizing systematic trading strategies. With over 25 years of experience, Davey gained significant recognition after winning the prestigious World Cup Trading Championships multiple times, where he achieved triple-digit returns with minimal drawdown. His success has made him a key figure in algorithmic trading education, with a focus on disciplined and rule-based trading systems.
Statistical ArbitrageThe Statistical Arbitrage Strategy, also known as pairs trading, is a quantitative trading method that capitalizes on price discrepancies between two correlated assets. The strategy assumes that over time, the prices of these two assets will revert to their historical relationship. The core idea is to take advantage of mean reversion, a principle suggesting that asset prices will revert to their long-term average after deviating significantly.
Strategy Mechanics:
1. Selection of Correlated Assets:
• The strategy focuses on two historically correlated assets (e.g., equity index futures like Dow Jones Mini and S&P 500 Mini). These assets tend to move in the same direction due to similar underlying fundamentals, such as overall market conditions. By tracking their relative prices, the strategy seeks to exploit temporary mispricings.
2. Spread Calculation:
• The spread is the difference between the prices of the two assets. This spread represents the relationship between the assets and serves as the basis for determining when to enter or exit trades.
3. Mean and Standard Deviation:
• The historical average (mean) of the spread is calculated using a Simple Moving Average (SMA) over a chosen period. The strategy also computes the standard deviation (volatility) of the spread, which measures how far the spread has deviated from the mean over time. This allows the strategy to define statistically significant price deviations.
4. Entry Signal (Mean Reversion):
• A buy signal is triggered when the spread falls below the mean by a multiple (e.g., two) of the standard deviation. This indicates that one asset is temporarily undervalued relative to the other, and the strategy expects the spread to revert to its mean, generating profits as the prices converge.
5. Exit Signal:
• The strategy exits the trade when the spread reverts to the mean. At this point, the mispricing has been corrected, and the profit from the mean reversion is realized.
Academic Support:
Statistical arbitrage has been widely studied in finance and economics. Gatev, Goetzmann, and Rouwenhorst’s (2006) landmark study on pairs trading demonstrated that this strategy could generate excess returns in equity markets. Their research found that by focusing on historically correlated stocks, traders could identify pricing anomalies and profit from their eventual correction.
Additionally, Avellaneda and Lee (2010) explored statistical arbitrage in different asset classes and found that exploiting deviations in price relationships can offer a robust, market-neutral trading strategy. In these studies, the strategy’s success hinges on the stability of the relationship between the assets and the timely execution of trades when deviations occur.
Risks of Statistical Arbitrage:
1. Correlation Breakdown:
• One of the primary risks is the breakdown of correlation between the two assets. Statistical arbitrage assumes that the historical relationship between the assets will hold in the future. However, market conditions, company fundamentals, or external shocks (e.g., macroeconomic changes) can cause these assets to deviate permanently, leading to potential losses.
• For instance, if two equity indices historically move together but experience divergent economic conditions or policy changes, their prices may no longer revert to the expected mean.
2. Execution Risk:
• This strategy relies on efficient execution and tight spreads. In volatile or illiquid markets, the actual price at which trades are executed may differ significantly from expected prices, leading to slippage and reduced profits.
3. Market Risk:
• Although statistical arbitrage is designed to be market-neutral (i.e., not dependent on the overall market direction), it is not entirely risk-free. Systematic market shocks, such as financial crises or sudden shifts in market sentiment, can affect both assets simultaneously, causing the spread to widen rather than revert to the mean.
4. Model Risk:
• The assumptions underlying the strategy, particularly regarding mean reversion, may not always hold true. The model assumes that asset prices will return to their historical averages within a certain timeframe, but the timing and magnitude of mean reversion can be uncertain. Misestimating this timeframe can lead to extended drawdowns or unrealized losses.
5. Overfitting:
• Over-reliance on historical data to fine-tune the strategy parameters (e.g., the lookback period or standard deviation thresholds) may result in overfitting. This means that the strategy works well on past data but fails to perform in live markets due to changing conditions.
Conclusion:
The Statistical Arbitrage Strategy offers a systematic and quantitative approach to trading that capitalizes on temporary price inefficiencies between correlated assets. It has been proven to generate returns in academic studies and is widely used by hedge funds and institutional traders for its market-neutral characteristics. However, traders must be aware of the inherent risks, including correlation breakdown, execution risks, and the potential for prolonged deviations from the mean. Effective risk management, diversification, and constant monitoring are essential for successfully implementing this strategy in live markets.
G-Channel with EMA StrategyThe G-Channel is a custom channel with an upper (a), lower (b), and average (avg) line. These lines are dynamically calculated based on the current and previous closing prices, using the length input (default 100) to smooth the values:
Upper Line (a): This is the maximum value of the current price or the previous upper value, adjusted by the difference between the upper and lower lines divided by the length.
Lower Line (b): This is the minimum value of the current price or the previous lower value, similarly adjusted by the difference between the upper and lower lines.
The average line (avg) is simply the midpoint between the upper and lower lines. The G-Channel signals trend direction:
Bullish Condition: The system looks for the condition when the price crosses over the lower line (b), indicating a potential upward trend.
Bearish Condition: When the price crosses under the upper line (a), it signals a potential downward trend.
Exponential Moving Average (EMA)
The strategy also incorporates an EMA with a default length of 200. The EMA serves as a trend filter to determine whether the market is trending upward or downward:
Price below EMA: Indicates a bearish trend.
Price above EMA: Indicates a bullish trend.
Buy/Sell Conditions
The strategy generates buy or sell signals based on the interaction between the G-Channel signals and the price relative to the EMA:
Buy Signal: The strategy triggers a buy when:
A bullish condition (recent crossover of price over the lower G-Channel line) is detected.
The price is below the EMA, indicating that despite the recent bullish signal, the market might still be undervalued or in a temporary downturn.
Sell Signal: The strategy triggers a sell when:
A bearish condition (recent crossunder of price below the upper G-Channel line) is detected.
The price is above the EMA, suggesting that the market might be overextended and poised for a downturn.
Visualization
The strategy plots:
The upper, lower, and average lines of the G-Channel, with the average line colored based on bullish (green) or bearish (red) conditions.
The EMA (orange) line to provide context on the general trend direction.
Markers for Buy and Sell signals to visually indicate the strategy's entry points.
Strategy Execution
When a buy or sell signal is detected:
Buy Entry: If the bullish condition and price < EMA condition are met, a long (buy) position is opened.
Sell Entry: If the bearish condition and price > EMA condition are met, a short (sell) position is opened.
Purpose
This strategy aims to catch price reversals at critical points (when the price moves through the G-Channel) while filtering trades using the EMA to avoid entering during unfavorable market trends.
Fibonacci & Bollinger Bands StrategyThis strategy combines Bollinger Bands and Fibonacci retracement/extension levels to identify potential entry and exit points in the market. Here’s a breakdown of each component and how the strategy works:
1. Bollinger Bands:
Bollinger Bands consist of a simple moving average (SMA) and two standard deviations (upper and lower bands) plotted above and below the SMA. The bands expand and contract based on market volatility.
Purpose in Strategy:
The lower band represents an area where the market might be oversold.
The upper band represents an area where the market might be overbought.
The price crossing these bands suggests overextended market conditions, which can be used to identify potential reversals.
2. Fibonacci Retracement and Extension Levels:
Fibonacci retracement levels are horizontal lines that indicate where price might find support or resistance as it retraces some of its previous movement. Common retracement levels are 61.8% and 78.6%.
Fibonacci extension levels are used to project areas where the price might extend after completing a retracement. These levels can help determine potential targets after a significant price movement.
Purpose in Strategy:
The strategy calculates the most recent swing high (fibHigh) and swing low (fibLow) over a lookback period. It then plots Fibonacci retracement and extension levels based on this range.
The Fibonacci levels are used as key support and resistance areas. The price approaching or touching these levels signals potential turning points in the market.
3. Entry Criteria:
A long position (buy) is triggered when:
The price crosses below the lower Bollinger Band, indicating an oversold condition.
The price is near or above a Fibonacci extension level (calculated based on the most recent price swing).
This suggests that the price is potentially reaching a strong support area, where a reversal is likely.
4. Exit Criteria:
The long position is closed (exit trade) when either:
The price touches or crosses the upper Bollinger Band, signaling an overbought condition.
The price reaches a Fibonacci retracement level or exceeds the recent swing high (fibHigh), indicating a potential exhaustion point or a reversal area.
5. General Strategy Logic:
The strategy takes advantage of market volatility (captured by the Bollinger Bands) and key support/resistance levels (determined by Fibonacci retracement and extension levels).
By combining these two techniques, the strategy identifies potential entry points at oversold levels with the expectation that the market will retrace or reverse upward, especially when near key Fibonacci extension levels.
Exit points are identified by potential overbought levels (Bollinger upper band) or key Fibonacci retracement levels, where the price might reverse downward.
6. Conditions to Execute the Strategy:
The Fibonacci levels are only calculated once the price has made a significant movement, establishing a recent high and low over a 50-bar period (which you can adjust). This ensures the Fibonacci levels are based on meaningful swings.
The entry and exit signals are filtered using both Bollinger Bands and Fibonacci levels to ensure that trades are not taken solely based on one indicator, thus reducing false signals.
Key Features of the Strategy:
Trend-following with reversal: It tries to catch reversals when the price hits extreme levels (Bollinger Bands) while respecting important Fibonacci levels.
Dynamic market adaptation: The strategy adapts to market conditions as it recalculates Fibonacci levels based on recent price swings and adjusts the Bollinger Bands for market volatility.
Confirmation through multiple indicators: It uses both the volatility-based signals from Bollinger Bands and the price structure from Fibonacci levels to confirm trade entries and exits.
Summary of the Strategy:
The strategy looks to buy low and sell high based on oversold/overbought signals from Bollinger Bands and Fibonacci levels that indicate key support and resistance zones.
By combining these two technical indicators, the strategy aims to reduce risk and increase accuracy by only entering trades when both indicators suggest favorable conditions.
Trade Entry Detector, Wick to Body Ratio Trade Entry Detector: Wick-to-Body Ratio Strategy with Bollinger Bands
Overview
The Trade Entry Detector is a custom strategy for TradingView that leverages the Bollinger Bands and a unique wick-to-body ratio approach to capture precise entry opportunities. This indicator is designed for traders who want to pinpoint high-probability reversal points when price interacts with Bollinger Bands, all while offering flexible entry fill options.
The strategy performs primary analysis on the daily time frame, regardless of your current chart setting, allowing you to view daily Bollinger Band levels and entry signals even on lower time frames. This approach is suitable for swing traders and short-term traders looking to align intraday moves with higher time frame signals.
How the Strategy Works
1. Bollinger Band Analysis on the Daily Time Frame
Bollinger Bands are calculated using a 20-period simple moving average (SMA) and a standard deviation multiplier (default is 2). These bands dynamically expand and contract based on market volatility, making them ideal for identifying overbought and oversold conditions:
* Upper Band: Indicates potential overbought levels.
* Lower Band: Indicates potential oversold levels.
2. Wick-to-Body Ratio Condition
This strategy places significant emphasis on candle wicks relative to the candle body. Here’s why:
* A large upper wick relative to the body signals potential selling pressure after testing the upper Bollinger Band.
* A large lower wick relative to the body indicates buying support after testing the lower Bollinger Band.
* Ratio Threshold: You can set a minimum wick-to-body ratio (default is 1.0), meaning that the wick must be at least equal in size to the body. This ensures only candles with significant reversals are considered for entry.
3. Flexible Entry Timing
To adapt to various trading styles, the indicator allows you to choose the entry fill timing:
* Daily Close: Enter at the close of the daily candle.
* Daily Open: Enter at the open of the following daily candle.
* HOD (High of Day): Set entry at the daily high, for those who want confirmation of upward momentum.
* LOD (Low of Day): Set entry at the daily low, ideal for confirming downward movement.
4. Position Sizing and Risk Management
The strategy calculates position size based on a fixed risk percentage of your account balance (default is 1%). This approach dynamically adjusts position sizes based on stop-loss distance:
* Stop Loss: Placed at the nearest swing high (for shorts) or swing low (for longs).
* Take Profit: Exits are triggered when the price reaches the opposite Bollinger Band.
5. Order Expiration
Each pending order (long or short) expires after two days if unfilled, allowing for new setups on subsequent candles if conditions are met again.
Using the Trade Entry Detector
Step-by-Step Guide
1. Set the Primary Time Frame
The core calculations run on the daily time frame, but the strategy can be applied to intraday charts (e.g., 65-minute or 15-minute) for deeper insights.
2. Adjust Bollinger Band Settings
* Length: Default is 20, which determines the period for calculating the moving average.
* Standard Deviation Multiplier: Default is 2.0, which sets the width of the bands. Adjusting this can help you capture broader or tighter volatility ranges.
3. Define the Wick-to-Body Ratio
Set the minimum ratio between wick and body (default 1.0). Higher values filter out candles with less wick-to-body contrast, focusing on stronger rejection moves.
4. Choose Entry Fill Timing
Select your preferred fill condition:
* Daily Close: Confirms the trade at the end of the daily session.
* Daily Open: Executes the entry at the open of the next day.
* HOD/LOD: Uses the daily high or low as an additional confirmation for upward or downward moves.
5. Position Sizing and Risk Management
* Set your account balance and risk percentage. The strategy automatically calculates position sizes based on the stop distance to manage risk efficiently.
* Stop Loss and Take Profit points are automatically set based on swing highs/lows and opposing Bollinger Bands, respectively.
Practical Example
Let’s say SPY (S&P 500 ETF) tests the lower Bollinger Band on the daily time frame, with a lower wick that is twice the size of the body (meeting the 1.0 ratio threshold). Here’s how the strategy might proceed:
1. Signal: The lower wick on SPY suggests buying interest at the lower Bollinger Band.
2. Entry Fill Timing: If you’ve selected "Daily Open," the entry order will be placed at the next day's open price.
3. Stop Loss: Positioned at the nearest daily swing low to minimize risk.
4. Take Profit: If SPY price moves up and reaches the upper Bollinger Band, the position is automatically closed.
Indicator Features and Benefits
* Multi-Time Frame Compatibility: Perform daily analysis while tracking signals on any intraday chart.
* Automatic Position Sizing: Tailor risk per trade based on account balance and desired risk percentage.
* Flexible Entry Options: Choose from close, open, HOD, or LOD for optimal timing.
* Effective Trend Reversal Identification: Uses wick-to-body ratio and Bollinger Band interaction to pinpoint potential reversals.
* Dynamic Visualization: Bollinger Bands are displayed on your chosen time frame, allowing seamless intraday tracking.
Summary
The Trade Entry Detector provides a unique, data-driven way to spot reversal points with customizable entry options. By combining Bollinger Bands with wick-to-body ratio conditions, it identifies potential trade setups where price has tested extremes and shown reversal signals. With its flexible entry timing, risk management features, and multi-time frame compatibility, this indicator is ideal for traders looking to blend daily market context with shorter-term execution.
Tips for Usage:
* For swing trading, consider the Daily Open or Close entry options.
* For momentum entries, HOD or LOD may offer better alignment with the direction of the wick.
* Backtest on different assets to find optimal Bollinger Band and wick-to-body settings for your market.
Use this indicator to enhance your understanding of price behavior at key levels and improve the precision of your entry points. Happy trading!
KAMA Cloud STIndicator:
Description:
The KAMA Cloud indicator is a sophisticated trading tool designed to provide traders with insights into market trends and their intensity. This indicator is built on the Kaufman Adaptive Moving Average (KAMA), which dynamically adjusts its sensitivity to filter out market noise and respond to significant price movements. The KAMA Cloud leverages multiple KAMAs to gauge trend direction and strength, offering a visual representation that is easy to interpret.
How It Works:
The KAMA Cloud uses twenty different KAMA calculations, each set to a distinct lookback period ranging from 5 to 100. These KAMAs are calculated using the average of the open, high, low, and close prices (OHLC4), ensuring a balanced view of price action. The relative positioning of these KAMAs helps determine the direction of the market trend and its momentum.
By measuring the cumulative relative distance between these KAMAs, the indicator effectively assesses the overall trend strength, akin to how the Average True Range (ATR) measures market volatility. This cumulative measure helps in identifying the trend’s robustness and potential sustainability.
The visualization component of the KAMA Cloud is particularly insightful. It plots a 'cloud' formed between the base KAMA (set at a 100-period lookback) and an adjusted KAMA that incorporates the cumulative relative distance scaled up. This cloud changes color based on the trend direction — green for upward trends and red for downward trends, providing a clear, visual representation of market conditions.
How the Strategy Works:
The KAMA Cloud ST strategy employs multiple KAMA calculations with varying lengths to capture the nuances of market trends. It measures the relative distances between these KAMAs to determine the trend's direction and strength, much like the original indicator. The strategy enhances decision-making by plotting a 'cloud' formed between the base KAMA (set to a 100-period lookback) and an adjusted KAMA that scales according to the cumulative relative distance of all KAMAs.
Key Components of the Strategy:
Multiple KAMA Layers: The strategy calculates KAMAs for periods ranging from 5 to 100 to analyze short to long-term market trends.
Dynamic Cloud: The cloud visually represents the trend’s strength and direction, updating in real-time as the market evolves.
Signal Generation: Trade signals are generated based on the orientation of the cloud relative to a smoothed version of the upper KAMA boundary. Long positions are initiated when the market trend is upward, and the current cloud value is above its smoothed average. Conversely, positions are closed when the trend reverses, indicated by the cloud falling below the smoothed average.
Suggested Usage:
Market: Stocks, not cryptocurrency
Timeframe: 1 Hour
Indicator:
Advanced Position Management [Mr_Rakun]Advanced Position Management
This Pine Script code is for a strategy titled "Advanced Position Management," aimed at effective trade execution and management using multiple take profit levels, trailing stop loss, and dynamic position sizing.
Take Profit Levels: It defines up to three take profit (TP) levels, allowing partial position exits at different price thresholds. The take profit levels and their respective quantities are adjustable using inputs.
Stop Loss and Trailing Stop: The script implements an initial stop loss based on a percentage from the entry price. Additionally, it features a trailing stop that moves based on either a percentage or previous TP levels, dynamically adjusting to maximize gains while protecting profits.
Position Size: The position size is customizable and based on USD value, allowing the trader to manage risk more effectively.
Advantages:
Flexibility: Multiple take profit levels and a dynamic stop loss system allow traders to lock in profits while keeping the position open for further gains.
Risk Management: The initial stop loss and trailing stop help to limit losses and protect profits as the trade moves in the desired direction.
Automation: Once the strategy is deployed, it automatically handles entry, exit, and stop management, reducing the need for constant monitoring.
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Gelişmiş Pozisyon Yönetimi
Bu Pine Script kodu, Gelişmiş Pozisyon Yönetimi için kendi stratejilerinize kolayca entegre edeceğiniz bir risk yönetimidir. Çoklu kâr al seviyeleri, takip eden stop-loss ve dinamik pozisyon büyüklüğü kullanarak işlem yürütme ve yönetiminde etkilidir.
Gelişmiş Pozisyon Yönetimi
Kâr Alma Seviyeleri;
Kod, pozisyonların farklı fiyat seviyelerinde kısmi kapatılmasını sağlayan üç farklı kâr alma (TP) seviyesini tanımlar. Bu kâr alma seviyeleri ve ilgili miktarları, girişlerle ayarlanabilir.
Stop Loss ve Takip Eden Stop;
Koda, giriş fiyatından bir yüzdeye dayalı olarak başlangıçta stop-loss uygulanır. Ayrıca, fiyat hareketine göre kendini ayarlayan takip eden bir stop-loss sistemi bulunur. Ayrıca TP seviyelerini takip eden stop loss özelliğide vardır.
Avantajları:
Esneklik;
Çoklu kâr alma seviyeleri ve dinamik stop-loss sistemi, trader'ların kazançlarını kilitleyip aynı zamanda pozisyonu açık tutmalarına olanak tanır.
Risk Yönetimi;
Başlangıç stop-loss ve takip eden stop, zararı sınırlamaya ve kazançları korumaya yardımcı olur.
Otomasyon;
Strateji bir kez devreye alındığında, giriş, çıkış ve stop yönetimi otomatik olarak gerçekleştirilir, bu da sürekli takip ihtiyacını azaltır.
Post-Open Long Strategy with ATR-based Stop Loss and Take ProfitThe "Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit" is designed to identify buying opportunities after the German and US markets open. It combines various technical indicators to filter entry signals, focusing on breakout moments following price lateralization periods.
Key Components and Their Interaction:
Bollinger Bands (BB):
Description: Uses BB with a 14-period length and standard deviation multiplier of 1.5, creating narrower bands for lower timeframes.
Role in the Strategy: Identifies low volatility phases (lateralization). The lateralization condition is met when the price is near the simple moving average of the BB, suggesting an imminent increase in volatility.
Exponential Moving Averages (EMA):
10-period EMA: Quickly detects short-term trend direction.
200-period EMA: Filters long-term trends, ensuring entries occur in a bullish market.
Interaction: Positions are entered only if the price is above both EMAs, indicating a consolidated positive trend.
Relative Strength Index (RSI):
Description: 7-period RSI with a threshold above 30.
Role in the Strategy: Confirms the market is not oversold, supporting the validity of the buy signal.
Average Directional Index (ADX):
Description: 7-period ADX with 7-period smoothing and a threshold above 10.
Role in the Strategy: Assesses trend strength. An ADX above 10 indicates sufficient momentum to justify entry.
Average True Range (ATR) for Dynamic Stop Loss and Take Profit:
Description: 14-period ATR with multipliers of 2.0 for Stop Loss and 4.0 for Take Profit.
Role in the Strategy: Adjusts exit levels based on current volatility, enhancing risk management.
Resistance Identification and Breakout:
Description: Analyzes the highs of the last 20 candles to identify resistance levels with at least two touches.
Role in the Strategy: A breakout above this level signals a potential continuation of the bullish trend.
Time Filters and Market Conditions:
Trading Hours: Operates only during the opening of the German market (8:00 - 12:00) and US market (15:30 - 19:00).
Panic Candle: The current candle must close negative, leveraging potential emotional reactions in the market.
Avoiding Entry During Pullbacks:
Description: Checks that the two previous candles are not both bearish.
Role in the Strategy: Avoids entering during a potential pullback, improving trade success probability.
Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit
The "Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit" is designed to identify buying opportunities after the German and US markets open. It combines various technical indicators to filter entry signals, focusing on breakout moments following price lateralization periods.
Key Components and Their Interaction:
Bollinger Bands (BB):
Description: Uses BB with a 14-period length and standard deviation multiplier of 1.5, creating narrower bands for lower timeframes.
Role in the Strategy: Identifies low volatility phases (lateralization). The lateralization condition is met when the price is near the simple moving average of the BB, suggesting an imminent increase in volatility.
Exponential Moving Averages (EMA):
10-period EMA: Quickly detects short-term trend direction.
200-period EMA: Filters long-term trends, ensuring entries occur in a bullish market.
Interaction: Positions are entered only if the price is above both EMAs, indicating a consolidated positive trend.
Relative Strength Index (RSI):
Description: 7-period RSI with a threshold above 30.
Role in the Strategy: Confirms the market is not oversold, supporting the validity of the buy signal.
Average Directional Index (ADX):
Description: 7-period ADX with 7-period smoothing and a threshold above 10.
Role in the Strategy: Assesses trend strength. An ADX above 10 indicates sufficient momentum to justify entry.
Average True Range (ATR) for Dynamic Stop Loss and Take Profit:
Description: 14-period ATR with multipliers of 2.0 for Stop Loss and 4.0 for Take Profit.
Role in the Strategy: Adjusts exit levels based on current volatility, enhancing risk management.
Resistance Identification and Breakout:
Description: Analyzes the highs of the last 20 candles to identify resistance levels with at least two touches.
Role in the Strategy: A breakout above this level signals a potential continuation of the bullish trend.
Time Filters and Market Conditions:
Trading Hours: Operates only during the opening of the German market (8:00 - 12:00) and US market (15:30 - 19:00).
Panic Candle: The current candle must close negative, leveraging potential emotional reactions in the market.
Avoiding Entry During Pullbacks:
Description: Checks that the two previous candles are not both bearish.
Role in the Strategy: Avoids entering during a potential pullback, improving trade success probability.
Entry and Exit Conditions:
Long Entry:
The price breaks above the identified resistance.
The market is in a lateralization phase with low volatility.
The price is above the 10 and 200-period EMAs.
RSI is above 30, and ADX is above 10.
No short-term downtrend is detected.
The last two candles are not both bearish.
The current candle is a "panic candle" (negative close).
Order Execution: The order is executed at the close of the candle that meets all conditions.
Exit from Position:
Dynamic Stop Loss: Set at 2 times the ATR below the entry price.
Dynamic Take Profit: Set at 4 times the ATR above the entry price.
The position is automatically closed upon reaching the Stop Loss or Take Profit.
How to Use the Strategy:
Application on Volatile Instruments:
Ideal for financial instruments that show significant volatility during the target market opening hours, such as indices or major forex pairs.
Recommended Timeframes:
Intraday timeframes, such as 5 or 15 minutes, to capture significant post-open moves.
Parameter Customization:
The default parameters are optimized but can be adjusted based on individual preferences and the instrument analyzed.
Backtesting and Optimization:
Backtesting is recommended to evaluate performance and make adjustments if necessary.
Risk Management:
Ensure position sizing respects risk management rules, avoiding risking more than 1-2% of capital per trade.
Originality and Benefits of the Strategy:
Unique Combination of Indicators: Integrates various technical metrics to filter signals, reducing false positives.
Volatility Adaptability: The use of ATR for Stop Loss and Take Profit allows the strategy to adapt to real-time market conditions.
Focus on Post-Lateralization Breakout: Aims to capitalize on significant moves following consolidation periods, often associated with strong directional trends.
Important Notes:
Commissions and Slippage: Include commissions and slippage in settings for more realistic simulations.
Capital Size: Use a realistic trading capital for the average user.
Number of Trades: Ensure backtesting covers a sufficient number of trades to validate the strategy (ideally more than 100 trades).
Warning: Past results do not guarantee future performance. The strategy should be used as part of a comprehensive trading approach.
With this strategy, traders can identify and exploit specific market opportunities supported by a robust set of technical indicators and filters, potentially enhancing their trading decisions during key times of the day.
Larry Connors %b Strategy (Bollinger Band)Larry Connors’ %b Strategy is a mean-reversion trading approach that uses Bollinger Bands to identify buy and sell signals based on the %b indicator. This strategy was developed by Larry Connors, a renowned trader and author known for his systematic, data-driven trading methods, particularly those focusing on short-term mean reversion.
The %b indicator measures the position of the current price relative to the Bollinger Bands, which are volatility bands placed above and below a moving average. The strategy specifically targets times when prices are oversold within a long-term uptrend and aims to capture rebounds by buying at relatively low points and selling at relatively high points.
Strategy Rules
The basic rules of the %b Strategy are:
1. Trend Confirmation: The closing price must be above the 200-day moving average. This filter ensures that trades are made in alignment with a longer-term uptrend, thereby avoiding trades against the primary market trend.
2. Oversold Conditions: The %b indicator must be below 0.2 for three consecutive days. The %b value below 0.2 indicates that the price is near the lower Bollinger Band, suggesting an oversold condition.
3. Entry Signal: Enter a long position at the close when conditions 1 and 2 are met.
4. Exit Signal: Exit the position when the %b value closes above 0.8, signaling an overbought condition where the price is near the upper Bollinger Band.
How the Strategy Works
This strategy operates on the premise of mean reversion, which suggests that extreme price movements will revert to the mean over time. By entering positions when the %b value indicates an oversold condition (below 0.2) in a confirmed uptrend, the strategy attempts to capture short-term price rebounds. The exit rule (when %b is above 0.8) aims to lock in profits once the price reaches an overbought condition, often near the upper Bollinger Band.
Who Was Larry Connors?
Larry Connors is a well-known figure in the world of financial markets and trading. He co-authored several influential trading books, including “Short-Term Trading Strategies That Work” and “High Probability ETF Trading.” Connors is recognized for his quantitative approach, focusing on systematic, rules-based strategies that leverage historical data to validate trading edges.
His work primarily revolves around short-term trading strategies, often using technical indicators like RSI (Relative Strength Index), Bollinger Bands, and moving averages. Connors’ methodologies have been widely adopted by traders seeking structured approaches to exploit short-term inefficiencies in the market.
Risks of the Strategy
While the %b Strategy can be effective, particularly in mean-reverting markets, it is not without risks:
1. Mean Reversion Assumption: The strategy is based on the assumption that prices will revert to the mean. In trending or sharply falling markets, this reversion may not occur, leading to sustained losses.
2. False Signals in Choppy Markets: In volatile or sideways markets, the strategy may generate multiple false signals, resulting in whipsaw trades that can erode capital through frequent small losses.
3. No Stop Loss: The basic implementation of the strategy does not include a stop loss, which increases the risk of holding losing trades longer than intended, especially if the market continues to move against the position.
4. Performance During Market Crashes: During major market downturns, the strategy’s buy signals could be triggered frequently as prices decline, compounding losses without the presence of a risk management mechanism.
Scientific References and Theoretical Basis
The %b Strategy relies on the concept of mean reversion, which has been extensively studied in finance literature. Studies by Avellaneda and Lee (2010) and Bouchaud et al. (2018) have demonstrated that mean-reverting strategies can be profitable in specific market environments, particularly when combined with volatility filters like Bollinger Bands. However, the same studies caution that such strategies are highly sensitive to market conditions and often perform poorly during periods of prolonged trends.
Bollinger Bands themselves were popularized by John Bollinger and are widely used to assess price volatility and detect potential overbought and oversold conditions. The %b value is a critical part of this analysis, as it standardizes the position of price relative to the bands, making it easier to compare conditions across different securities and time frames.
Conclusion
Larry Connors’ %b Strategy is a well-known mean-reversion technique that leverages Bollinger Bands to identify buying opportunities in uptrending markets when prices are temporarily oversold. While the strategy can be effective under the right conditions, traders should be aware of its limitations and risks, particularly in trending or highly volatile markets. Incorporating risk management techniques, such as stop losses, could help mitigate some of these risks, making the strategy more robust against adverse market conditions.
