Chandelier Exit Pro w/ExtensionsChandelier Exit Pro w/Extensions
The Chandelier Exit Pro w/Extensions indicator is designed to assist traders in managing risk and identifying trend reversals. The strategy is based on the Chandelier Exit concept, originally created by Charles Le Beau. It uses the Average True Range (ATR) to calculate dynamic stop levels that adjust based on market volatility. This script not only implements the standard Chandelier Exit, but also introduces extension levels and alerts to enhance decision-making.
Key Features:
➡️Dynamic Stop Levels: The indicator calculates stop levels for both long and short positions based on an ATR multiple. This allows traders to determine exit points by monitoring when the price crosses above or below these levels. These levels adapt in real-time based on price volatility, making them a versatile tool for trend-following strategies.
➡️Extension Levels: In addition to the primary stop levels, the script includes extension levels for more advanced stop-loss management. Traders can view active and extension levels separately, providing more flexibility in their exit strategies.
➡️Labels and Visual Cues: The indicator provides dynamic labels that automatically update and follow the plotted stop levels. Labels include the ATR multiplier value (e.g., "2.5" or "2.5ext"), clearly showing the significance of each level. When price crosses below or above a level, the corresponding label is highlighted, aiding traders in quickly identifying the most relevant stop level.
➡️Bar Confirmation and Alerts: The script includes an "await bar confirmation" option to ensure that the stop levels and alerts only trigger after the bar has closed. Alerts are customizable and will notify traders when price crosses critical levels, helping to make timely decisions without the need to constantly monitor charts.
➡️Multiple ATR Levels for Enhanced Precision: The indicator supports up to four different ATR levels, each with customizable multipliers. This allows traders to set different thresholds for exits based on varying degrees of volatility. For example, Level 1 (2.5x ATR) might represent a tighter stop, while Level 4 (10x ATR) could serve as a wider stop for long-term positions.
➡️Calc_bars_count: Improves efficiency of the indicator by reducing the on-chart calculations in to the past. This input can be found at the bottom of the INPUTS tab.
How it Helps Traders:
💥Trend Identification: By using the Chandelier Exit levels, traders can identify when the trend is likely to reverse. When the price crosses below the stop level in a long trade or above the stop level in a short trade, it signals a potential exit point.
💥Volatility-based Adjustments: Unlike static stop-loss methods, the ATR-based stop levels dynamically adjust based on the market’s volatility. This means tighter stops during low volatility periods and wider stops during high volatility periods, reducing the chance of being stopped out prematurely.
💥Risk Management: The dynamic stop levels and extension levels provide a structured way to manage risk. Traders can set tighter stops for short-term trades and wider stops for longer-term trades. The script's visual labels make it easy to track these levels in real-time.
💥Automation with Alerts: The built-in alert system ensures that traders are notified when key levels are crossed. This helps to avoid emotional decision-making and allows for better execution of trading strategies.
Confluence and Price Fluidity:
One of the powerful ways to enhance the effectiveness of the Chandelier Exit indicator is by using it in conjunction with other technical analysis tools to create confluence. Confluence occurs when multiple indicators or price action signals align, providing stronger confirmation for a trade decision. For example:
🎯Support and Resistance Levels: Traders can use the Chandelier Exit levels in combination with key support and resistance zones. If the price is nearing a support level and the Chandelier Exit signals a bullish reversal, this alignment strengthens the case for entering a long position.
🎯Moving Averages: When the Chandelier Exit signals a trend reversal and this is confirmed by a crossover in moving averages (such as a 50-day and 200-day moving average), traders gain additional confidence in the trade direction.
🎯Momentum Indicators: Traders can also look for momentum indicators like RSI or MACD to confirm the strength of a trend or potential reversal. For instance, if the Chandelier Exit triggers a short signal and the RSI also shows overbought conditions, this could provide stronger confirmation to exit a long trade or enter a short position.
🎯Candlestick Patterns: Price fluidity can be monitored using candlestick formations. For example, a bearish engulfing pattern near a Chandelier Exit resistance level offers confluence, adding confidence to the signal to close or short the trade.
By combining the Chandelier Exit with other tools, traders ensure that they are not relying on a single indicator. This layered approach can reduce the likelihood of false signals and improve overall trading accuracy.
Practical Use Case:
Imagine a trader enters a long position, and the price moves favorably. Using the Chandelier Exit, the trader sets the initial stop level at 2.5x ATR below the highest close. As the price continues to rise, the stop level follows the price, locking in profits. If the market suddenly turns, the price crossing below the stop level signals an exit, helping the trader preserve gains. With extension levels, the trader can further refine exits, adjusting based on their risk tolerance and market conditions.
Good luck and I hope that you can find a place in your tool bag to use this dynamic indicator 🙏
在腳本中搜尋"the strat"
VIDYA ProTrend Multi-Tier ProfitHello! This time is about a trend-following system.
VIDYA is quite an interesting indicator that adjusts dynamically to market volatility, making it more responsive to price changes compared to traditional moving averages. Balancing adaptability and precision, especially with the more aggressive short trade settings, challenged me to fine-tune the strategy for a variety of market conditions.
█ Introduction and How it is Different
The "VIDYA ProTrend Multi-Tier Profit" strategy is a trend-following system that combines the VIDYA (Variable Index Dynamic Average) indicator with Bollinger Bands and a multi-step take-profit mechanism.
Unlike traditional trend strategies, this system allows for more adaptive profit-taking, adjusting for long and short positions through distinct ATR-based and percentage-based targets. The innovation lies in its dynamic multi-tier approach to profit-taking, especially for short trades, where more aggressive percentages are applied using a multiplier. This flexibility helps adapt to various market conditions by optimizing trade management and profit allocation based on market volatility and trend strength.
BTCUSD 6hr performance
█ Strategy, How it Works: Detailed Explanation
The core of the "VIDYA ProTrend Multi-Tier Profit" strategy lies in the dual VIDYA indicators (fast and slow) that analyze price trends while accounting for market volatility. These indicators work alongside Bollinger Bands to filter trade entries and exits.
🔶 VIDYA Calculation
The VIDYA indicator is calculated using the following formula:
Smoothing factor (𝛼):
alpha = 2 / (Length + 1)
VIDYA formula:
VIDYA(t) = alpha * k * Price(t) + (1 - alpha * k) * VIDYA(t-1)
Where:
k = |Chande Momentum Oscillator (MO)| / 100
🔶 Bollinger Bands as a Volatility Filter
Bollinger Bands are calculated using a rolling mean and standard deviation of price over a specified period:
Upper Band:
BB_upper = MA + (K * stddev)
Lower Band:
BB_lower = MA - (K * stddev)
Where:
MA is the moving average,
K is the multiplier (typically 2), and
stddev is the standard deviation of price over the Bollinger Bands length.
These bands serve as volatility filters to identify potential overbought or oversold conditions, aiding in the entry and exit logic.
🔶 Slope Calculation for VIDYA
The slopes of both fast and slow VIDYAs are computed to assess the momentum and direction of the trend. The slope for a given VIDYA over its length is:
Slope = (VIDYA(t) - VIDYA(t-n)) / n
Where:
n is the length of the lookback period. Positive slope indicates bullish momentum, while negative slope signals bearish momentum.
LOCAL picture
🔶 Entry and Exit Conditions
- Long Entry: Occurs when the price moves above the slow VIDYA and the fast VIDYA is trending upward. Bollinger Bands confirm the signal when the price crosses the upper band, indicating bullish strength.
- Short Entry: Happens when the price drops below the slow VIDYA and the fast VIDYA trends downward. The signal is confirmed when the price crosses the lower Bollinger Band, showing bearish momentum.
- Exit: Based on VIDYA slopes flattening or reversing, or when the price hits specific ATR or percentage-based profit targets.
🔶 Multi-Step Take Profit Mechanism
The strategy incorporates three levels of take profit for both long and short trades:
- ATR-based Take Profit: Each step applies a multiple of the ATR (Average True Range) to the entry price to define the exit point.
The first level of take profit (long):
TP_ATR1_long = Entry Price + (2.618 * ATR)
etc.
█ Trade Direction
The strategy offers flexibility in defining the trading direction:
- Long: Only long trades are considered based on the criteria for upward trends.
- Short: Only short trades are initiated in bearish trends.
- Both: The strategy can take both long and short trades depending on the market conditions.
█ Usage
To use the strategy effectively:
- Adjust the VIDYA lengths (fast and slow) based on your preference for trend sensitivity.
- Use Bollinger Bands as a filter for identifying potential breakout or reversal scenarios.
- Enable the multi-step take profit feature to manage positions dynamically, allowing for partial exits as the price reaches specified ATR or percentage levels.
- Leverage the short trade multiplier for more aggressive take profit levels in bearish markets.
This strategy can be applied to different asset classes, including equities, forex, and cryptocurrencies. Adjust the input parameters to suit the volatility and characteristics of the asset being traded.
█ Default Settings
The default settings for this strategy have been designed for moderate to trending markets:
- Fast VIDYA Length (10): A shorter length for quick responsiveness to price changes. Increasing this length will reduce noise but may delay signals.
- Slow VIDYA Length (30): The slow VIDYA is set longer to capture broader market trends. Shortening this value will make the system more reactive to smaller price swings.
- Minimum Slope Threshold (0.05): This threshold helps filter out weak trends. Lowering the threshold will result in more trades, while raising it will restrict trades to stronger trends.
Multi-Step Take Profit Settings
- ATR Multipliers (2.618, 5.0, 10.0): These values define how far the price should move before taking profit. Larger multipliers widen the profit-taking levels, aiming for larger trend moves. In higher volatility markets, these values might be adjusted downwards.
- Percentage Levels (3%, 8%, 17%): These percentage levels define how much the price must move before taking profit. Increasing the percentages will capture larger moves, while smaller percentages offer quicker exits.
- Short TP Multiplier (1.5): This multiplier applies more aggressive take profit levels for short trades. Adjust this value based on the aggressiveness of your short trade management.
Each of these settings directly impacts the performance and risk profile of the strategy. Shorter VIDYA lengths and lower slope thresholds will generate more trades but may result in more whipsaws. Higher ATR multipliers or percentage levels can delay profit-taking, aiming for larger trends but risking partial gains if the trend reverses too early.
Williams %R StrategyThe Williams %R Strategy implemented in Pine Script™ is a trading system based on the Williams %R momentum oscillator. The Williams %R indicator, developed by Larry Williams in 1973, is designed to identify overbought and oversold conditions in a market, helping traders time their entries and exits effectively (Williams, 1979). This particular strategy aims to capitalize on short-term price reversals in the S&P 500 (SPY) by identifying extreme values in the Williams %R indicator and using them as trading signals.
Strategy Rules:
Entry Signal:
A long position is entered when the Williams %R value falls below -90, indicating an oversold condition. This threshold suggests that the market may be near a short-term bottom, and prices are likely to reverse or rebound in the short term (Murphy, 1999).
Exit Signal:
The long position is exited when:
The current close price is higher than the previous day’s high, or
The Williams %R indicator rises above -30, indicating that the market is no longer oversold and may be approaching an overbought condition (Wilder, 1978).
Technical Analysis and Rationale:
The Williams %R is a momentum oscillator that measures the level of the close relative to the high-low range over a specific period, providing insight into whether an asset is trading near its highs or lows. The indicator values range from -100 (most oversold) to 0 (most overbought). When the value falls below -90, it indicates an oversold condition where a reversal is likely (Achelis, 2000). This strategy uses this oversold threshold as a signal to initiate long positions, betting on mean reversion—an established principle in financial markets where prices tend to revert to their historical averages (Jegadeesh & Titman, 1993).
Optimization and Performance:
The strategy allows for an adjustable lookback period (between 2 and 25 days) to determine the range used in the Williams %R calculation. Empirical tests show that shorter lookback periods (e.g., 2 days) yield the most favorable outcomes, with profit factors exceeding 2. This finding aligns with studies suggesting that shorter timeframes can effectively capture short-term momentum reversals (Fama, 1970; Jegadeesh & Titman, 1993).
Scientific Context:
Mean Reversion Theory: The strategy’s core relies on mean reversion, which suggests that prices fluctuate around a mean or average value. Research shows that such strategies, particularly those using oscillators like Williams %R, can exploit these temporary deviations (Poterba & Summers, 1988).
Behavioral Finance: The overbought and oversold conditions identified by Williams %R align with psychological factors influencing trading behavior, such as herding and panic selling, which often create opportunities for price reversals (Shiller, 2003).
Conclusion:
This Williams %R-based strategy utilizes a well-established momentum oscillator to time entries and exits in the S&P 500. By targeting extreme oversold conditions and exiting when these conditions revert or exceed historical ranges, the strategy aims to capture short-term gains. Scientific evidence supports the effectiveness of short-term mean reversion strategies, particularly when using indicators sensitive to momentum shifts.
References:
Achelis, S. B. (2000). Technical Analysis from A to Z. McGraw Hill.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
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.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
Williams, L. (1979). How I Made One Million Dollars… Last Year… Trading Commodities. Windsor Books.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
This explanation provides a scientific and evidence-based perspective on the Williams %R trading strategy, aligning it with fundamental principles in technical analysis and behavioral finance.
Gold Scalping Strategy with Precise EntriesThe Gold Scalping Strategy with Precise Entries is designed to take advantage of short-term price movements in the gold market (XAU/USD). This strategy uses a combination of technical indicators and chart patterns to identify precise buy and sell opportunities during times of consolidation and trend continuation.
Key Elements of the Strategy:
Exponential Moving Averages (EMAs):
50 EMA: Used as the shorter-term moving average to detect the recent price trend.
200 EMA: Used as the longer-term moving average to determine the overall market trend.
Trend Identification:
A bullish trend is identified when the 50 EMA is above the 200 EMA.
A bearish trend is identified when the 50 EMA is below the 200 EMA.
Average True Range (ATR):
ATR (14) is used to calculate the market's volatility and to set a dynamic stop loss based on recent price movements. Higher ATR values indicate higher volatility.
ATR helps define a suitable stop-loss distance from the entry point.
Relative Strength Index (RSI):
RSI (14) is used as a momentum oscillator to detect overbought or oversold conditions.
However, in this strategy, the RSI is primarily used as a consolidation filter to look for neutral zones (between 45 and 55), which may indicate a potential breakout or trend continuation after a consolidation phase.
Engulfing Patterns:
Bullish Engulfing: A bullish signal is generated when the current candle fully engulfs the previous bearish candle, indicating potential upward momentum.
Bearish Engulfing: A bearish signal is generated when the current candle fully engulfs the previous bullish candle, signaling potential downward momentum.
Precise Entry Conditions:
Long (Buy):
The 50 EMA is above the 200 EMA (bullish trend).
The RSI is between 45 and 55 (neutral/consolidation zone).
A bullish engulfing pattern occurs.
The price closes above the 50 EMA.
Short (Sell):
The 50 EMA is below the 200 EMA (bearish trend).
The RSI is between 45 and 55 (neutral/consolidation zone).
A bearish engulfing pattern occurs.
The price closes below the 50 EMA.
Take Profit and Stop Loss:
Take Profit: A fixed 20-pip target (where 1 pip = 0.10 movement in gold) is used for each trade.
Stop Loss: The stop-loss is dynamically set based on the ATR, ensuring that it adapts to current market volatility.
Visual Signals:
Buy and sell signals are visually plotted on the chart using green and red labels, indicating precise points of entry.
Advantages of This Strategy:
Trend Alignment: The strategy ensures that trades are taken in the direction of the overall trend, as indicated by the 50 and 200 EMAs.
Volatility Adaptation: The use of ATR allows the stop loss to adapt to the current market conditions, reducing the risk of premature exits in volatile markets.
Precise Entries: The combination of engulfing patterns and the neutral RSI zone provides a high-probability entry signal that captures momentum after consolidation.
Quick Scalping: With a fixed 20-pip profit target, the strategy is designed to capture small price movements quickly, which is ideal for scalping.
This strategy can be applied to lower timeframes (such as 1-minute, 5-minute, or 15-minute charts) for frequent trade opportunities in gold trading, making it suitable for day traders or scalpers. However, proper risk management should always be used due to the inherent volatility of gold.
Simple RSI stock Strategy [1D] The "Simple RSI Stock Strategy " is designed to long-term traders. Strategy uses a daily time frame to capitalize on signals generated by the Relative Strength Index (RSI) and the Simple Moving Average (SMA). This strategy is suitable for low-leverage trading environments and focuses on identifying potential buy opportunities when the market is oversold, while incorporating strong risk management with both dynamic and static Stop Loss mechanisms.
This strategy is recommended for use with a relatively small amount of capital and is best applied by diversifying across multiple stocks in a strong uptrend, particularly in the S&P 500 stock market. It is specifically designed for equities, and may not perform well in other markets such as commodities, forex, or cryptocurrencies, where different market dynamics and volatility patterns apply.
Indicators Used in the Strategy:
1. RSI (Relative Strength Index):
- The RSI is a momentum oscillator used to identify overbought and oversold conditions in the market.
- This strategy enters long positions when the RSI drops below the oversold level (default: 30), indicating a potential buying opportunity.
- It focuses on oversold conditions but uses a filter (SMA 200) to ensure trades are only made in the context of an overall uptrend.
2. SMA 200 (Simple Moving Average):
- The 200-period SMA serves as a trend filter, ensuring that trades are only executed when the price is above the SMA, signaling a bullish market.
- This filter helps to avoid entering trades in a downtrend, thereby reducing the risk of holding positions in a declining market.
3. ATR (Average True Range):
- The ATR is used to measure market volatility and is instrumental in setting the Stop Loss.
- By multiplying the ATR value by a custom multiplier (default: 1.5), the strategy dynamically adjusts the Stop Loss level based on market volatility, allowing for flexibility in risk management.
How the Strategy Works:
Entry Signals:
The strategy opens long positions when RSI indicates that the market is oversold (below 30), and the price is above the 200-period SMA. This ensures that the strategy buys into potential market bottoms within the context of a long-term uptrend.
Take Profit Levels:
The strategy defines three distinct Take Profit (TP) levels:
TP 1: A 5% from the entry price.
TP 2: A 10% from the entry price.
TP 3: A 15% from the entry price.
As each TP level is reached, the strategy closes portions of the position to secure profits: 33% of the position is closed at TP 1, 66% at TP 2, and 100% at TP 3.
Visualizing Target Points:
The strategy provides visual feedback by plotting plotshapes at each Take Profit level (TP 1, TP 2, TP 3). This allows traders to easily see the target profit levels on the chart, making it easier to monitor and manage positions as they approach key profit-taking areas.
Stop Loss Mechanism:
The strategy uses a dual Stop Loss system to effectively manage risk:
ATR Trailing Stop: This dynamic Stop Loss adjusts based on the ATR value and trails the price as the position moves in the trader’s favor. If a price reversal occurs and the market begins to trend downward, the trailing stop closes the position, locking in gains or minimizing losses.
Basic Stop Loss: Additionally, a fixed Stop Loss is set at 25%, limiting potential losses. This basic Stop Loss serves as a safeguard, automatically closing the position if the price drops 25% from the entry point. This higher Stop Loss is designed specifically for low-leverage trading, allowing more room for market fluctuations without prematurely closing positions.
to determine the level of stop loss and target point I used a piece of code by RafaelZioni, here is the script from which a piece of code was taken
Together, these mechanisms ensure that the strategy dynamically manages risk while offering robust protection against significant losses in case of sharp market downturns.
The position size has been estimated by me at 75% of the total capital. For optimal capital allocation, a recommended value based on the Kelly Criterion, which is calculated to be 59.13% of the total capital per trade, can also be considered.
Enjoy !
D_Rock's MA IndicatorD_Rock's Moving Average Indicator
This is an indicator version of my strategy linked here
**Overview:**
The basic concept of this indicator is to generate a signal when a faster/shorter length moving average crosses over (for Longs) or crosses under (for Shorts) a medium/longer length moving average. All of which are customizable. This indicator can work on any timeframe, however the daily is the timeframe used for the default settings and screenshots, as it was designed to be a multi-day swing strategy. Once a signal has been confirmed with a candle close, based on user options, the strategy is to enter the trade on the open of the next candle.
The crossover strategy is nothing new to trading, but what can make this strategy unique and helpful, is the addition of further confirmation points before a signal is generated along with the ability to show multiple moving averages on the chart if you choose. Each moving average pair can also be turned into a "cloud" instead of the traditional lines, for additional viewing preferences. Just about everything visual can be toggled on/off as well.
This indicator is a Trend (MA) indicator with optional confirmation points using a Momentum (MACD) indicator. While a Volume-based indicator is not shown here, one could consider using their favorite from that category to further compliment the signal idea.
If you would like to see the backtesting results for your favorite moving average crossover/under, please see my strategy version linked here .
Shoutout given to Ripster's Clouds Indicator as pieces of that code were taken and modified to create both the Cloud visualization effects, and the Moving Average Pair Plots that are implemented in this strategy.
MOVING AVERAGE OPTIONS
Select between and change the length & type of up to 5 pairs (10 total) of moving averages
The "Show Cloud-x" option will display a fill color between the "a" and "b" pairs
All moving averages lines can be toggled on/off in the "Style" tab, as well as adjusting their colors.
Visualization features do not affect calculations, meaning you could have all or nothing on the chart and the strategy will still produce results
SIGNAL CHOICES
Choose the fast/shorter length MA and the medium/longer length MA to determine the entry signal
CONFIRMATION OPTIONS
Both of these have customizable values and can be toggled on/off
A candle close over a slower/much longer length moving average
An additional cross-over (cross-under for Shorts) on the MACD indicator using default MACD values. While the MACD indicator is not necessary to have on the chart, it can help to add that for visualization. The calculations will perform whether the indicator is on the chart or not.
ADDITIONAL PLOTS
MACD (Moving Average Convergence/Divergence):
- The MACD is an optional confirmation indicator for this strategy.
- Plotting the indicator is not necessary for the strategy to work, but it can be helpful to visually see the status and position of the MACD if this feature is enabled in the strategy
- This helps to identify if there is also momentum behind the entry signal
ADX + Volume Strategy### Strategy Description: ADX and Volume-Based Trading Strategy
This strategy is designed to identify strong market trends using the **Average Directional Index (ADX)** and confirm trading signals with **Volume**. The idea behind the strategy is to enter trades only when the market shows a strong trend (as indicated by ADX) and when the price movement is supported by high trading volume. This combination helps filter out weaker signals and provides more reliable entries into positions.
### Key Indicators:
1. **ADX (Average Directional Index)**:
- **Purpose**: ADX is a technical indicator that measures the strength of a trend, regardless of its direction (up or down).
- **Usage**: The strategy uses ADX to determine whether the market is trending strongly. If ADX is above a certain threshold (default is 25), it indicates that a strong trend is present.
- **Directional Indicators**:
- **DI+ (Directional Indicator Plus)**: Indicates the strength of the upward price movement.
- **DI- (Directional Indicator Minus)**: Indicates the strength of the downward price movement.
- ADX does not indicate the direction of the trend but confirms that a trend exists. DI+ and DI- are used to determine the direction.
2. **Volume**:
- **Purpose**: Volume is a key indicator for confirming the strength of a price movement. High volume suggests that a large number of market participants are supporting the movement, making it more likely to continue.
- **Usage**: The strategy compares the current volume to the 20-period moving average of the volume. The trade signal is confirmed if the current volume is greater than the average volume by a specified **Volume Multiplier** (default multiplier is 1.5). This ensures that the trade is supported by strong market participation.
### Strategy Logic:
#### **Entry Conditions:**
1. **Long Position** (Buy):
- **ADX** is above the threshold (default is 25), indicating a strong trend.
- **DI+ > DI-**, signaling that the market is trending upward.
- The **current volume** is greater than the 20-period average volume multiplied by the **Volume Multiplier** (e.g., 1.5), indicating that the upward price movement is backed by sufficient market activity.
2. **Short Position** (Sell):
- **ADX** is above the threshold (default is 25), indicating a strong trend.
- **DI- > DI+**, signaling that the market is trending downward.
- The **current volume** is greater than the 20-period average volume multiplied by the **Volume Multiplier** (e.g., 1.5), indicating that the downward price movement is backed by strong selling activity.
#### **Exit Conditions**:
- Positions are closed when the opposite signal appears:
- **For long positions**: Close when the short conditions are met (ADX still above the threshold, DI- > DI+, and the volume condition holds).
- **For short positions**: Close when the long conditions are met (ADX still above the threshold, DI+ > DI-, and the volume condition holds).
### Parameters:
- **ADX Period**: The period used to calculate ADX (default is 14). This controls how sensitive the ADX is to price movements.
- **ADX Threshold**: The minimum ADX value required for the strategy to consider the market trend as strong (default is 25). Higher values focus on stronger trends.
- **Volume Multiplier**: This parameter adjusts how much higher the current volume needs to be compared to the 20-period moving average for the signal to be valid. A value of 1.5 means the current volume must be 50% higher than the average volume.
### Example Trade Flow:
1. **Long Trade Example**:
- ADX > 25, confirming a strong trend.
- DI+ > DI-, confirming that the trend direction is upward.
- The current volume is 50% higher than the 20-period average volume (multiplied by 1.5).
- **Action**: Enter a long position.
2. **Short Trade Example**:
- ADX > 25, confirming a strong trend.
- DI- > DI+, confirming that the trend direction is downward.
- The current volume is 50% higher than the 20-period average volume.
- **Action**: Enter a short position.
### Strengths of the Strategy:
- **Trend Filtering**: The strategy ensures that trades are only taken when the market is trending strongly (confirmed by ADX) and that the price movement is supported by high volume, reducing the likelihood of false signals.
- **Volume Confirmation**: Using volume as confirmation provides an additional layer of reliability, as volume spikes often accompany sustained price moves.
- **Dual Signal Confirmation**: Both trend strength (ADX) and volume conditions must be met for a trade, making the strategy more robust.
### Weaknesses of the Strategy:
- **Limited Effectiveness in Range-Bound Markets**: Since the strategy relies on strong trends, it may underperform in sideways or non-trending markets where ADX stays below the threshold.
- **Lagging Nature of ADX**: ADX is a lagging indicator, which means that it may confirm the trend after it has already begun, potentially leading to late entries.
- **Volume Requirement**: In low-volume markets, the volume multiplier condition may not be met often, leading to fewer trade opportunities.
### Customization:
- **Adjust the ADX Threshold**: You can raise the threshold if you want to focus only on very strong trends, or lower it to capture moderate trends.
- **Adjust the Volume Multiplier**: You can change the multiplier to be more or less strict. A higher multiplier (e.g., 2.0) will require a stronger volume spike to confirm the signal, while a lower multiplier (e.g., 1.2) will allow more trades with weaker volume confirmation.
### Summary:
This ADX and Volume strategy is ideal for traders who want to follow strong trends while ensuring that the trend is supported by high trading volume. By combining a trend strength filter (ADX) and volume confirmation, the strategy aims to increase the probability of entering profitable trades while reducing the number of false signals. However, it may underperform in range-bound markets or in markets with low volume.
Trend Following ADX + Parabolic SAR### Strategy Description: Trend Following using **ADX** and **Parabolic SAR**
This strategy is designed to follow market trends using two popular indicators: **Average Directional Index (ADX)** and **Parabolic SAR**. The strategy attempts to enter trades when the market shows a strong trend (using ADX) and confirms the trend direction using the Parabolic SAR. Here's a breakdown:
### Key Indicators:
1. **ADX (Average Directional Index)**:
- **Purpose**: ADX measures the strength of a trend, regardless of direction.
- **Usage**: The strategy uses ADX to confirm that the market is trending. When ADX is above a certain threshold (e.g., 25), it indicates a strong trend.
- **Directional Indicators**:
- **DI+ (Directional Indicator Plus)**: Indicates upward movement strength.
- **DI- (Directional Indicator Minus)**: Indicates downward movement strength.
2. **Parabolic SAR**:
- **Purpose**: Parabolic SAR is a trend-following indicator used to identify potential reversals in the price direction.
- **Usage**: It provides specific price points above or below which the strategy confirms buy or sell signals.
### Strategy Logic:
#### **Entry Conditions**:
1. **Long Position** (Buy):
- **ADX** is above the threshold (default: 25), indicating a strong trend.
- **DI+ > DI-**, indicating the upward trend is stronger than the downward.
- The price is above the **Parabolic SAR** level, confirming the upward trend.
2. **Short Position** (Sell):
- **ADX** is above the threshold (default: 25), indicating a strong trend.
- **DI- > DI+**, indicating the downward trend is stronger than the upward.
- The price is below the **Parabolic SAR** level, confirming the downward trend.
#### **Exit Conditions**:
- Positions are closed when an opposite signal is detected.
- For example, if a long position is open and the conditions for a short position are met, the long position is closed, and a short position is opened.
### Parameters:
1. **ADX Period**: Defines the length of the period for the ADX calculation (default: 14).
2. **ADX Threshold**: The minimum value of ADX to confirm a strong trend (default: 25).
3. **Parabolic SAR Start**: The initial step for the SAR (default: 0.02).
4. **Parabolic SAR Increment**: The step increment for SAR (default: 0.02).
5. **Parabolic SAR Max**: The maximum step for SAR (default: 0.2).
### Example Trade Flow:
#### **Long Trade**:
1. ADX > 25, confirming a strong trend.
2. DI+ > DI-, indicating the market is trending upward.
3. The price is above the Parabolic SAR, confirming the upward direction.
4. **Action**: Enter a long (buy) position.
5. Exit the long position when a short signal is triggered (i.e., DI- > DI+, price below Parabolic SAR).
#### **Short Trade**:
1. ADX > 25, confirming a strong trend.
2. DI- > DI+, indicating the market is trending downward.
3. The price is below the Parabolic SAR, confirming the downward direction.
4. **Action**: Enter a short (sell) position.
5. Exit the short position when a long signal is triggered (i.e., DI+ > DI-, price above Parabolic SAR).
### Strengths of the Strategy:
- **Trend-Following**: It performs well in markets with strong trends, whether upward or downward.
- **Dual Confirmation**: The combination of ADX and Parabolic SAR reduces false signals by ensuring both trend strength and direction are considered before entering a trade.
### Weaknesses:
- **Range-Bound Markets**: This strategy may perform poorly in choppy, non-trending markets because both ADX and SAR are trend-following indicators.
- **Lagging Nature**: Since both ADX and SAR are lagging indicators, the strategy may enter trades after the trend has already started, potentially missing early profits.
### Customization:
- **ADX Threshold**: You can increase the threshold if you only want to trade in very strong trends, or lower it to capture more moderate trends.
- **SAR Parameters**: Adjusting the SAR `start`, `increment`, and `max` values will make the Parabolic SAR more or less sensitive to price changes.
### Summary:
This strategy combines the ADX and Parabolic SAR to take advantage of strong market trends. By confirming both trend strength (ADX) and trend direction (Parabolic SAR), it aims to enter high-probability trades in trending markets while minimizing false signals. However, it may struggle in sideways or non-trending markets.
For Educational purposes only !!!
Fibonacci Swing Trading BotStrategy Overview for "Fibonacci Swing Trading Bot"
Strategy Name: Fibonacci Swing Trading Bot
Version: Pine Script v5
Purpose: This strategy is designed for swing traders who want to leverage Fibonacci retracement levels and candlestick patterns to enter and exit trades on higher time frames.
Key Components:
1. Multiple Timeframe Analysis:
The strategy uses a customizable timeframe for analysis. You can choose between 4hour, daily, weekly, or monthly time frames to fit your preferred trading horizon. The high and low-price data is retrieved from the selected timeframe to identify swing points.
2. Fibonacci Retracement Levels:
The script calculates two key Fibonacci retracement levels:
0.618: A common level where price often retraces before resuming its trend.
0.786: A deeper retracement level, often used to identify stronger support/resistance areas.
These levels are dynamically plotted on the chart based on the highest high and lowest low over the last 50 bars of the selected timeframe.
3. Candlestick Based Entry Signals:
The strategy uses candlestick patterns as the only indicator for trade entries:
Bullish Candle: A green candle (close > open) that forms between the 0.618 retracement level and the swing high.
Bearish Candle: A red candle (close < open) that forms between the 0.786 retracement level and the swing low.
When these candlestick patterns align with the Fibonacci levels, the script triggers buy or sell signals.
4. Risk Management:
Stop Loss: The stop loss is set at 1% below the entry price for long trades and 1% above the entry price for short trades. This tight risk management ensures controlled losses.
Take Profit: The strategy uses a 2:1 risk-to-reward ratio. The take profit is automatically calculated based on this ratio relative to the stop loss.
5. Buy/Sell Logic:
Buy Signal: Triggered when a bullish candle forms above the 0.618 retracement level and below the swing high. The bot then places a long position.
Sell Signal: Triggered when a bearish candle forms below the 0.786 retracement level and above the swing low. The bot then places a short position.
The stop loss and take profit levels are automatically managed once the trade is placed.
Strengths of This Strategy:
Swing Trading Focus: The strategy is ideal for swing traders, targeting longer-term price moves that can take days or weeks to play out.
Simple Yet Effective Indicators: By only relying on Fibonacci retracement levels and basic candlestick patterns, the strategy avoids complexity while capitalizing on well-known support and resistance zones.
Automated Risk Management: The built-in stop loss and take profit mechanism ensures trades are protected, adhering to a strict 2:1 risk/reward ratio.
Multiple Timeframe Analysis: The script adapts to various market conditions by allowing users to switch between different timeframes (4hour, daily, weekly, monthly), giving traders flexibility.
Strategy Use Cases:
Retracement Traders: Traders who focus on entering the market at key retracement levels (0.618 and 0.786) will find this strategy especially useful.
Trend Reversal Traders: The strategy’s reliance on candlestick formations at Fibonacci levels helps traders spot potential reversals in price trends.
Risk Conscious Traders: With its 1% risk per trade and 2:1 risk/reward ratio, the strategy is ideal for traders who prioritize risk management in their trades.
analytics_tablesLibrary "analytics_tables"
📝 Description
This library provides the implementation of several performance-related statistics and metrics, presented in the form of tables.
The metrics shown in the afforementioned tables where developed during the past years of my in-depth analalysis of various strategies in an atempt to reason about the performance of each strategy.
The visualization and some statistics where inspired by the existing implementations of the "Seasonality" script, and the performance matrix implementations of @QuantNomad and @ZenAndTheArtOfTrading scripts.
While this library is meant to be used by my strategy framework "Template Trailing Strategy (Backtester)" script, I wrapped it in a library hoping this can be usefull for other community strategy scripts that will be released in the future.
🤔 How to Guide
To use the functionality this library provides in your script you have to import it first!
Copy the import statement of the latest release by pressing the copy button below and then paste it into your script. Give a short name to this library so you can refer to it later on. The import statement should look like this:
import jason5480/analytics_tables/1 as ant
There are three types of tables provided by this library in the initial release. The stats table the metrics table and the seasonality table.
Each one shows different kinds of performance statistics.
The table UDT shall be initialized once using the `init()` method.
They can be updated using the `update()` method where the updated data UDT object shall be passed.
The data UDT can also initialized and get updated on demend depending on the use case
A code example for the StatsTable is the following:
var ant.StatsData statsData = ant.StatsData.new()
statsData.update(SideStats.new(), SideStats.new(), 0)
if (barstate.islastconfirmedhistory or (barstate.isrealtime and barstate.isconfirmed))
var statsTable = ant.StatsTable.new().init(ant.getTablePos('TOP', 'RIGHT'))
statsTable.update(statsData)
A code example for the MetricsTable is the following:
var ant.StatsData statsData = ant.StatsData.new()
statsData.update(ant.SideStats.new(), ant.SideStats.new(), 0)
if (barstate.islastconfirmedhistory or (barstate.isrealtime and barstate.isconfirmed))
var metricsTable = ant.MetricsTable.new().init(ant.getTablePos('BOTTOM', 'RIGHT'))
metricsTable.update(statsData, 10)
A code example for the SeasonalityTable is the following:
var ant.SeasonalData seasonalData = ant.SeasonalData.new().init(Seasonality.monthOfYear)
seasonalData.update()
if (barstate.islastconfirmedhistory or (barstate.isrealtime and barstate.isconfirmed))
var seasonalTable = ant.SeasonalTable.new().init(seasonalData, ant.getTablePos('BOTTOM', 'LEFT'))
seasonalTable.update(seasonalData)
🏋️♂️ Please refer to the "EXAMPLE" regions of the script for more advanced and up to date code examples!
Special thanks to @Mrcrbw for the proposal to develop this library and @DCNeu for the constructive feedback 🏆.
getTablePos(ypos, xpos)
Get table position compatible string
Parameters:
ypos (simple string) : The position on y axise
xpos (simple string) : The position on x axise
Returns: The position to be passed to the table
method init(this, pos, height, width, positiveTxtColor, negativeTxtColor, neutralTxtColor, positiveBgColor, negativeBgColor, neutralBgColor)
Initialize the stats table object with the given colors in the given position
Namespace types: StatsTable
Parameters:
this (StatsTable) : The stats table object
pos (simple string) : The table position string
height (simple float) : The height of the table as a percentage of the charts height. By default, 0 auto-adjusts the height based on the text inside the cells
width (simple float) : The width of the table as a percentage of the charts height. By default, 0 auto-adjusts the width based on the text inside the cells
positiveTxtColor (simple color) : The text color when positive
negativeTxtColor (simple color) : The text color when negative
neutralTxtColor (simple color) : The text color when neutral
positiveBgColor (simple color) : The background color with transparency when positive
negativeBgColor (simple color) : The background color with transparency when negative
neutralBgColor (simple color) : The background color with transparency when neutral
method init(this, pos, height, width, neutralBgColor)
Initialize the metrics table object with the given colors in the given position
Namespace types: MetricsTable
Parameters:
this (MetricsTable) : The metrics table object
pos (simple string) : The table position string
height (simple float) : The height of the table as a percentage of the charts height. By default, 0 auto-adjusts the height based on the text inside the cells
width (simple float) : The width of the table as a percentage of the charts width. By default, 0 auto-adjusts the width based on the text inside the cells
neutralBgColor (simple color) : The background color with transparency when neutral
method init(this, seas)
Initialize the seasonal data
Namespace types: SeasonalData
Parameters:
this (SeasonalData) : The seasonal data object
seas (simple Seasonality) : The seasonality of the matrix data
method init(this, data, pos, maxNumOfYears, height, width, extended, neutralTxtColor, neutralBgColor)
Initialize the seasonal table object with the given colors in the given position
Namespace types: SeasonalTable
Parameters:
this (SeasonalTable) : The seasonal table object
data (SeasonalData) : The seasonality data of the table
pos (simple string) : The table position string
maxNumOfYears (simple int) : The maximum number of years that fit into the table
height (simple float) : The height of the table as a percentage of the charts height. By default, 0 auto-adjusts the height based on the text inside the cells
width (simple float) : The width of the table as a percentage of the charts width. By default, 0 auto-adjusts the width based on the text inside the cells
extended (simple bool) : The seasonal table with extended columns for performance
neutralTxtColor (simple color) : The text color when neutral
neutralBgColor (simple color) : The background color with transparency when neutral
method update(this, wins, losses, numOfInconclusiveExits)
Update the strategy info data of the strategy
Namespace types: StatsData
Parameters:
this (StatsData) : The strategy statistics object
wins (SideStats)
losses (SideStats)
numOfInconclusiveExits (int) : The number of inconclusive trades
method update(this, stats, positiveTxtColor, negativeTxtColor, negativeBgColor, neutralBgColor)
Update the stats table object with the given data
Namespace types: StatsTable
Parameters:
this (StatsTable) : The stats table object
stats (StatsData) : The stats data to update the table
positiveTxtColor (simple color) : The text color when positive
negativeTxtColor (simple color) : The text color when negative
negativeBgColor (simple color) : The background color with transparency when negative
neutralBgColor (simple color) : The background color with transparency when neutral
method update(this, stats, buyAndHoldPerc, positiveTxtColor, negativeTxtColor, positiveBgColor, negativeBgColor)
Update the metrics table object with the given data
Namespace types: MetricsTable
Parameters:
this (MetricsTable) : The metrics table object
stats (StatsData) : The stats data to update the table
buyAndHoldPerc (float) : The buy and hold percetage
positiveTxtColor (simple color) : The text color when positive
negativeTxtColor (simple color) : The text color when negative
positiveBgColor (simple color) : The background color with transparency when positive
negativeBgColor (simple color) : The background color with transparency when negative
method update(this)
Update the seasonal data based on the season and eon timeframe
Namespace types: SeasonalData
Parameters:
this (SeasonalData) : The seasonal data object
method update(this, data, positiveTxtColor, negativeTxtColor, neutralTxtColor, positiveBgColor, negativeBgColor, neutralBgColor, timeBgColor)
Update the seasonal table object with the given data
Namespace types: SeasonalTable
Parameters:
this (SeasonalTable) : The seasonal table object
data (SeasonalData) : The seasonal cell data to update the table
positiveTxtColor (simple color) : The text color when positive
negativeTxtColor (simple color) : The text color when negative
neutralTxtColor (simple color) : The text color when neutral
positiveBgColor (simple color) : The background color with transparency when positive
negativeBgColor (simple color) : The background color with transparency when negative
neutralBgColor (simple color) : The background color with transparency when neutral
timeBgColor (simple color) : The background color of the time gradient
SideStats
Object that represents the strategy statistics data of one side win or lose
Fields:
numOf (series int)
sumFreeProfit (series float)
freeProfitStDev (series float)
sumProfit (series float)
profitStDev (series float)
sumGain (series float)
gainStDev (series float)
avgQuantityPerc (series float)
avgCapitalRiskPerc (series float)
avgTPExecutedCount (series float)
avgRiskRewardRatio (series float)
maxStreak (series int)
StatsTable
Object that represents the stats table
Fields:
table (series table) : The actual table
rows (series int) : The number of rows of the table
columns (series int) : The number of columns of the table
StatsData
Object that represents the statistics data of the strategy
Fields:
wins (SideStats)
losses (SideStats)
numOfInconclusiveExits (series int)
avgFreeProfitStr (series string)
freeProfitStDevStr (series string)
lossFreeProfitStDevStr (series string)
avgProfitStr (series string)
profitStDevStr (series string)
lossProfitStDevStr (series string)
avgQuantityStr (series string)
MetricsTable
Object that represents the metrics table
Fields:
table (series table) : The actual table
rows (series int) : The number of rows of the table
columns (series int) : The number of columns of the table
SeasonalData
Object that represents the seasonal table dynamic data
Fields:
seasonality (series Seasonality)
eonToMatrixRow (map)
numOfEons (series int)
mostRecentMatrixRow (series int)
balances (matrix)
returnPercs (matrix)
maxDDs (matrix)
eonReturnPercs (array)
eonCAGRs (array)
eonMaxDDs (array)
SeasonalTable
Object that represents the seasonal table
Fields:
table (series table) : The actual table
headRows (series int) : The number of head rows of the table
headColumns (series int) : The number of head columns of the table
eonRows (series int) : The number of eon rows of the table
seasonColumns (series int) : The number of season columns of the table
statsRows (series int)
statsColumns (series int) : The number of stats columns of the table
rows (series int) : The number of rows of the table
columns (series int) : The number of columns of the table
extended (series bool) : Whether the table has additional performance statistics
Bitcoin CME-Spot Z-Spread - Strategy [presentTrading]This time is a swing trading strategy! It measures the sentiment of the Bitcoin market through the spread of CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. By applying Bollinger Bands to the spread, the strategy seeks to capture mean-reversion opportunities when prices deviate significantly from their historical norms
█ Introduction and How it is Different
The Bitcoin CME-Spot Bollinger Bands Strategy is designed to capture mean-reversion opportunities by exploiting the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. The strategy uses Bollinger Bands to detect when the spread between these two correlated assets has deviated significantly from its historical norm, signaling potential overbought or oversold conditions.
What sets this strategy apart is its focus on spread trading between futures and spot markets rather than price-based indicators. By applying Bollinger Bands to the spread rather than individual prices, the strategy identifies price inefficiencies across markets, allowing traders to take advantage of the natural reversion to the mean that often occurs in these correlated assets.
BTCUSD 8hr Performance
█ Strategy, How It Works: Detailed Explanation
The strategy relies on Bollinger Bands to assess the volatility and relative deviation of the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. Bollinger Bands consist of a moving average and two standard deviation bands, which help measure how much the spread deviates from its historical mean.
🔶 Spread Calculation:
The spread is calculated by subtracting the Bitfinex spot price from the CME Bitcoin futures price:
Spread = CME Price - Bitfinex Price
This spread represents the difference between the futures and spot markets, which may widen or narrow based on supply and demand dynamics in each market. By analyzing the spread, the strategy can detect when prices are too far apart (potentially overbought or oversold), indicating a trading opportunity.
🔶 Bollinger Bands Calculation:
The Bollinger Bands for the spread are calculated using a simple moving average (SMA) and the standard deviation of the spread over a defined period.
1. Moving Average (SMA):
The simple moving average of the spread (mu_S) over a specified period P is calculated as:
mu_S = (1/P) * sum(S_i from i=1 to P)
Where S_i represents the spread at time i, and P is the lookback period (default is 200 bars). The moving average provides a baseline for the normal spread behavior.
2. Standard Deviation:
The standard deviation (sigma_S) of the spread is calculated to measure the volatility of the spread:
sigma_S = sqrt((1/P) * sum((S_i - mu_S)^2 from i=1 to P))
3. Upper and Lower Bollinger Bands:
The upper and lower Bollinger Bands are derived by adding and subtracting a multiple of the standard deviation from the moving average. The number of standard deviations is determined by a user-defined parameter k (default is 2.618).
- Upper Band:
Upper Band = mu_S + (k * sigma_S)
- Lower Band:
Lower Band = mu_S - (k * sigma_S)
These bands provide a dynamic range within which the spread typically fluctuates. When the spread moves outside of these bands, it is considered overbought or oversold, potentially offering trading opportunities.
Local view
🔶 Entry Conditions:
- Long Entry: A long position is triggered when the spread crosses below the lower Bollinger Band, indicating that the spread has become oversold and is likely to revert upward.
Spread < Lower Band
- Short Entry: A short position is triggered when the spread crosses above the upper Bollinger Band, indicating that the spread has become overbought and is likely to revert downward.
Spread > Upper Band
🔶 Risk Management and Profit-Taking:
The strategy incorporates multi-step take profits to lock in gains as the trade moves in favor. The position is gradually reduced at predefined profit levels, reducing risk while allowing part of the trade to continue running if the price keeps moving favorably.
Additionally, the strategy uses a hold period exit mechanism. If the trade does not hit any of the take-profit levels within a certain number of bars, the position is closed automatically to avoid excessive exposure to market risks.
█ Trade Direction
The trade direction is based on deviations of the spread from its historical norm:
- Long Trade: The strategy enters a long position when the spread crosses below the lower Bollinger Band, signaling an oversold condition where the spread is expected to narrow.
- Short Trade: The strategy enters a short position when the spread crosses above the upper Bollinger Band, signaling an overbought condition where the spread is expected to widen.
These entries rely on the assumption of mean reversion, where extreme deviations from the average spread are likely to revert over time.
█ Usage
The Bitcoin CME-Spot Bollinger Bands Strategy is ideal for traders looking to capitalize on price inefficiencies between Bitcoin futures and spot markets. It’s especially useful in volatile markets where large deviations between futures and spot prices occur.
- Market Conditions: This strategy is most effective in correlated markets, like CME futures and spot Bitcoin. Traders can adjust the Bollinger Bands period and standard deviation multiplier to suit different volatility regimes.
- Backtesting: Before deployment, backtesting the strategy across different market conditions and timeframes is recommended to ensure robustness. Adjust the take-profit steps and hold periods to reflect the trader’s risk tolerance and market behavior.
█ Default Settings
The default settings provide a balanced approach to spread trading using Bollinger Bands but can be adjusted depending on market conditions or personal trading preferences.
🔶 Bollinger Bands Period (200 bars):
This defines the number of bars used to calculate the moving average and standard deviation for the Bollinger Bands. A longer period smooths out short-term fluctuations and focuses on larger, more significant trends. Adjusting the period affects the responsiveness of the strategy:
- Shorter periods (e.g., 100 bars): Makes the strategy more reactive to short-term market fluctuations, potentially generating more signals but increasing the risk of false positives.
- Longer periods (e.g., 300 bars): Focuses on longer-term trends, reducing the frequency of trades and focusing only on significant deviations.
🔶 Standard Deviation Multiplier (2.618):
The multiplier controls how wide the Bollinger Bands are around the moving average. By default, the bands are set at 2.618 standard deviations away from the average, ensuring that only significant deviations trigger trades.
- Higher multipliers (e.g., 3.0): Require a more extreme deviation to trigger trades, reducing trade frequency but potentially increasing the accuracy of signals.
- Lower multipliers (e.g., 2.0): Make the bands narrower, increasing the number of trade signals but potentially decreasing their reliability.
🔶 Take-Profit Levels:
The strategy has four take-profit levels to gradually lock in profits:
- Level 1 (3%): 25% of the position is closed at a 3% profit.
- Level 2 (8%): 20% of the position is closed at an 8% profit.
- Level 3 (14%): 15% of the position is closed at a 14% profit.
- Level 4 (21%): 10% of the position is closed at a 21% profit.
Adjusting these take-profit levels affects how quickly profits are realized:
- Lower take-profit levels: Capture gains more quickly, reducing risk but potentially cutting off larger profits.
- Higher take-profit levels: Let trades run longer, aiming for bigger gains but increasing the risk of price reversals before profits are locked in.
🔶 Hold Days (20 bars):
The strategy automatically closes the position after 20 bars if none of the take-profit levels are hit. This feature prevents trades from being held indefinitely, especially if market conditions are stagnant. Adjusting this:
- Shorter hold periods: Reduce the duration of exposure, minimizing risks from market changes but potentially closing trades too early.
- Longer hold periods: Allow trades to stay open longer, increasing the chance for mean reversion but also increasing exposure to unfavorable market conditions.
By understanding how these default settings affect the strategy’s performance, traders can optimize the Bitcoin CME-Spot Bollinger Bands Strategy to their preferences, adapting it to different market environments and risk tolerances.
High/Low Breakout Statistical Analysis StrategyThis Pine Script strategy is designed to assist in the statistical analysis of breakout systems on a monthly, weekly, or daily timeframe. It allows the user to select whether to open a long or short position when the price breaks above or below the respective high or low for the chosen timeframe. The user can also define the holding period for each position in terms of bars.
Core Functionality:
Breakout Logic:
The strategy triggers trades based on price crossing over (for long positions) or crossing under (for short positions) the high or low of the selected period (daily, weekly, or monthly).
Timeframe Selection:
A dropdown menu enables the user to switch between the desired timeframe (monthly, weekly, or daily).
Trade Direction:
Another dropdown allows the user to select the type of trade (long or short) depending on whether the breakout occurs at the high or low of the timeframe.
Holding Period:
Once a trade is opened, it is automatically closed after a user-defined number of bars, making it useful for analyzing how breakout signals perform over short-term periods.
This strategy is intended exclusively for research and statistical purposes rather than real-time trading, helping users to assess the behavior of breakouts over different timeframes.
Relevance of Breakout Systems:
Breakout trading systems, where trades are executed when the price moves beyond a significant price level such as the high or low of a given period, have been extensively studied in financial literature for their potential predictive power.
Momentum and Trend Following:
Breakout strategies are a form of momentum-based trading, exploiting the tendency of prices to continue moving in the direction of a strong initial movement after breaching a critical support or resistance level. According to academic research, momentum strategies, including breakouts, can produce returns above average market returns when applied consistently. For example, Jegadeesh and Titman (1993) demonstrated that stocks that performed well in the past 3-12 months continued to outperform in the subsequent months, suggesting that price continuation patterns, like breakouts, hold value .
Market Efficiency Hypothesis:
While the Efficient Market Hypothesis (EMH) posits that markets are generally efficient, and it is difficult to outperform the market through technical strategies, some studies show that in less liquid markets or during specific times of market stress, breakout systems can capitalize on temporary inefficiencies. Taylor (2005) and other researchers have found instances where breakout systems can outperform the market under certain conditions.
Volatility and Breakouts:
Breakouts are often linked to periods of increased volatility, which can generate trading opportunities. Coval and Shumway (2001) found that periods of heightened volatility can make breakouts more significant, increasing the likelihood that price trends will follow the breakout direction. This correlation between volatility and breakout reliability makes it essential to study breakouts across different timeframes to assess their potential profitability .
In summary, this breakout strategy offers an empirical way to study price behavior around key support and resistance levels. It is useful for researchers and traders aiming to statistically evaluate the effectiveness and consistency of breakout signals across different timeframes, contributing to broader research on momentum and market behavior.
References:
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Fama, E. F., & French, K. R. (1996). Multifactor Explanations of Asset Pricing Anomalies. Journal of Finance, 51(1), 55-84.
Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.
Coval, J. D., & Shumway, T. (2001). Expected Option Returns. Journal of Finance, 56(3), 983-1009.
Simultaneous INSIDE Bar Break IndicatorSimultaneous Inside Bar Break Indicator (SIBBI) for The Strat Community
Overview:
The Simultaneous Inside Bar Break Indicator (SIBBI) is designed to help traders using The Strat methodology identify one of the most powerful breakout patterns: the Simultaneous Inside Bar Break across multiple symbols. This indicator detects when all four user-selected symbols form inside bars on the previous candle and then break those inside bars in the same direction (either bullish or bearish) on the current candle.
Inside bars represent consolidation periods where price action does not break the high or low of the previous candle. When a simultaneous break occurs across multiple symbols, this often signals a strong move in the market, making this a key actionable signal in The Strat trading strategy.
Key Features:
Multi-Symbol Analysis: You can track up to four different symbols simultaneously. By default, the indicator comes with SPY, QQQ, IWM, and DIA, but you can modify these to track any other assets or symbols.
Inside Bar Detection: The indicator checks whether all four symbols have inside bars on the previous candle. It only triggers when all symbols meet this condition, making it a highly specific and reliable signal.
Simultaneous Break Detection: Once all symbols have inside bars, the indicator waits for a breakout in the same direction across all four symbols. A simultaneous bullish break (prices breaking above the previous candle’s high) triggers a green label, while a simultaneous bearish break (prices breaking below the previous candle’s low) triggers a red label.
Dynamic Label Timeframe: The indicator dynamically adjusts the timeframe in the label based on the user’s selected timeframe. This allows traders to know precisely which timeframe the break is occurring on. If the user selects "Chart Timeframe," the indicator will evolve with the current chart's timeframe, making it more versatile.
Timeframe Flexibility: The indicator can be set to analyze any timeframe—15-minute, 30-minute, 60-minute, daily, weekly, and so on. It only works for the specific timeframe you set it to in the settings. If set to "Chart Timeframe," the label will adapt dynamically based on the timeframe you are currently viewing.
Customizable Labels: The user can choose the size of the labels (tiny, small, or normal), ensuring that the visual output is tailored to individual preferences and chart layouts.
Best Use Case:
The Simultaneous Inside Bar Break Indicator is particularly powerful when applied to multiple timeframes. Here’s how to use it for maximum impact:
Multi-Timeframe Setup: Set the indicator on various timeframes (e.g., 15-minute, 30-minute, 60-minute, and daily) across multiple charts. This allows you to monitor different timeframes and identify when lower timeframe breaks trigger potential moves on higher timeframes.
Anticipating Strong Moves: When a simultaneous inside bar break occurs on one timeframe (e.g., 30-minute), keep an eye on the higher timeframes (e.g., 60-minute or daily) to see if those timeframes also break. This stacking of inside bar breaks can signal powerful market moves.
Higher Conviction Signals: The indicator is designed to provide high-conviction signals. Since it requires all four symbols to break in the same direction simultaneously, it reduces false signals and focuses on higher probability setups, which is crucial for traders using The Strat to time their trades effectively.
How the Indicator Works:
Inside Bar Formation: The indicator first checks that all four selected symbols had inside bars in the previous bar (i.e., the current high and low are contained within the previous bar’s high and low).
Simultaneous Break Detection: After detecting inside bars, the indicator checks if all four symbols break out in the same direction—bullish (breaking above the previous bar’s high) or bearish (breaking below the previous bar’s low).
Label Display: When a simultaneous inside bar break occurs, a label is plotted on the chart—either green for a bullish break (below the candle) or red for a bearish break (above the candle). The label will display the timeframe you set in the settings (e.g., "IBSB 60" for a 60-minute break).
Chart Timeframe Option: If you prefer, you can set the indicator to evolve with the chart’s current timeframe. In this mode, the label will not show a specific timeframe but will still display the simultaneous inside bar break when it occurs.
Recommendations for Usage:
Focus on Multiple Timeframes: The Strat methodology is all about understanding the relationship between different timeframes. Use this indicator on multiple timeframes to get a better picture of potential moves.
Pair with Other Strat Techniques: This indicator is most powerful when combined with other Strat tools, such as broadening formations, timeframe continuity, and actionable signals (e.g., 2-2 reversals). The simultaneous inside bar break can help confirm or invalidate other signals.
Customize Symbols and Timeframes: Although the default symbols are SPY, QQQ, IWM, and DIA, feel free to replace them with symbols more relevant to your trading. This indicator works well across equities, indices, futures, and forex pairs.
How to Set It Up:
Select Symbols: Choose four symbols that you want to track. These can be index ETFs (like SPY and QQQ), individual stocks, or any other tradable instruments.
Set Timeframe: In the indicator’s settings, choose a specific timeframe (e.g., 15-minute, 30-minute, daily). The label will reflect the selected timeframe, making it clear which time-based break you are seeing.
Optional - Chart Timeframe Mode: If you want the indicator to adapt to the chart’s current timeframe, select the "Chart Timeframe" option in the settings. The indicator will plot the breaks without showing a specific timeframe in the label.
Customize Label Size: Depending on your chart layout and personal preference, you can adjust the size of the labels (tiny, small, or normal) in the settings.
Conclusion:
The Simultaneous Inside Bar Break Indicator is a powerful tool for traders using The Strat methodology, offering a highly specific and reliable signal that can indicate potential large market moves. By monitoring multiple symbols and timeframes, you can gain deeper insight into the market's behavior and act with greater confidence. This indicator is ideal for traders looking to catch high-conviction moves and align their trades with broader market continuity.
Note: The indicator works best when paired with multi-timeframe analysis, allowing you to see how breaks on lower timeframes might influence larger trends. For traders who prefer simplicity, setting it to the "Chart Timeframe" mode offers flexibility while maintaining the core benefits of this indicator.
Higher Time Frame Strat [QuantVue]The Higher Time Frame Strat Indicator is a tool that helps traders visualize and analyze price action from a higher timeframe (HTF) on their current chart. It applies the Strat method, a trading strategy focused on identifying key price action setups by observing how current price bars relate to previous ones. This helps in understanding the market's structure and determining potential trading opportunities based on higher timeframe data.
Key Concepts:
Strat Basics:
Type 1 Bar (Inside Bar): The current bar's high is lower than the previous bar's high, and its low is higher than the previous bar's low. This signifies a consolidation, or indecision, as the price is contained within the previous bar's range.
Type 2 Bar (Directional Bar): The current bar either breaks above the previous bar's high (bullish) or stays above the previous bar's low (bearish), indicating a continuation in the price direction.
Type 3 Bar (Outside Bar): The current bar breaks both above the previous bar's high and below the previous bar's low, showing volatility and a potential reversal.
Higher Timeframe Visualization:
The indicator uses a user-defined higher timeframe (default: 1 hour) and plots the last three higher timeframe candles on the current chart.
Strat Classification:
When a new higher timeframe candle forms, the indicator draws a semi-transparent box around the candle's range (high to low), along with the Strat type label. This provides a visual cue to the trader about the structure of the newly formed candle and how it fits into the overall market movement.
The script classifies each higher timeframe candle as one of the Strat types (1, 2, or 3). Based on the relationship between the current candle and the previous candle's high/low, it assigns a label ("1", "2", or "3"), helping traders quickly identify the price action setup on the higher timeframe.
How to Use the Indicator:
Trend Continuation: Look for Type 2 bars, which indicate a continuation in the current trend. For example, a Type 2 up suggests the price is breaking above the previous high, potentially signaling further upward movement.
Reversals: Type 3 bars show increased volatility, where the price breaks both above and below the previous bar's range. This could indicate a reversal, so be prepared for a potential change in direction.
Consolidation: Inside bars (Type 1) signify a tightening range and can signal the beginning of a breakout once the price moves outside of the previous bar's high or low.
By combining these price action concepts with the visualization of higher timeframe data, traders can potentially get earlier entry and exits as a higher timeframe set up forms.
Ichimoku Crosses_RSI_AITIchimoku Crosser_RSI_AIT
Overview
The "Ichimoku Cloud Crosses_AIT" strategy is a technical trading strategy that combines the Ichimoku Cloud components with the Relative Strength Index (RSI) to generate trade signals. This strategy leverages the crossovers of the Tenkan-sen and Kijun-sen lines of the Ichimoku Cloud, along with RSI levels, to identify potential entry and exit points for long and short trades. This guide explains the strategy components, conditions, and how to use it effectively in your trading.
1. Strategy Parameters
User Inputs
Tenkan-sen Period (tenkanLength): Default value is 21. This is the period used to calculate the Tenkan-sen line (conversion line) of the Ichimoku Cloud.
Kijun-sen Period (kijunLength): Default value is 120. This is the period used to calculate the Kijun-sen line (base line) of the Ichimoku Cloud.
Senkou Span B Period (senkouBLength): Default value is 52. This is the period used to calculate the Senkou Span B line (leading span B) of the Ichimoku Cloud.
RSI Period (rsiLength): Default value is 14. This period is used to calculate the Relative Strength Index (RSI).
RSI Long Entry Level (rsiLongLevel): Default value is 60. This level indicates the minimum RSI value for a long entry signal.
RSI Short Entry Level (rsiShortLevel): Default value is 40. This level indicates the maximum RSI value for a short entry signal.
2. Strategy Components
Ichimoku Cloud
Tenkan-sen: A short-term trend indicator calculated as the simple moving average (SMA) of the highest high and the lowest low over the Tenkan-sen period.
Kijun-sen: A medium-term trend indicator calculated as the SMA of the highest high and the lowest low over the Kijun-sen period.
Senkou Span A: Calculated as the average of the Tenkan-sen and Kijun-sen, plotted 26 periods ahead.
Senkou Span B: Calculated as the SMA of the highest high and lowest low over the Senkou Span B period, plotted 26 periods ahead.
Chikou Span: The closing price plotted 26 periods behind.
Relative Strength Index (RSI)
RSI: A momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is used to identify overbought or oversold conditions.
3. Entry and Exit Conditions
Entry Conditions
Long Entry:
The Tenkan-sen crosses above the Kijun-sen (bullish crossover).
The RSI value is greater than or equal to the rsiLongLevel.
Short Entry:
The Tenkan-sen crosses below the Kijun-sen (bearish crossover).
The RSI value is less than or equal to the rsiShortLevel.
Exit Conditions
Exit Long Position: The Tenkan-sen crosses below the Kijun-sen.
Exit Short Position: The Tenkan-sen crosses above the Kijun-sen.
4. Visual Representation
Tenkan-sen Line: Plotted on the chart. The color changes based on its relation to the Kijun-sen (green if above, red if below) and is displayed with a line width of 2.
Kijun-sen Line: Plotted as a white line with a line width of 1.
Entry Arrows:
Long Entry: Displayed as a yellow triangle below the bar.
Short Entry: Displayed as a fuchsia triangle above the bar.
5. How to Use
Apply the Strategy: Apply the "Ichimoku Cloud Crosses_AIT" strategy to your chart in TradingView.
Configure Parameters: Adjust the strategy parameters (Tenkan-sen, Kijun-sen, Senkou Span B, and RSI settings) according to your trading preferences.
Interpret the Signals:
Long Entry: A yellow triangle appears below the bar when a long entry signal is generated.
Short Entry: A fuchsia triangle appears above the bar when a short entry signal is generated.
Monitor Open Positions: The strategy automatically exits positions based on the defined conditions.
Backtesting and Live Trading: Use the strategy for backtesting and live trading. Adjust risk management settings in the strategy properties as needed.
Conclusion
The "Ichimoku Cloud Crosses_AIT" strategy uses Ichimoku Cloud crossovers and RSI to generate trading signals. This strategy aims to capture market trends and potential reversals, providing a structured way to enter and exit trades. Make sure to backtest and optimize the strategy parameters to suit your trading style and market conditions before using it in a live trading environment.
Connors RSI with Down GapThe Connors RSI with Down Gap indicator is a technical tool designed to support Larry Connors' Terror Gap Strategy, which is part of his broader framework outlined in the book "Buy the Fear, Sell the Greed: 7 Behavioral Quant Strategies for Traders." This specific indicator integrates the ConnorsRSI calculation with a focus on detecting down gaps in price, providing insights into moments when panic selling may occur.
The ConnorsRSI
ConnorsRSI is a composite indicator developed by Larry Connors that combines three core components:
RSI: A short-term relative strength index measuring the speed and magnitude of price changes.
Streak RSI: Tracks consecutive up or down closes to assess momentum.
Percent Rank: Evaluates how the current close ranks in relation to past prices.
When combined, these three elements provide a nuanced view of short-term overbought or oversold conditions. ConnorsRSI readings below a certain threshold (commonly 30 or lower) suggest that the asset has been heavily sold, indicating potential exhaustion of selling pressure.
Behavioral Finance Insights
The Terror Gap Strategy is grounded in principles from behavioral finance, which studies how psychological factors affect market participants' decision-making. Specifically, the indicator exploits the fear and irrational behavior that often arise when traders face persistent losses, especially after a down gap. According to behavioral finance theories like prospect theory (Kahneman & Tversky, 1979), people tend to overreact to losses, leading to panic selling. This creates opportunities for contrarian traders who understand the psychology behind these market movements.
The ConnorsRSI with Down Gap indicator works because it identifies:
Overextended selling through the ConnorsRSI, where persistent price declines result in low RSI values (indicating panic).
Gap down days, where the opening price is below the previous day’s close, typically amplifying the sense of loss and fear for traders already in losing positions.
Why This Indicator Works
The psychology of losses makes traders more prone to selling during periods of fear, especially when confronted with a gap down after sustained price declines. This indicator, by combining ConnorsRSI with down gaps, offers a quantitative way to spot these moments of panic. Traders can take advantage of these signals to enter positions when the market is in a state of fear, often when there is potential for a reversion to the mean.
Indicator Mechanics
In the current implementation:
The ConnorsRSI is calculated using three components: a short-term RSI, streak RSI, and percent rank.
When the ConnorsRSI drops below a user-defined lower threshold, the indicator highlights oversold conditions.
If there is a down gap (open price lower than the previous close) and the ConnorsRSI is below the threshold, a label is displayed, signaling a potential opportunity to buy.
Practical Use and Application
For traders looking to implement the Terror Gap Strategy, this indicator provides a clear visual cue (via background coloring and labels) when conditions are ripe for a contrarian trade. It can be particularly useful for traders who thrive on taking advantage of fear-driven sell-offs.
However, to fully understand and apply this strategy effectively, it is recommended to purchase Larry Connors' book "Buy the Fear, Sell the Greed." The book provides detailed explanations of how to execute the strategy with precision, including insights into exit conditions, scaling into positions, and managing risk.
Conclusion
The ConnorsRSI with Down Gap indicator combines quantitative analysis with behavioral finance principles to exploit fear-driven market behavior. By utilizing this tool within a disciplined trading strategy, traders can potentially profit from temporary market inefficiencies caused by panic selling.
References
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Connors, L. (2013). Buy the Fear, Sell the Greed: 7 Behavioral Quant Strategies for Traders.
This indicator can be a valuable asset, but understanding its proper use within a broader strategy framework is essential. Purchasing Connors' book is a recommended step toward mastering the approach.
TPS Short Strategy by Larry ConnersThe TPS Short strategy aims to capitalize on extreme overbought conditions in an ETF by employing a scaling-in approach when certain technical indicators signal potential reversals. The strategy is designed to short the ETF when it is deemed overextended, based on the Relative Strength Index (RSI) and moving averages.
Components:
200-Day Simple Moving Average (SMA):
Purpose: Acts as a long-term trend filter. The ETF must be below its 200-day SMA to be eligible for shorting.
Rationale: The 200-day SMA is widely used to gauge the long-term trend of a security. When the price is below this moving average, it is often considered to be in a downtrend (Tushar S. Chande & Stanley Kroll, "The New Technical Trader: Boost Your Profit by Plugging Into the Latest Indicators").
2-Period RSI:
Purpose: Measures the speed and change of price movements to identify overbought conditions.
Criteria: Short 10% of the position when the 2-period RSI is above 75 for two consecutive days.
Rationale: A high RSI value (above 75) indicates that the ETF may be overbought, which could precede a price reversal (J. Welles Wilder, "New Concepts in Technical Trading Systems").
Scaling-In Mechanism:
Purpose: Gradually increase the short position as the ETF price rises beyond previous entry points.
Scaling Strategy:
20% more when the price is higher than the first entry.
30% more when the price is higher than the second entry.
40% more when the price is higher than the third entry.
Rationale: This incremental approach allows for an increased position size in a worsening trend, potentially increasing profitability if the trend continues to align with the strategy’s premise (Marty Schwartz, "Pit Bull: Lessons from Wall Street's Champion Day Trader").
Exit Conditions:
Criteria: Close all positions when the 2-period RSI drops below 30 or the 10-day SMA crosses above the 30-day SMA.
Rationale: A low RSI value (below 30) suggests that the ETF may be oversold and could be poised for a rebound, while the SMA crossover indicates a potential change in the trend (Martin J. Pring, "Technical Analysis Explained").
Risks and Considerations:
Market Risk:
The strategy assumes that the ETF will continue to decline once shorted. However, markets can be unpredictable, and price movements might not align with the strategy's expectations, especially in a volatile market (Nassim Nicholas Taleb, "The Black Swan: The Impact of the Highly Improbable").
Scaling Risks:
Scaling into a position as the price increases may increase exposure to adverse price movements. This method can amplify losses if the market moves against the position significantly before any reversal occurs.
Liquidity Risk:
Depending on the ETF’s liquidity, executing large trades in increments might affect the price and increase trading costs. It is crucial to ensure that the ETF has sufficient liquidity to handle large trades without significant slippage (James Altucher, "Trade Like a Hedge Fund").
Execution Risk:
The strategy relies on timely execution of trades based on specific conditions. Delays or errors in order execution can impact performance, especially in fast-moving markets.
Technical Indicator Limitations:
Technical indicators like RSI and SMA are based on historical data and may not always predict future price movements accurately. They can sometimes produce false signals, leading to potential losses if used in isolation (John Murphy, "Technical Analysis of the Financial Markets").
Conclusion
The TPS Short strategy utilizes a combination of long-term trend filtering, overbought conditions, and incremental shorting to potentially profit from price reversals. While the strategy has a structured approach and leverages well-known technical indicators, it is essential to be aware of the inherent risks, including market volatility, liquidity issues, and potential limitations of technical indicators. As with any trading strategy, thorough backtesting and risk management are crucial to its successful implementation.
Sygnały Long/Short z SL i TPChoosing the Best Timeframe for Your Trading Strategy
The ideal timeframe for your trading strategy depends on several factors, including your trading style, risk preferences, and the goals of your strategy. Here’s a guide to different timeframes and their applications:
Timeframes and Their Uses:
Short-Term Timeframes (e.g., 5-minute, 15-minute):
Advantages: Provide more frequent signals and allow for quick responses to market changes. Ideal for day traders who prefer short, rapid trades.
Disadvantages: Can generate more false signals and be more susceptible to market noise. Requires more frequent attention and monitoring.
Medium-Term Timeframes (e.g., 1-hour, 4-hour):
Advantages: Offer fewer false signals compared to shorter timeframes. Suitable for swing traders looking to capture short-term trends.
Disadvantages: Fewer signals compared to shorter timeframes. Requires less frequent monitoring.
Long-Term Timeframes (e.g., daily, weekly):
Advantages: Provide more stable signals and are less affected by market noise. Ideal for long-term investors and those trading based on trends.
Disadvantages: Fewer signals, which may be less frequent but more reliable. Requires longer confirmation times.
Recommendation for Your Strategy:
For a strategy based on moving averages (MA) and generating long/short signals, the 5-minute and 15-minute timeframes might be suitable if:
You are a day trader and want to generate multiple signals per day.
You prefer quick responses to price changes and want to execute trades within a shorter timeframe.
For more stable signals and fewer false signals:
1-hour or 4-hour timeframes might be more appropriate.
Testing and Optimization:
Test Different Timeframes: See how your strategy performs on various timeframes to find the one that works best for you.
Adjust Parameters: Modify the lengths of the short and long SMAs, as well as the SL and TP levels, to fit the chosen timeframe.
How to Test:
Add the script to your chart on different timeframes on TradingView.
Observe the effectiveness and accuracy of the signals.
Adjust settings based on results and personal preferences.
Summary:
There isn’t a single “best” timeframe as it depends on your trading style and objectives. Start by testing on shorter timeframes if you are interested in day trading, and then explore how the strategy performs on longer timeframes for more stable signals.
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.
Potential Divergence Checker#### Key Features
1. Potential Divergence Signals:
Potential divergences can signal a change in price movement before it occurs. This indicator identifies potential divergences instead of waiting for full confirmation, allowing it to detect signs of divergence earlier than traditional methods. This provides more flexible entry points and can act as a broader filter for potential setups.
2. Exposing Signals for External Use:
One of its advanced features is the ability to expose signals for use in other scripts. This allows users to integrate divergence signals and related entry/exit points into custom strategies or automated systems.
3. Custom Entry/Exit Timing Based on Years and Days:
The indicator provides entry and exit signals based on years and days, which could be useful for time-specific market behavior, long-term trades, and back testing.
#### Basic Usage
This indicator can check for all types of potential divergences: bullish, hidden bullish, bearish, hidden bearish. All you need to do is choose the type you want to check for under “DIVERGENCE TYPE” in the settings. On the chart, potential bullish divergences will show up as triangles below the price candles. one the chart potential bearish divergences will show up as upside down triangles above the price candles
#### Signals for Advanced Usage
You can use this indicator as a source in other indicators or strategies using the following information:
“ PD: Bull divergence signal ” will return “1” when a divergence is present and “0” when not present
“ PD: HBull divergence(hidden bull) signal ” will return “1” when a divergence is present and “0” when not present
“ PD: Bear divergence signal ” will return “1” when a divergence is present and “0” when not present
“ PD: HBear divergence(hidden bear) signal ” will return “1” when a divergence is present and “0” when not present
“ PD: enter ” signal will return a “1” when both the days and years criteria in the “entry filter settings” are met and “0” when not met.
“ PD: exit ” signal will return a “1” when the days criteria in the “exit filter settings” are met and “0” when not met.
#### Examples of Using Signals
1. If you are testing a long strategy for Bitcoin and do not want it to run during bear market years(e.g., the second year after a US presidential election), you can enable the “year and day filter for entry,” uncheck the following years in the settings: 2010, 2014, 2018, 2022, 2026, and reference the signal below in our strategy
signal: “ PD: enter ”
2. Let’s say you have a good long strategy, but want to make it a bit more profitable, you can tell the strategy not to run on days where there is potential bearish divergence and have it only run on more profitable days using these signals and the appropriate settings in the indicator
signal: “ PD: Bear divergence signal ” will return a ‘0’ with no bearish divergence present
signal: “ PD: enter ” will return a “1” if the entry falls on a specific, more profitable day chosen in the settings
#### Disclaimer
The "Potential Divergence Checker" indicator is a tool designed to identify potential market signals. It may have bugs and not do what it should do. It is not a guarantee of future trading performance, and users should exercise caution when making trading decisions based on its outputs. Always perform your own research and consider consulting with a financial advisor before making any investment decisions. Trading involves significant risk, and past performance is not indicative of future results.
TRIN (Arms Index) Trading StrategyThe TRIN (Arms Index), also known as the Short-Term Trading Index, is a technical indicator designed to gauge the internal strength or weakness of the market. It compares the number of advancing and declining stocks to the advancing and declining volume (AD Volume). A TRIN value above 1.0 generally indicates bearish market conditions, while a value below 1.0 suggests bullish market sentiment.
Strategy Rules:
Entry Condition (Long Position): When the TRIN value is above 1.0, the strategy enters a long position, indicating that the market may be oversold, and a potential reversal could occur.
Exit Condition: The strategy exits the long position when the closing price is higher than the previous day’s high, signaling a potential rebound in the market.
This strategy aims to capitalize on short-term market inefficiencies by entering trades during periods of potential market weakness and exiting when signs of recovery appear.
How the TRIN Index Works:
The TRIN is calculated as follows:
TRIN=Advancing Issues / Declining IssuesAdvancing Volume / Declining Volume
TRIN=Advancing Volume / Declining VolumeAdvancing Issues / Declining Issues
A TRIN value above 1.0 indicates that the market is potentially oversold (more declining stocks with higher volume), while a value below 1.0 suggests the market may be overbought (more advancing stocks with higher volume) .
Empirical Evidence:
Market Sentiment Indicator: The TRIN has been widely used as a sentiment indicator. Research by Zweig (1997) suggests that extreme TRIN values can serve as a contrarian signal, indicating potential turning points in the market. For instance, a TRIN above 2.0 is often considered a sign of panic selling, which can precede a market bottom .
Overbought/Oversold Conditions: Studies have shown that indicators like TRIN, which measure market breadth and volume, can be effective in identifying overbought and oversold conditions. According to Fama and French (1988), market sentiment indicators that consider both price and volume data can offer insights into future price movements .
Risks and Limitations:
False Signals:
One of the primary risks of using the TRIN-based strategy is the possibility of false signals. A TRIN value above 1.0 does not always guarantee a market rebound, especially in sustained bearish trends. In such cases, the strategy might enter long positions prematurely, leading to losses.
Research by Brock, Lakonishok, and LeBaron (1992) found that while market indicators like TRIN can be useful, they are not foolproof and can generate multiple false positives, particularly in volatile markets .
Market Regimes:
The effectiveness of the TRIN index can vary depending on the market regime. In strongly trending markets, either bullish or bearish, the TRIN may not provide reliable reversal signals, and relying on it could result in trades that go against the prevailing trend. For instance, during strong bear markets, the TRIN may frequently remain above 1.0, leading to multiple losing trades as the market continues to decline.
Short-Term Focus:
The TRIN strategy is inherently short-term focused, aiming to capture quick market reversals. This makes it sensitive to market noise and less effective for longer-term investors. Moreover, short-term trading strategies often require more frequent adjustments and can incur higher transaction costs, which may erode profitability over time.
Liquidity and Execution Risk:
Since the TRIN strategy requires entering and exiting trades based on short-term market movements, it is vulnerable to liquidity and execution risks. In fast-moving markets, the execution of trades may be delayed, leading to slippage and potentially unfavorable entry or exit points.
Conclusion:
The TRIN (Arms Index) Trading Strategy can be an effective tool for traders looking to capitalize on short-term market inefficiencies and potential reversals. However, it is important to recognize the risks associated with this strategy, including false signals, sensitivity to market regimes, and execution risks. Traders should employ proper risk management techniques and consider combining the TRIN with other indicators to improve the robustness of the strategy.
While the TRIN provides valuable insights into market sentiment, it is not a standalone solution and should be used in conjunction with a broader trading plan that takes into account both technical and fundamental analysis.
References:
Arms, Richard W. "Volume Adjusted Moving Averages." Technical Analysis of Stocks & Commodities, 1993.
Zweig, Martin. Winning on Wall Street. Warner Books, 1997.
Fama, Eugene F., and Kenneth R. French. "Permanent and Temporary Components of Stock Prices." Journal of Political Economy, 1988.
Brock, William, Josef Lakonishok, and Blake LeBaron. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns." Journal of Finance, 1992.
Averaging Down Strategy1. Averaging Down:
Definition: "Averaging Down" is a strategy in which an investor buys more shares of a declining asset, thus lowering the average purchase price. The main idea is that, by averaging down, the investor can recover faster when the price eventually rebounds.
Risk Considerations: This strategy assumes that the asset will recover in value. If the price continues to decline, however, the investor may suffer larger losses. Academic research highlights the psychological bias of loss aversion that often leads investors to engage in averaging down, despite the increased risk (Barberis & Huang, 2001).
2. RSI (Relative Strength Index):
Definition: The RSI is a momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is commonly used to identify overbought or oversold conditions. A reading below 30 (or in this case, 35) typically indicates an oversold condition, which might suggest a potential buying opportunity (Wilder, 1978).
Risk Considerations: RSI-based strategies can produce many false signals in range-bound or choppy markets, where prices do not exhibit strong trends. This can lead to multiple losing trades and an overall negative performance (Gencay, 1998).
3. Combination of RSI and Price Movement:
Approach: The combination of RSI for entry signals and price movement (previous day's high) for exit signals aims to capture short-term market reversals. This hybrid approach attempts to balance momentum with price confirmation.
Risk Considerations: While this combination can work well in trending markets, it may struggle in volatile or sideways markets. Additionally, a significant risk of averaging down is that the trader may continue adding to a losing position, which can exacerbate losses if the price keeps falling.
Risk Warnings:
Increased Losses Through Averaging Down:
Averaging down involves buying more of a falling asset, which can increase exposure to downside risk. Studies have shown that this approach can lead to larger losses when markets continue to decline, especially during prolonged bear markets (Statman, 2004).
A key risk is that this strategy may lead to significant capital drawdowns if the price of the asset does not recover as expected. In the worst-case scenario, this can result in a total loss of the invested capital.
False Signals with RSI:
RSI-based strategies are prone to generating false signals, particularly in markets that do not exhibit strong trends. For example, Gencay (1998) found that while RSI can be effective in certain conditions, it often fails in choppy or range-bound markets, leading to frequent stop-outs and drawdowns.
Psychological Bias:
Behavioral finance research suggests that the "Averaging Down" strategy may be influenced by loss aversion, a bias where investors prefer to avoid losses rather than achieve gains (Kahneman & Tversky, 1979). This can lead to poor decision-making, as investors continue to add to losing positions in the hope of a recovery.
Empirical Studies:
Gencay (1998): The study "The Predictability of Security Returns with Simple Technical Trading Rules" found that technical indicators like RSI can provide predictive value in certain markets, particularly in volatile environments. However, they are less reliable in markets that lack clear trends.
Barberis & Huang (2001): Their research on behavioral biases, including loss aversion, explains why investors are often tempted to average down despite the risks, as they attempt to avoid realizing losses.
Statman (2004): In "The Diversification Puzzle," Statman discusses how strategies like averaging down can increase risk exposure without necessarily improving long-term returns, especially if the underlying asset continues to perform poorly.
Conclusion:
The "Averaging Down Strategy with RSI" combines elements of technical analysis with a psychologically-driven averaging down approach. While the strategy may offer opportunities in trending or oversold markets, it carries significant risks, particularly in volatile or declining markets. Traders should be cautious when using this strategy, ensuring they manage risk effectively and avoid overexposure to a losing position.
Black-Scholes option price model & delta hedge strategyBlack-Scholes Option Pricing Model Strategy
The strategy is based on the Black-Scholes option pricing model and allows the calculation of option prices, various option metrics (the Greeks), and the creation of synthetic positions through delta hedging.
ATTENTION!
Trading derivative financial instruments involves high risks. The author of the strategy is not responsible for your financial results! The strategy is not self-sufficient for generating profit! It is created exclusively for constructing a synthetic derivative financial instrument. Also, there might be errors in the script, so use it at your own risk! I would appreciate it if you point out any mistakes in the comments! I would be even more grateful if you send the corrected code!
Application Scope
This strategy can be used for delta hedging short positions in sold options. For example, suppose you sold a call option on Bitcoin on the Deribit exchange with a strike price of $60,000 and an expiration date of September 27, 2024. Using this script, you can create a delta hedge to protect against the risk of loss in the option position if the price of Bitcoin rises.
Another example: Suppose you use staking of altcoins in your strategies, for which options are not available. By using this strategy, you can hedge the risk of a price drop (Put option). In this case, you won't lose money if the underlying asset price increases, unlike with a short futures position.
Another example: You received an airdrop, but your tokens will not be fully unlocked soon. Using this script, you can fully hedge your position and preserve their dollar value by the time the tokens are fully unlocked. And you won't fear the underlying asset price increasing, as the loss in the event of a price rise is limited to the option premium you will pay if you rebalance the portfolio.
Of course, this script can also be used for simple directional trading of momentum and mean reversion strategies!
Key Features and Input Parameters
1. Option settings:
- Style of option: "European vanilla", "Binary", "Asian geometric".
- Type of option: "Call" (bet on the rise) or "Put" (bet on the fall).
- Strike price: the option contract price.
- Expiration: the expiry date and time of the option contract.
2. Market statistic settings:
- Type of price source: open, high, low, close, hl2, hlc3, ohlc4, hlcc4 (using hl2, hlc3, ohlc4, hlcc4 allows smoothing the price in more volatile series).
- Risk-free return symbol: the risk-free rate for the market where the underlying asset is traded. For the cryptocurrency market, the return on the funding rate arbitrage strategy is accepted (a special function is written for its calculation based on the Premium Price).
- Volatility calculation model: realized (standard deviation over a moving period), implied (e.g., DVOL or VIX), or custom (you can specify a specific number in the field below). For the cryptocurrency market, the calculation of implied volatility is implemented based on the product of the realized volatility ratio of the considered asset and Bitcoin to the Bitcoin implied volatility index.
- User implied volatility: fixed implied volatility (used if "Custom" is selected in the "Volatility Calculation Method").
3. Display settings:
- Choose metric: what to display on the indicator scale – the price of the underlying asset, the option price, volatility, or Greeks (all are available).
- Measure: bps (basis points), percent. This parameter allows choosing the unit of measurement for the displayed metric (for all except the Greeks).
4. Trading settings:
- Hedge model: None (do not trade, default), Simple (just open a position for the full volume when the strike price is crossed), Synthetic option (creating a synthetic option based on the Black-Scholes model).
- Position side: Long, Short.
- Position size: the number of units of the underlying asset needed to create the option.
- Strategy start time: the moment in time after which the strategy will start working to create a synthetic option.
- Delta hedge interval: the interval in minutes for rebalancing the portfolio. For example, a value of 5 corresponds to rebalancing the portfolio every 5 minutes.
Post scriptum
My strategy based on the SegaRKO model. Many thanks to the author! Unfortunately, I don't have enough reputation points to include a link to the author in the description. You can find the original model via the link in the code, as well as through the search indicators on the charts by entering the name: "Black-Scholes Option Pricing Model". I have significantly improved the model: the calculation of volatility, risk-free rate and time value of the option have been reworked. The code performance has also been significantly optimized. And the most significant change is the execution, with which you can now trade using this script.
