zavaUnni-bitcoin signals(1day)
📌 This strategy predicts price movements based on trading volume and enters positions accordingly. It calculates the expected price increase based on bullish volume and the expected price decrease based on bearish volume to determine the direction of the position.
Top predicted price based on declining bullish volume: top_ifpricebull
Bottom predicted price based on declining bearish volume: top_ifpricebear
Top predicted price based on increasing bullish volume: bot_ifpricebull
Bottom predicted price based on increasing bearish volume: bot_ifpricebear
Using these four values, the strategy calculates the final maxprice and minprice based on volume. If the price settles above the max value, it indicates an upward trend; if it settles below the min value, it indicates a downward trend.
📌 The indicator does not solely rely on the maxprice and minprice conditions. It incorporates complex and sophisticated analysis by considering average volume and candle size.
During a decline, if the average volume and spread of bullish candles exceed those of bearish candles and the price settles above the max value, a long position is entered.
During a rise, if the average volume and spread of bearish candles exceed those of bullish candles and the price settles below the min value, a short position is entered.
Even if the above conditions are met, if the buying pressure significantly outweighs the selling pressure, the position will be closed, but a reverse position will not be entered.
Reviewing historical data shows that while there are instances where the position switches from long to short immediately, there are also cases where the position is closed and re-entered after a few candles.
📌 Trading volume is one of the most traditional yet essential indicators, accurately reflecting price direction. This strategy, which simultaneously predicts fundamental trading volume and price changes, consistently achieves a profit factor above 3.
Characteristics and Historical Data of the Strategy
🔴 Short position entry: April 11, 2022
🟢 Long position entry after closing short: January 11, 2023
⚫ Short position holding period: 270 days
🟢 Long position entry: October 9, 2020
🔵 Long position exit: November 30, 2019
⚫ Long position holding period: 52 days
🟢 Long position entry: November 30, 2019
🔵 Long position exit: February 22, 2021
⚫ Long position holding period: 84 days
Settings Explanation
🛠️ In the input, you can choose between spot and futures. Buy and sell signals are generated in spot trading, while long and short signals are generated in futures trading.
🌈 You can configure the screen view.
Fibonacci Trend
Falling Fibonacci levels from the top: 382 and 618 levels (Red lines)
Rising Fibonacci levels from the bottom: 382 and 618 levels (Green lines)
When the price stays within the 382 and 618 levels of the falling Fibonacci, the background turns red; when it stays within the 382 and 618 levels of the rising Fibonacci, the background turns green.
Real-time Volume Strength of Bullish and Bearish Candles
Red arrow: Appears when the strength of bearish candles increases
Green arrow: Appears when the strength of bullish candles increases
Cumulative Volume of Bullish and Bearish Candles during the Trend
Cumulative data of falling bullish and bearish candles from the top
Cumulative data of rising bullish and bearish candles from the bottom
Profit Table
Provides annual and monthly profit tables.
Setting Options
You can change the options in the attributes to test different configurations.
📌 Trading Data
Although Binance data starts from 2017, limiting the number of trades to 60 as of July 2024, this does not undermine the validity of the strategy. Binance provides reliable volume data, which is crucial for evaluating the strategy's performance. In contrast, exchanges like Bitstamp may have longer trading histories but insufficient volume to properly assess the strategy's actual performance. A volume-based strategy cannot be reliably tested on an exchange with low trading volume. Therefore, despite the limited number of trades on Binance, its reliable volume data justifies its use for this strategy.
► Backtesting Details:
Timeframe: 1D / Bitcoin / TetherUS
Initial Balance: $50,000 (Enter the initial capital you will invest)
Order Size: 10% (Enter the percentage of your account balance you will trade)
Commission: 0.04% (Enter the trading commission)
Slippage: 10 ticks (Enter the slippage you want to test)
When using the strategy:
📢 Timeframe: While the strategy performs well on timeframes lower than daily, it is particularly profitable on the daily timeframe.
📢 Exchange: It is recommended to use Binance due to its reliable volume data.
📢 This strategy is suitable for traders who have the patience to hold positions for extended periods, as it calculates the size of bullish and bearish candles carefully and does not change positions easily.
📢 Spot trading is recommended over futures, and if using futures, leverage should be limited to a maximum of 2x.
Zmienność
Fractal Breakout Trend Following StrategyOverview
The Fractal Breakout Trend Following Strategy is a trend-following system which utilizes the Willams Fractals and Alligator to execute the long trades on the fractal's breakouts which have a high probability to be the new uptrend phase beginning. This system also uses the normalized Average True Range indicator to filter trades after a large moves, because it's more likely to see the trend continuation after a consolidation period. Strategy can execute only long trades.
Unique Features
Trend and volatility filtering system: Strategy uses Williams Alligator to filter the counter-trend fractals breakouts and normalized Average True Range to avoid the trades after large moves, when volatility is high
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Flexible Risk Management: Users can choose the stop-loss percent (by default = 3%) for trades, but strategy also has the dynamic stop-loss level using down fractals.
Methodology
The strategy places stop order at the last valid fractal breakout level. Validity of this fractal is defined by the Williams Alligator indicator. If at the moment of time when price breaking the last fractal price is higher than Alligator's teeth line (8 period SMA shifted 5 bars in the future) this is a valid breakout. Moreover strategy has the additional volatility filtering system using normalized ATR. It calculates the average normalized ATR for last user-defined number of bars and if this value lower than the user-defined threshold value the long trade is executed.
When trade is opened, script places the stop loss at the price higher of two levels: user defined stop-loss from the position entry price or down fractal validation level. The down fractal is valid with the rule, opposite as the up fractal validation. Price shall break to the downside the last down fractal below the Willians Alligator's teeth line.
Strategy has no fixed take profit. Exit level changes with the down fractal validation level. If price is in strong uptrend trade is going to be active until last down fractal is not valid. Strategy closes trade when price hits the down fractal validation level.
Risk Management
The strategy employs a combined approach to risk management:
It allows positions to ride the trend as long as the price continues to move favorably, aiming to capture significant price movements. It features a user-defined stop-loss parameter to mitigate risks based on individual risk tolerance. By default, this stop-loss is set to a 3% drop from the entry point, but it can be adjusted according to the trader's preferences.
Justification of Methodology
This strategy leverages Williams Fractals to open long trade when price has broken the key resistance level to the upside. This resistance level is the last up fractal and is shall be broken above the Williams Alligator's teeth line to be qualified as the valid breakout according to this strategy. The Alligator filtering increases the probability to avoid the false breakouts against the current trend.
Moreover strategy has an additional filter using Average True Range(ATR) indicator. If average value of ATR for the last user-defined number of bars is lower than user-defined threshold strategy can open the long trade according to open trade condition above. The logic here is following: we want to open trades after period of price consolidation inside the range because before and after a big move price is more likely to be in sideways, but we need a trend move to have a profit.
Another one important feature is how the exit condition is defined. On the one hand, strategy has the user-defined stop-loss (3% below the entry price by default). It's made to give users the opportunity to restrict their losses according to their risk-tolerance. On the other hand, strategy utilizes the dynamic exit level which is defined by down fractal activation. If we assume the breaking up fractal is the beginning of the uptrend, breaking down fractal can be the start of downtrend phase. We don't want to be in long trade if there is a high probability of reversal to the downside. This approach helps to not keep open trade if trend is not developing and hold it if price continues going up.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.05.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.19%
Maximum Single Profit: +24.97%
Net Profit: +3036.90 USDT (+30.37%)
Total Trades: 83 (28.92% win rate)
Profit Factor: 1.953
Maximum Accumulated Loss: 963.98 USDT (-8.29%)
Average Profit per Trade: 36.59 USDT (+1.12%)
Average Trade Duration: 72 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h and higher time frames and the BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
MA MACD BB BackTesterOverview:
This Pine Script™ code provides a comprehensive backtesting tool that combines Moving Average (MA), Moving Average Convergence Divergence (MACD), and Bollinger Bands (BB). It is designed to help traders analyze market trends and make informed trading decisions by testing various strategies over historical data.
Key Features:
1. Customizable Indicators:
Moving Average (MA): Smooths out price data for clearer trend direction.
MACD: Measures trend momentum through MACD Line, Signal Line, and Histogram.
Bollinger Bands (BB): Identifies overbought or oversold conditions with upper and lower bands.
2. Flexible Trading Direction: Choose between long or short positions to adapt to different market conditions.
3. Risk Management: Efficiently allocate your capital with customizable position sizes.
4. Signal Generation:
Buy Signals: Triggered by crossovers for MACD, MA, and BB.
Sell Signals: Triggered by crossunders for MACD, MA, and BB.
5. Automated Trading: Automatically enter and exit trades based on signal conditions and strategy parameters.
How It Works:
1. Indicator Selection: Select your preferred indicator (MA, MACD, BB) and trading direction (Long/Short).
2. Risk Management Configuration: Set the percentage of capital to allocate per position to manage risk effectively.
3.Signal Detection: The algorithm identifies and plots buy/sell signals directly on the chart based on the chosen indicator.
4. Trade Execution: The strategy automatically enters and exits trades based on signal conditions and configured strategy parameters.
Use Cases:
- Backtesting: Evaluate the effectiveness of trading strategies using historical data to understand potential performance.
- Strategy Development: Customize and expand the strategy to incorporate additional indicators or conditions to fit specific trading styles.
ADDONS That Affect Strategy:
1. Indicator Parameters:
Adjustments to the settings of MACD (e.g., fast length, slow length), MA (e.g., length), and BB (e.g., length, multiplier) will directly impact the detection of signals and the strategy's performance.
2. Trading Direction:
Changing the trading direction (Long/Short) will alter the entry and exit conditions based on the detected signals.
3. Risk Management Settings:
Modifying the position size percentage affects capital allocation and overall risk exposure per trade.
ADDONS That Do Not Affect Strategy:
1. Visual Customizations:
Changes to the color, shape, and style of the plotted lines and signals do not impact the core functionality of the strategy but enhance visual clarity.
2. Text and Labels:
Modifying text labels for the signals (such as renaming "Buy MACD" to "MACD Buy Signal") is purely cosmetic and does not influence the strategy’s logic or outcomes.
Notes:
- Customization: The indicator is highly customizable to fit various trading styles and market conditions.
- Risk Management: Adjust position sizes and risk parameters according to your risk tolerance and account size.
- Optimization: Regularly backtest and optimize parameters to adapt to changing market dynamics for better performance.
Getting Started:
-Add the script to your chart.
-Adjust the input parameters to suit your analysis preferences.
-Observe the marked buy and sell signals on your chart to make informed trading decisions.
BBTrend w SuperTrend decision - Strategy [presentTrading]This strategy aims to improve upon the performance of Traidngview's newly published "BB Trend" indicator by incorporating the SuperTrend for better trade execution and risk management. Enjoy :)
█Introduction and How it is Different
The "BBTrend w SuperTrend decision - Strategy " is a trading strategy designed to identify market trends using Bollinger Bands and SuperTrend indicators. What sets this strategy apart is its use of two Bollinger Bands with different lengths to capture both short-term and long-term market trends, providing a more comprehensive view of market dynamics. Additionally, the strategy includes customizable take profit (TP) and stop loss (SL) settings, allowing traders to tailor their risk management according to their preferences.
BTCUSD 4h Long Performance
█ Strategy, How It Works: Detailed Explanation
The BBTrend strategy employs two key indicators: Bollinger Bands and SuperTrend.
🔶 Bollinger Bands Calculation:
- Short Bollinger Bands**: Calculated using a shorter period (default 20).
- Long Bollinger Bands**: Calculated using a longer period (default 50).
- Bollinger Bands use the standard deviation of price data to create upper and lower bands around a moving average.
Upper Band = Middle Band + (k * Standard Deviation)
Lower Band = Middle Band - (k * Standard Deviation)
🔶 BBTrend Indicator:
- The BBTrend indicator is derived from the absolute differences between the short and long Bollinger Bands' lower and upper values.
BBTrend = (|Short Lower - Long Lower| - |Short Upper - Long Upper|) / Short Middle * 100
🔶 SuperTrend Indicator:
- The SuperTrend indicator is calculated using the average true range (ATR) and a multiplier. It helps identify the market trend direction by plotting levels above and below the price, which act as dynamic support and resistance levels. * @EliCobra makes the SuperTrend Toolkit. He is GOAT.
SuperTrend Upper = HL2 + (Factor * ATR)
SuperTrend Lower = HL2 - (Factor * ATR)
The strategy determines market trends by checking if the close price is above or below the SuperTrend values:
- Uptrend: Close price is above the SuperTrend lower band.
- Downtrend: Close price is below the SuperTrend upper band.
Short: 10 Long: 20 std 2
Short: 20 Long: 40 std 2
Short: 20 Long: 40 std 4
█ Trade Direction
The strategy allows traders to choose their trading direction:
- Long: Enter long positions only.
- Short: Enter short positions only.
- Both: Enter both long and short positions based on market conditions.
█ Usage
To use the "BBTrend - Strategy " effectively:
1. Configure Inputs: Adjust the Bollinger Bands lengths, standard deviation multiplier, and SuperTrend settings.
2. Set TPSL Conditions: Choose the take profit and stop loss percentages to manage risk.
3. Choose Trade Direction: Decide whether to trade long, short, or both directions.
4. Apply Strategy: Apply the strategy to your chart and monitor the signals for potential trades.
█ Default Settings
The default settings are designed to provide a balance between sensitivity and stability:
- Short BB Length (20): Captures short-term market trends.
- Long BB Length (50): Captures long-term market trends.
- StdDev (2.0): Determines the width of the Bollinger Bands.
- SuperTrend Length (10): Period for calculating the ATR.
- SuperTrend Factor (12): Multiplier for the ATR to adjust the SuperTrend sensitivity.
- Take Profit (30%): Sets the level at which profits are taken.
- Stop Loss (20%): Sets the level at which losses are cut to manage risk.
Effect on Performance
- Short BB Length: A shorter length makes the strategy more responsive to recent price changes but can generate more false signals.
- Long BB Length: A longer length provides smoother trend signals but may be slower to react to price changes.
- StdDev: Higher values create wider bands, reducing the frequency of signals but increasing their reliability.
- SuperTrend Length and Factor: Shorter lengths and higher factors make the SuperTrend more sensitive, providing quicker signals but potentially more noise.
- Take Profit and Stop Loss: Adjusting these levels affects the risk-reward ratio. Higher take profit percentages can increase gains but may result in fewer closed trades, while higher stop loss percentages can decrease the likelihood of being stopped out but increase potential losses.
Double Vegas SuperTrend Enhanced - Strategy [presentTrading]
█ Introduction and How It Is Different
The "Double Vegas SuperTrend Enhanced" strategy is a sophisticated trading system that combines two Vegas SuperTrend Enhanced. Very Powerful!
Let's celebrate the joy of Children's Day on June 1st! Enjoyyy!
BTCUSD LS performance
The strategy aims to pinpoint market trends with greater accuracy and generate trades that align with the overall market direction.
This approach differentiates itself by integrating volatility adjustments and leveraging the Vegas Channel's width to refine the SuperTrend calculations, resulting in a dynamic and responsive trading system.
Additionally, the strategy incorporates customizable take-profit and stop-loss levels, providing traders with a robust framework for risk management.
-> check Vegas SuperTrend Enhanced - Strategy
█ Strategy, How It Works: Detailed Explanation
🔶 Vegas Channel and SuperTrend Calculations
The strategy initiates by calculating the Vegas Channel, which is derived from a simple moving average (SMA) and the standard deviation (STD) of the closing prices over a specified window length. This channel helps in measuring market volatility and forms the basis for adjusting the SuperTrend indicator.
Vegas Channel Calculation:
- vegasMovingAverage = SMA(close, vegasWindow)
- vegasChannelStdDev = STD(close, vegasWindow)
- vegasChannelUpper = vegasMovingAverage + vegasChannelStdDev
- vegasChannelLower = vegasMovingAverage - vegasChannelStdDev
SuperTrend Multiplier Adjustment:
- channelVolatilityWidth = vegasChannelUpper - vegasChannelLower
- adjustedMultiplier = superTrendMultiplierBase + volatilityAdjustmentFactor * (channelVolatilityWidth / vegasMovingAverage)
The adjusted multiplier enhances the SuperTrend's sensitivity to market volatility, making it more adaptable to changing market conditions.
BTCUSD Local picture.
🔶 Average True Range (ATR) and SuperTrend Values
The ATR is computed over a specified period to measure market volatility. Using the ATR and the adjusted multiplier, the SuperTrend upper and lower levels are determined.
ATR Calculation:
- averageTrueRange = ATR(atrPeriod)
**SuperTrend Calculation:**
- superTrendUpper = hlc3 - (adjustedMultiplier * averageTrueRange)
- superTrendLower = hlc3 + (adjustedMultiplier * averageTrueRange)
The SuperTrend levels are continuously updated based on the previous values and the current market trend direction. The market trend is determined by comparing the closing prices with the SuperTrend levels.
Trend Direction:
- If close > superTrendLowerPrev, then marketTrend = 1 (bullish)
- If close < superTrendUpperPrev, then marketTrend = -1 (bearish)
🔶 Trade Entry and Exit Conditions
The strategy generates trade signals based on the alignment of both SuperTrends. Trades are executed only when both SuperTrends indicate the same market direction.
Entry Conditions:
- Long Position: Both SuperTrends must signal a bullish trend.
- Short Position: Both SuperTrends must signal a bearish trend.
Exit Conditions:
- Positions are exited if either SuperTrend reverses its trend direction.
- Additional conditions include holding periods and configurable take-profit and stop-loss levels.
█ Trade Direction
The strategy allows traders to specify the desired trade direction through a customizable input setting. Options include:
- Long: Only enter long positions.
- Short: Only enter short positions.
- Both: Enter both long and short positions based on the market conditions.
█ Usage
To utilize the "Double Vegas SuperTrend Enhanced" strategy, traders need to configure the input settings according to their trading preferences and market conditions. The strategy includes parameters for ATR periods, Vegas Channel window lengths, SuperTrend multipliers, volatility adjustment factors, and risk management settings such as hold days, take-profit, and stop-loss percentages.
█ Default Settings
The strategy comes with default settings that can be adjusted to fit individual trading styles:
- trade Direction: Both (allows trading in both long and short directions for maximum flexibility).
- ATR Periods: 10 for SuperTrend 1 and 5 for SuperTrend 2 (shorter ATR period results in more sensitivity to recent price movements).
- Vegas Window Lengths: 100 for SuperTrend 1 and 200 for SuperTrend 2 (longer window length results in smoother moving averages and less sensitivity to short-term volatility).
- SuperTrend Multipliers: 5 for SuperTrend 1 and 7 for SuperTrend 2 (higher multipliers lead to wider SuperTrend channels, reducing the frequency of trades).
- Volatility Adjustment Factors: 5 for SuperTrend 1 and 7 for SuperTrend 2 (higher adjustment factors increase the responsiveness to changes in market volatility).
- Hold Days: 5 (defines the minimum duration a position is held, ensuring trades are not exited prematurely).
- Take Profit: 30% (sets the target profit level to lock in gains).
- Stop Loss: 20% (sets the maximum acceptable loss level to mitigate risk).
HilalimSB Strategy HilalimSB A Wedding Gift 🌙
What is HilalimSB🌙?
First of all, as mentioned in the title, HilalimSB is a wedding gift.
HilalimSB - Revealing the Secrets of the Trend
HilalimSB is a powerful indicator designed to help investors analyze market trends and optimize trading strategies. Designed to uncover the secrets at the heart of the trend, HilalimSB stands out with its unique features and impressive algorithm.
Hilalim Algorithm and Fixed ATR Value:
HilalimSB is equipped with a special algorithm called "Hilalim" to detect market trends. This algorithm can delve into the depths of price movements to determine the direction of the trend and provide users with the ability to predict future price movements. Additionally, HilalimSB uses its own fixed Average True Range (ATR) value. ATR is an indicator that measures price movement volatility and is often used to determine the strength of a trend. The fixed ATR value of HilalimSB has been tested over long periods and its reliability has been proven. This allows users to interpret the signals provided by the indicator more reliably.
ATR Calculation Steps
1.True Range Calculation:
+ The True Range (TR) is the greatest of the following three values:
1. Current high minus current low
2. Current high minus previous close (absolute value)
3. Current low minus previous close (absolute value)
2.Average True Range (ATR) Calculation:
-The initial ATR value is calculated as the average of the TR values over a specified period
(typically 14 periods).
-For subsequent periods, the ATR is calculated using the following formula:
ATRt=(ATRt−1×(n−1)+TRt)/n
Where:
+ ATRt is the ATR for the current period,
+ ATRt−1 is the ATR for the previous period,
+ TRt is the True Range for the current period,
+ n is the number of periods.
Pine Script to Calculate ATR with User-Defined Length and Multiplier
Here is the Pine Script code for calculating the ATR with user-defined X length and Y multiplier:
//@version=5
indicator("Custom ATR", overlay=false)
// User-defined inputs
X = input.int(14, minval=1, title="ATR Period (X)")
Y = input.float(1.0, title="ATR Multiplier (Y)")
// True Range calculation
TR1 = high - low
TR2 = math.abs(high - close )
TR3 = math.abs(low - close )
TR = math.max(TR1, math.max(TR2, TR3))
// ATR calculation
ATR = ta.rma(TR, X)
// Apply multiplier
customATR = ATR * Y
// Plot the ATR value
plot(customATR, title="Custom ATR", color=color.blue, linewidth=2)
This code can be added as a new Pine Script indicator in TradingView, allowing users to calculate and display the ATR on the chart according to their specified parameters.
HilalimSB's Distinction from Other ATR Indicators
HilalimSB emerges with its unique Average True Range (ATR) value, presenting itself to users. Equipped with a proprietary ATR algorithm, this indicator is released in a non-editable form for users. After meticulous testing across various instruments with predetermined period and multiplier values, it is made available for use.
ATR is acknowledged as a critical calculation tool in the financial sector. The ATR calculation process of HilalimSB is conducted as a result of various research efforts and concrete data-based computations. Therefore, the HilalimSB indicator is published with its proprietary ATR values, unavailable for modification.
The ATR period and multiplier values provided by HilalimSB constitute the fundamental logic of a trading strategy. This unique feature aids investors in making informed decisions.
Visual Aesthetics and Clear Charts:
HilalimSB provides a user-friendly interface with clear and impressive graphics. Trend changes are highlighted with vibrant colors and are visually easy to understand. You can choose colors based on eye comfort, allowing you to personalize your trading screen for a more enjoyable experience. While offering a flexible approach tailored to users' needs, HilalimSB also promises an aesthetic and professional experience.
Strong Signals and Buy/Sell Indicators:
After completing test operations, HilalimSB produces data at various time intervals. However, we would like to emphasize to users that based on our studies, it provides the best signals in 1-hour chart data. HilalimSB produces strong signals to identify trend reversals. Buy or sell points are clearly indicated, allowing users to develop and implement trading strategies based on these signals.
For example, let's imagine you wanted to open a position on BTC on 2023.11.02. You are aware that you need to calculate which of the buying or selling transactions would be more profitable. You need support from various indicators to open a position. Based on the analysis and calculations it has made from the data it contains, HilalimSB would have detected that the graph is more suitable for a selling position, and by producing a sell signal at the most ideal selling point at 08:00 on 2023.11.02 (UTC+3 Istanbul), it would have informed you of the direction the graph would follow, allowing you to benefit positively from a 2.56% decline.
Technology and Innovation:
HilalimSB aims to enhance the trading experience using the latest technology. With its innovative approach, it enables users to discover market opportunities and support their decisions. Thus, investors can make more informed and successful trades. Real-Time Data Analysis: HilalimSB analyzes market data in real-time and identifies updated trends instantly. This allows users to make more informed trading decisions by staying informed of the latest market developments. Continuous Update and Improvement: HilalimSB is constantly updated and improved. New features are added and existing ones are enhanced based on user feedback and market changes. Thus, HilalimSB always aims to provide the latest technology and the best user experience.
Social Order and Intrinsic Motivation:
Negative trends such as widespread illegal gambling and uncontrolled risk-taking can have adverse financial effects on society. The primary goal of HilalimSB is to counteract these negative trends by guiding and encouraging users with data-driven analysis and calculable investment systems. This allows investors to trade more consciously and safely.
What is HilalimSB Strategy🌙?
HilalimSB Strategy is a strategy that is supported by the HilalimSB algorithm created by the creator of HilalimSB and continues transactions with take profit and stop loss levels determined by users who strategically and automatically open transactions as a result of the data it receives and automatically closes transactions under necessary conditions. It is a first in the tradingview world with its unique take profit and stop loss markings. HilalimSB Strategy is open to users' initiatives and is a trading strategy developed on BTC.
What does the HilalimSB Strategy target?
The main purpose of HilalimSB Strategy is to reduce the transaction load of traders and to be integrated into various brokerage firms and operated by automatic trading bots, and it is aimed to serve this purpose. In addition to the strategies currently available in the markets, HilalimSB Strategy offers a useful infrastructure to traders with its useful interface. HilalimSB Strategy, which was decided to be published as a result of various calculations, was offered to the users with its unique visual effects after the completion of the testing procedures under market conditions.
HilalimSB Strategy and Heikin Ashi
HilalimSB Strategy produces data in Heikin Ashi chart types, but since Heikin Ashi chart types have their own calculation method, HilalimSB Strategy has been published in a way that cannot produce data in this chart type due to HilalimSB Strategy's ideology of appealing to all types of users, and any confusion that may arise is prevented in this way.
After the necessary conditions determined by the creator of HilalimSB are met, HilalimSB Heikin Ashi will be shared exclusively with invited users only, upon request, to users who request an invitation.
Differences between HilalimSB Strategy and HilalimSB
HilalimSB Strategy has been shared as a strategy and its features have been explained above. HilalimSB is a trading indicator and this is the main difference between them.We can explain it briefly this way.
Here are the differences between indicators and strategies:
1.Purpose and Use:
Indicators: Analyze market data to provide information about price movements and trends. They typically generate buy and sell signals and give traders clues about when to make trades in the market.
Strategies: These are plans for trading based on specific rules. They use signals from indicators and other market data to execute buy and sell transactions.
2.Features:
Indicators: Operate independently and are based on specific mathematical formulas. Examples include moving averages, RSI, and MACD.
Strategies: Combine one or more indicators and other market analysis tools to create a comprehensive trading plan. This plan determines entry and exit points, risk management, and trade size.
3.Scope:
Indicators: Are single analysis tools focusing on specific time frames or price movements.
Strategies: Are comprehensive trading plans that typically involve multiple trades over a certain period.
4.Decision Making:
Indicators: Provide information to traders and help in the decision-making process.
Strategies: Are direct decision-making mechanisms that execute trades automatically according to predetermined rules.
5.Automation:
Indicators: Are mostly interpreted manually and used based on the trader’s discretion.
Strategies: Can be used in automated trading systems and execute trades automatically according to the set rules.
The shared image is a 1-hour chart of BTCUSDC.P determined by the user as 1 percent take profit and 1 percent stop loss. And transactions were opened on Binance with the commission rate determined as 0.017 for the USDC trading pair.
HilalimSB Strategy, which presents users with completely concrete data, has proven itself in testing processes and is a project of SB that aims to reach all user profiles.🌙
Kaufman Adaptive Moving Average (KAMA) Strategy [TradeDots]"The Kaufman Adaptive Moving Average (KAMA) Strategy" is a trend-following system that leverages the adaptive qualities of the Kaufman Adaptive Moving Average (KAMA). This strategy is distinguished by its ability to adjust dynamically to market volatility, enhancing trading accuracy by minimizing the effects of false and delayed signals often associated with the Simple Moving Average (SMA).
HOW IT WORKS
This strategy is centered around use of the Kaufman Adaptive Moving Average (KAMA) indicator, which refines the principles of the Exponential Moving Average (EMA) with a superior smoothing technique.
KAMA distinguishes itself by its responsiveness to changes in market prices through an "Efficiency Ratio (ER)." This ratio is computed by dividing the recent absolute net price change by the cumulative sum of the absolute price changes over a specified period. The resulting ER value ranges between 0 and 1, where 0 indicates high market noise and 1 reflects stronger market momentum.
Using ER, we could get the smoothing constant (SC) for the moving average derived using the following formula:
fastest = 2/(fastma_length + 1)
slowest = 2/(slowma_length + 1)
SC = math.pow((ER * (fastest-slowest) + slowest), 2)
The KAMA line is then calculated by applying the SC to the difference between the current price and the previous KAMA.
APPLICATION
For entering long positions, this strategy initializes when there is a sequence of 10 consecutive rising KAMA lines. Conversely, a sequence of 10 consecutive falling KAMA lines triggers sell orders for long positions. The same logic applies inversely for short positions.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 80%
Users are advised to adjust and personalize this trading strategy to better match their individual trading preferences and style.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Trend Following Parabolic Buy Sell Strategy [TradeDots]The Trend Following Parabolic Buy-Sell Strategy leverages the Parabolic SAR in combination with moving average crossovers to deliver buy and sell signals within a trend-following framework.
This strategy synthesizes proven methodologies sourced from various trading tutorials available on platforms such as YouTube and blogs, enabling traders to conduct robust backtesting on their selected trading pairs to assess the strategy's effectiveness.
HOW IT WORKS
This strategy employs four key indicators to orchestrate its trading signals:
1. Trend Alignment: It first assesses the relationship between the price and the predominant trendline to determine the directional stance—taking long positions only when the price trends above the moving average, signaling an upward market trajectory.
2. Momentum Confirmation: Subsequent to trend alignment, the strategy looks for moving average crossovers as a confirmation that the price is gaining momentum in the direction of the intended trades.
3. Signal Finalization: Finally, buy or sell signals are validated using the Parabolic SAR indicator. A long order is validated when the closing price is above the Parabolic SAR dots, and similarly, conditions are reversed for short orders.
4. Risk Management: The strategy institutes a fixed stop-loss at the moving average trendline and a take-profit level determinable by a prefixed risk-reward ratio calculated from the moving average trendline. These parameters are customizable by the users within the strategy settings.
APPLICATION
Designed for assets exhibiting pronounced directional momentum, this strategy aims to capitalize on clear trend movements conducive to achieving set take-profit targets.
As a lagging strategy that waits for multiple confirmatory signals, entry into trades might occasionally lag beyond optimal timing.
Furthermore, in periods of consolidation or sideways movement, the strategy may generate several false signals, suggesting the potential need for additional market condition filters to enhance signal accuracy during volatile phases.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 70%
Users are advised to adjust and personalize this trading strategy to better match their individual trading preferences and style.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
TASC 2024.06 REIT ETF Trading System█ OVERVIEW
This strategy script demonstrates the application of the Real Estate Investment Trust (REIT) ETF trading system presented in the article by Markos Katsanos titled "Is The Price REIT?" from TASC's June 2024 edition of Traders' Tips .
█ CONCEPTS
REIT stocks and ETFs offer a simplified, diversified approach to real estate investment. They exhibit sensitivity to interest rates, often moving inversely to interest rate and treasury yield changes. Markos Katsanos explores this relationship and the correlation of prices with the broader market to develop a trading strategy for REIT ETFs.
The script employs Bollinger Bands and Donchian channel indicators to identify oversold conditions and trends in REIT ETFs. It incorporates the 10-year treasury yield index (TNX) as a proxy for interest rates and the S&P 500 ETF (SPY) as a benchmark for the overall market. The system filters trade entries based on their behavior and correlation with the REIT ETF price.
█ CALCULATIONS
The strategy initiates long entries (buy signals) under two conditions:
1. Oversold condition
The weekly ETF low price dips below the 15-week Bollinger Band bottom, the closing price is above the value by at least 0.2 * ATR ( Average True Range ), and the price exceeds the week's median.
Either of the following:
– The TNX index is down over 15% from its 25-week high, and its correlation with the ETF price is less than 0.3.
– The yield is below 2%.
2. Uptrend
The weekly ETF price crosses above the previous week's 30-week Donchian channel high.
The SPY ETF is above its 20-week moving average.
Either of the following:
– Over ten weeks have passed since the TNX index was at its 30-week high.
– The correlation between the TNX value and the ETF price exceeds 0.3.
– The yield is below 2%.
The strategy also includes three exit (sell) rules:
1. Trailing (Chandelier) stop
The weekly close drops below the highest close over the last five weeks by over 1.5 * ATR.
The TNX value rises over the latest 25 weeks, with a yield exceeding 4%, or its value surges over 15% above the 25-week low.
2. Stop-loss
The ETF's price declines by at least 8% of the previous week's close and falls below the 30-week moving average.
The SPY price is down by at least 8%, or its correlation with the ETF's price is negative.
3. Overbought condition
The ETF's value rises above the 100-week low by over 50%.
The ETF's price falls over 1.5 * ATR below the 3-week high.
The ETF's 10-week Stochastic indicator exceeds 90 within the last three weeks.
█ DISCLAIMER
This strategy script educates users on the system outlined by the TASC article. However, note that its default properties might not fully represent real-world trading conditions for an individual. By default, it uses 10% of equity as the order size and a slippage amount of 5 ticks. Traders should adjust these settings and the commission amount when using this script. Additionally, since this strategy utilizes compound conditions on weekly data to trigger orders, it will generate significantly fewer trades than other, higher-frequency strategies.
Price-Volume Dynamic - Strategy [presentTrading]█ Introduction and How it is Different
The "Price-Volume Dynamic - Strategy" leverages a unique blend of price action, volume analysis, and statistical z-scores to establish trading positions. This approach differentiates itself by integrating the concept of the Point of Control (POC) from volume profile analysis with price-based z-score indicators to create a dynamic trading strategy. It tailors entry and exit thresholds based on current market volatility, providing a responsive and adaptive trading method. This strategy stands out by considering both historical volatility and price trends to adjust trading decisions in real-time, enhancing its effectiveness in various market conditions.
BTCUSD 4h LS Performance
█ Strategy: How It Works – Detailed Explanation
🔶 Calculating Point of Control (POC)
The Point of Control (POC) represents the price level with the highest traded volume over a specified lookback period. It's calculated by dividing the price range into a number of rows, each representing a price level. The volume at each price level is tallied and the level with the maximum volume is designated as the POC.
🔶 Dynamic Thresholds Adjustments
The entry and exit thresholds are dynamically adjusted based on normalized volatility, which is derived from the current, minimum, and maximum ATR over a specified period. This normalization ensures that the thresholds adapt to changes in market conditions, making the strategy sensitive to shifts in market volatility.
BTCUSD local performance
█ Trade Direction
The strategy can be configured to trade in three different directions: Long, Short, or Both. This flexibility allows traders to align their trading strategy with their market outlook or risk preferences. By adjusting the `POC_tradeDirection` input, traders can selectively participate in market movements that match their trading style and objectives.
█ Usage
To deploy this strategy, traders should apply it within a trading software that supports scripting and backtesting, such as TradingView's Pine Script environment. Users can input their parameters based on their analysis of the market conditions and their risk tolerance. It is essential for traders to backtest the strategy using historical data to evaluate its performance and make necessary adjustments before applying it in live trading scenarios.
█ Default Settings
- Lookback Length: Sets the period over which the highest and lowest prices, and the volume per price level, are calculated. A higher lookback length smoothens the volatility but may delay response to recent market movements.
- Number of Rows: Determines the granularity of price levels within the price range. More rows provide a more detailed volume profile but require more computational resources.
- Entry Z-Score Threshold Base: Influences the sensitivity of the strategy to enter trades. Higher values make the strategy more conservative, requiring stronger deviation from the mean to trigger a trade.
- Exit Z-Score Threshold Base: Sets the threshold for exiting trades, with lower values allowing trades to close on smaller price retractions, thereby potentially preserving profits or reducing losses.
- Trading Direction: Allows selection between Long, Short, or Both, enabling traders to tailor the strategy to their market view or risk preferences.
Stochastic Z-Score Oscillator Strategy [TradeDots]The "Stochastic Z-Score Oscillator Strategy" represents an enhanced approach to the original "Buy Sell Strategy With Z-Score" trading strategy. Our upgraded Stochastic model incorporates an additional Stochastic Oscillator layer on top of the Z-Score statistical metrics, which bolsters the affirmation of potential price reversals.
We also revised our exit strategy to when the Z-Score revert to a level of zero. This amendment gives a much smaller drawdown, resulting in a better win-rate compared to the original version.
HOW DOES IT WORK
The strategy operates by calculating the Z-Score of the closing price for each candlestick. This allows us to evaluate how significantly the current price deviates from its typical volatility level.
The strategy first takes the scope of a rolling window, adjusted to the user's preference. This window is used to compute both the standard deviation and mean value. With these values, the strategic model finalizes the Z-Score. This determination is accomplished by subtracting the mean from the closing price and dividing the resulting value by the standard deviation.
Following this, the Stochastic Oscillator is utilized to affirm the Z-Score overbought and oversold indicators. This indicator operates within a 0 to 100 range, so a base adjustment to match the Z-Score scale is required. Post Stochastic Oscillator calculation, we recalibrate the figure to lie within the -4 to 4 range.
Finally, we compute the average of both the Stochastic Oscillator and Z-Score, signaling overpriced or underpriced conditions when the set threshold of positive or negative is breached.
APPLICATION
Firstly, it is better to identify a stable trading pair for this technique, such as two stocks with considerable correlation. This is to ensure conformance with the statistical model's assumption of a normal Gaussian distribution model. The ideal performance is theoretically situated within a sideways market devoid of skewness.
Following pair selection, the user should refine the span of the rolling window. A broader window smoothens the mean, more accurately capturing long-term market trends, while potentially enhancing volatility. This refinement results in fewer, yet precise trading signals.
Finally, the user must settle on an optimal Z-Score threshold, which essentially dictates the timing for buy/sell actions when the Z-Score exceeds with thresholds. A positive threshold signifies the price veering away from its mean, triggering a sell signal. Conversely, a negative threshold denotes the price falling below its mean, illustrating an underpriced condition that prompts a buy signal.
Within a normal distribution, a Z-Score of 1 records about 68% of occurrences centered at the mean, while a Z-Score of 2 captures approximately 95% of occurrences.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
The following is the default setup on EURAUD 1h timeframe
Rolling Window: 80
Z-Score Threshold: 2.8
Signal Cool Down Period: 5
Stochastic Length: 14
Stochastic Smooth Period: 7
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 40%
FURTHER IMPLICATION
The Stochastic Oscillator imparts minimal impact on the current strategy. As such, it may be beneficial to adjust the weightings between the Z-Score and Stochastic Oscillator values or the scale of Stochastic Oscillator to test different performance outcomes.
Alternative momentum indicators such as Keltner Channels or RSI could also serve as robust confirmations of overbought and oversold signals when used for verification.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Price Based Z-Trend - Strategy [presentTrading]█ Introduction and How it is Different
Z-score: a statistical measurement of a score's relationship to the mean in a group of scores.
Simple but effective approach.
The "Price Based Z-Trend - Strategy " leverages the Z-score, a statistical measure that gauges the deviation of a price from its moving average, normalized against its standard deviation. This strategy stands out due to its simplicity and effectiveness, particularly in markets where price movements often revert to a mean. Unlike more complex systems that might rely on a multitude of indicators, the Z-Trend strategy focuses on clear, statistically significant price movements, making it ideal for traders who prefer a streamlined, data-driven approach.
BTCUSD 6h LS Performance
█ Strategy, How It Works: Detailed Explanation
🔶 Calculation of the Z-score
"Z-score is a statistical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. If a Z-score is 0, it indicates that the data point's score is identical to the mean score. A Z-score of 1.0 would indicate a value that is one standard deviation from the mean. Z-scores may be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean."
The Z-score is central to this strategy. It is calculated by taking the difference between the current price and the Exponential Moving Average (EMA) of the price over a user-defined length, then dividing this by the standard deviation of the price over the same length:
z = (x - μ) /σ
Local
🔶 Trading Signals
Trading signals are generated based on the Z-score crossing predefined thresholds:
- Long Entry: When the Z-score crosses above the positive threshold.
- Long Exit: When the Z-score falls below the negative threshold.
- Short Entry: When the Z-score falls below the negative threshold.
- Short Exit: When the Z-score rises above the positive threshold.
█ Trade Direction
The strategy allows users to select their preferred trading direction through an input option.
█ Usage
To use this strategy effectively, traders should first configure the Z-score thresholds according to their risk tolerance and market volatility. It's also crucial to adjust the length for the EMA and standard deviation calculations based on historical performance and the expected "noise" in price data.
The strategy is designed to be flexible, allowing traders to refine settings to better capture profitable opportunities in specific market conditions.
█ Default Settings
- Trade Direction: Both
- Standard Deviation Length: 100
- Average Length: 100
- Threshold for Z-score: 1.0
- Bar Color Indicator: Enabled
These settings offer a balanced starting point but can be customized to suit various trading styles and market environments. The strategy's parameters are designed to be adjusted as traders gain experience and refine their approach based on ongoing market analysis.
Z-score is a must-learn approach for every algorithmic trader.
Pullback_Power [JackTz]Welcome to Pullback_Power
Pullback_Power is a scalping strategy designed to capitalize on market retracements while incorporating unique dynamic features to enhance profitability.
Calculation
Pullback_Power purely uses moving averages to calculate both entry and exits. Exits can also be set to fixed percentages for both take profit and stop loss.
How the Strategy Works
Statistics show that markets normally do a recovery after each drop. Crypto markets can easily drop up to 20% within a few hours and then do a complete or partial recovery. Pullback_Power utilizes this known pattern alongside pyramiding. The strategy aims to catch one or more entries when the price drops, hoping to make profits when the market recovers from the drop. The fixed take profit and stop loss can be used to define your risk management, while the dynamic exit opportunity is riskier but provides the ability to stay in the trade longer while it recovers. Pullback_Power can make up to four entries. This means it utilizes pyramiding to spread out the entry points, but every exit is a full exit. It is not possible to partially exit.
Utility
Pullback_Power is a scalping strategy suitable for traders who operate with small trades and don't want to stay in the market for too long. Pullback_Power offers precise signals with no repainting. The strategy thrives in volatility, so crypto pairs might yield the best results, although this strategy can be adapted to work on all pairs and markets.
How to Automate It
Pullback_Power utilizes the standard placeholders of strategies on TradingView. This enables the trader to add every data point into a webhook, making it fully flexible to suit every trader's needs. To automate, create an alert, set the webhook URL, and add the JSON body needed for the webhook. An example of a simple JSON webhook with some of the standard strategy placeholders:
{
"side": "{{strategy.order.action}}",
"symbol": "{{ticker}}",
"amount": "{{strategy.order.contracts}}"
}
Read about all the standard placeholders that you can use here: TradingView - Standard strategy placeholders
Originality
Pullback_Power is unique in its ability to create precise signals without repainting while maintaining a solid approach to the pullback strategy. Its simplicity not only makes the strategy easy to use and understand but also highly effective. The simplicity reduces inputs, eliminating overfitting and limits each input to avoid incorrect usage. Many times, default settings are enough to achieve good backtesting results on almost all pairs available. Pullback_Power also differs from many other strategies by its solid code, which enhances performance and provides more reliable backtesting. The clean code increases the resilience and precision of the entries, making it less prone to errors.
Many pullback/scalping strategies normally only works on specific scopes of timeframes or pairs. Pullback_Power can easily be adapted to work on almost every scenario. The biggest change needed is the length of the moving average. The lower the timeframe, the higher a length is needed for proper results. I.e. on a 2H timeframe a length of 3 can yield good results. On a 5min timeframe the length might need to be as high as 70.
How to Use
To use Pullback_Power, add the script to your trading chart. By default, Pullback_Power opens four orders to optimize trade opportunities with a default fee value set at 0.1%. You can change these default settings in the Settings window under the Properties tab. To tailor Pullback_Power to your individual trading style, navigate to the Settings under the Input tab. Here you can configure various inputs to fit your trading style.
- Backtest settings , Start Date:
Defines the date of when the calculation starts. Use this to set the date of when the first trade could potentially emit.
- Backtest settings , End Date:
Defines the date of when the calculation ends. If there are any open trades after this date the close calculations are still live. It only makes sure that new orders cannot be opened after this date.
- Backtest settings , Only trade on weekdays:
This is a toggle you can enable or disable. If enabled it only allows new entries to happen during the normal week days, meaning Monday, Tuesday, Wednesday, Thursday and Friday.
Disable this to enable the script to open trades on all 7 days of the week.
- Open settings , Use dynamic long positions:
This toggle allows you to enable or disable the pullback level calculations after first trade.
If enabled, the calculations of level 2, 3 and 4 continues to happen after each bar, making the levels follow the price with the moving averages calculations.
If disabled, the calculations of the levels stop after the first trade. This means that the levels calculation at the point of the first trade stay fixed until all trades are closed.
You can see the difference of the green lines on the chart when you toggle this flag.
- Open settings , Data type:
This is the bar data used for the moving average calculation when opening trades. The possible data types are Open, High, Low, Close, HL2, HLC3, OHLC4, OC2 and HC2.
- Open settings , Source type:
This is the source used to calculate the moving average. The types available are: SMA, PCMA, EMA, WMA, DEMA, ZLEMA and HMA.
- Open settings , Length:
This is the length used for the moving average calculations. 3 means it takes the last 3 bars of historical data for the calculation.
- Open settings , Offset:
This defines if the calculation should use an offset for the historical data. This does not use a look-forward feature, but a look-backward feature. To prevent any possible repaints the offset can only be positive, not negative.
For instance, if the length is 3 and the offset is 0 the calculation is made from the last 3 bars, making it bar1, bar2 and bar3. If the length is 3 and the offset is 1 the calculation is made from bar2, bar3, and bar4 – offsetting the calculation by 1 bar.
- Leverage settings , Leverage liquidation (1-125):
The script itself does not handle any custom leverage calculation – this must be done in the Properties tabs and increasing the order size.
This setting is made to test a possible liquidation event if using leverage.
By setting this to higher than 1, a red line is visible after the first trade on the chart. This indicates the liquidation price.
If this setting is set to 25, the script will calculate the liquidation price from a x25 leverage. If this price is hit, the scripts stops emitting any orders and the background turns red.
You can use this to test if your settings could handle a certain level of leverage.
- Pullback settings , Pullback 1, 2, 3 and 4:
Each of these settings defines the entry price of each pullback level. If Pullback 1 is set to -6 it means that the moving average calculation should be 6% lower than the actual price.
The same logic applies to Pullback 2, 3 and 4.
Setting any level to 0 will disable the level – eliminating any orders to emit on that level.
This can be used to change the level of pyramiding down from 4 if needed.
If you do this, remember to also change the order size and the pyramiding value in the Properties tab accordingly.
- Close settings , Use dynamic TP and SL:
If enabled, script will exit all orders using the same but separate algorithm for moving averages. This enables the user to define if you want the orders to be closed if the price level of this moving average is hit. The price level for this calculation is visible on the chart by the blue line.
Although you can change the length and offset, as described underneath, this calculation uses the same data and source type defined in the Open settings area.
- Close settings , Length, Close:
This is the length used for the closing moving average calculations. 3 means it takes the last 3 bars of historical data for the calculation.
- Close settings , Offset, Close:
This defines if the calculation for the closing moving average should use an offset for the historical data. Just as the offset used for opening order, this does not use a look-forward feature, but a look-backward feature. To prevent any possible repaints the offset can only be positive, not negative.
For instance, if the length is 3 and the offset is 0 the calculation is made from the last 3 bars, making it bar1, bar2 and bar3. If the length is 3 and the offset is 1 the calculation is made from bar2, bar3, and bar4 – offsetting the calculation by 1 bar.
- Close settings , Use TakeProfit:
This toggle enables/disables a fixed take profit percentage.
- Close settings , TP %:
This sets the wanted % to reach on a take profit. This setting is ignored if the toggle above is disabled.
- Close settings , Use StopLoss:
This toggle enables/disables a fixed stop loss percentage.
- Close settings , SL %:
This sets the wanted % to reach on a stop loss. This setting is ignored if the toggle above is disabled.
Exit on Same Bar as Entry
By default, the script doesn't emit any exit orders on the same bar as the first entry order. Enable "Recalculation: After order is filled" to change this behavior.
Troubleshooting
While Pullback_Power is designed to provide reliable trading signals, you may encounter rare issues. One such issue could be receiving an error message stating "can't open orders with 0 or negative qty." If you encounter this error, it is likely due to specific conditions on the selected timeframe. To resolve this issue, change the timeframe on your trading chart.
Underlying Principles and Value Proposition
Pullback_Power leverages moving averages and volatility behavior to identify market retracements and capitalize on them. The strategy is rooted in the understanding that markets often experience temporary reversals or "pullbacks" before resuming their primary trend. By identifying these pullbacks and entering trades at opportune moments, Pullback_Power aims to capture quick profits from short-term market movements.
The dynamic and fixed calculations of Take Profit (TP) and Stop Loss (SL) levels enhances risk management, ensuring that potential losses are controlled while allowing room for profits to grow. The adaptive approach using the moving averages considers current market conditions, making the strategy flexible and responsive to changing volatility.
Moreover, Pullback_Power's non-repainting nature ensures the reliability of its signals, eliminating hindsight bias and providing traders with actionable insights based on real-time market data.
The strategy's simplicity and effectiveness make it accessible for traders of all experience levels. Whether you're a beginner looking to start scalping or an experienced trader seeking to diversify your trading approach, Pullback_Power offers a balanced blend of simplicity and sophistication to help you navigate the markets with confidence.
By focusing on clear, transparent principles and offering practical tools for risk management, Pullback_Power aims to provide tangible value to traders, empowering them to make informed decisions and optimize their trading outcomes.
Thank you for choosing Pullback_Power. I wish you successful trading!
Buy Sell Strategy With Z-Score [TradeDots]The "Buy Sell Strategy With Z-Score" is a trading strategy that harnesses Z-Score statistical metrics to identify potential pricing reversals, for opportunistic buying and selling opportunities.
HOW DOES IT WORK
The strategy operates by calculating the Z-Score of the closing price for each candlestick. This allows us to evaluate how significantly the current price deviates from its typical volatility level.
The strategy first takes the scope of a rolling window, adjusted to the user's preference. This window is used to compute both the standard deviation and mean value. With these values, the strategic model finalizes the Z-Score. This determination is accomplished by subtracting the mean from the closing price and dividing the resulting value by the standard deviation.
This approach provides an estimation of the price's departure from its traditional trajectory, thereby identifying market conditions conducive to an asset being overpriced or underpriced.
APPLICATION
Firstly, it is better to identify a stable trading pair for this technique, such as two stocks with considerable correlation. This is to ensure conformance with the statistical model's assumption of a normal Gaussian distribution model. The ideal performance is theoretically situated within a sideways market devoid of skewness.
Following pair selection, the user should refine the span of the rolling window. A broader window smoothens the mean, more accurately capturing long-term market trends, while potentially enhancing volatility. This refinement results in fewer, yet precise trading signals.
Finally, the user must settle on an optimal Z-Score threshold, which essentially dictates the timing for buy/sell actions when the Z-Score exceeds with thresholds. A positive threshold signifies the price veering away from its mean, triggering a sell signal. Conversely, a negative threshold denotes the price falling below its mean, illustrating an underpriced condition that prompts a buy signal.
Within a normal distribution, a Z-Score of 1 records about 68% of occurrences centered at the mean, while a Z-Score of 2 captures approximately 95% of occurrences.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
The following is the default setup on EURUSD 1h timeframe
Rolling Window: 80
Z-Score Threshold: 2.8
Signal Cool Down Period: 5
Commission: 0.03%
Initial Capital: $10,000
Equity per Trade: 30%
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Fibonacci Trend Reversal StrategyIntroduction
This publication introduces the " Fibonacci Retracement Trend Reversal Strategy, " tailored for traders aiming to leverage shifts in market momentum through advanced trend analysis and risk management techniques. This strategy is designed to pinpoint potential reversal points, optimizing trading opportunities.
Overview
The strategy leverages Fibonacci retracement levels derived from @IMBA_TRADER's lance Algo to identify potential trend reversals. It's further enhanced by a method called " Trend Strength Over Time " (TSOT) (by @federalTacos5392b), which utilizes percentile rankings of price action to measure trend strength. This also has implemented Dynamic SL finder by utilizing @veryfid's ATR Stoploss Finder which works pretty well
Indicators:
Fibonacci Retracement Levels : Identifies critical reversal zones at 23.6%, 50%, and 78.6% levels.
TSOT (Trend Strength Over Time) : Employs percentile rankings across various timeframes to gauge the strength and direction of trends, aiding in the confirmation of Fibonacci-based signals.
ATR (Average True Range) : Implements dynamic stop-loss settings for both long and short positions, enhancing trade security.
Strategy Settings :
- Sensitivity: Set default at 18, adjustable for more frequent or sparse signals based on market volatility.
- ATR Stop Loss Finder: Multiplier set at 3.5, applying the ATR value to determine stop losses dynamically.
- ATR Length: Default set to 14 with RMA smoothing.
- TSOT Settings: Hard-coded to identify percentile ranks, with no user-adjustable inputs due to its intrinsic calculation method.
Trade Direction Options : Configurable to support long, short, or both directions, adaptable to the trader's market assessment.
Entry Conditions :
- Long Entry: Triggered when the price surpasses the mid Fibonacci level (50%) with a bullish TSOT signal.
- Short Entry: Activated when the price falls below the mid Fibonacci level with a bearish TSOT indication.
Exit Conditions :
- Employs ATR-based dynamic stop losses, calibrated according to current market volatility, ensuring effective risk management.
Strategy Execution :
- Risk Management: Features adjustable risk-reward settings and enables partial take profits by default to systematically secure gains.
- Position Reversal: Includes an option to reverse positions based on new TSOT signals, improving the strategy's responsiveness to evolving market conditions.
The strategy is optimized for the BYBIT:WIFUSDT.P market on a scalping (5-minute) timeframe, using the default settings outlined above.
I spent a lot of time creating the dynamic exit strategies for partially taking profits and reversing positions so please make use of those and feel free to adjust the settings, tool tips are also provided.
For Developers: this is published as open-sourced code so that developers can learn something especially on dynamic exits and partial take profits!
Good Luck!
Disclaimer
This strategy is shared for educational purposes and must be thoroughly tested under diverse market conditions. Past performance does not guarantee future results. Traders are advised to integrate this strategy with other analytical tools and tailor it to specific market scenarios. I was only sharing what I've crafted while strategizing over a Solana Meme Coin.
Vegas SuperTrend Enhanced - Strategy [presentTrading]█ Introduction and How it is Different
The "Vegas SuperTrend Enhanced - Strategy " trading strategy represents a novel integration of two powerful technical analysis tools: the Vegas Channel and the SuperTrend indicator. This fusion creates a dynamic, adaptable strategy designed for the volatile and fast-paced cryptocurrency markets, particularly focusing on Bitcoin trading.
Unlike traditional trading strategies that rely on a static set of rules, this approach modifies the SuperTrend's sensitivity to market volatility, offering traders the ability to customize their strategy based on current market conditions. This adaptability makes it uniquely suited to navigating the often unpredictable swings in cryptocurrency valuations, providing traders with signals that are both timely and reflective of underlying market dynamics.
BTC 6h LS
█ Strategy, How it Works: Detailed Explanation
This is an innovative approach that combines the volatility-based Vegas Channel with the trend-following SuperTrend indicator to create dynamic trading signals. This section delves deeper into the mechanics and mathematical foundations of the strategy.
Detail picture to show :
🔶 Vegas Channel Calculation
The Vegas Channel serves as the foundation of this strategy, employing a simple moving average (SMA) coupled with standard deviation to define the upper and lower bounds of the trading channel. This channel adapts to price movements, offering a visual representation of potential support and resistance levels based on historical price volatility.
🔶 SuperTrend Indicator Adjustment
Central to the strategy is the SuperTrend indicator, which is adjusted according to the width of the Vegas Channel. This adjustment is achieved by modifying the SuperTrend's multiplier based on the channel's volatility, allowing the indicator to become more sensitive during periods of high volatility and less so during quieter market phases.
🔶 Trend Determination and Signal Generation
The market trend is determined by comparing the current price with the SuperTrend values. A shift from below to above the SuperTrend line signals a potential bullish trend, prompting a "buy" signal, whereas a move from above to below indicates a bearish trend, generating a "sell" signal. This methodology ensures that trades are entered in alignment with the prevailing market direction, enhancing the potential for profitability.
BTC 6h Local
█ Trade Direction
A distinctive feature of this strategy is its configurable trade direction input, allowing traders to specify whether they wish to engage in long positions, short positions, or both. This flexibility enables users to tailor the strategy according to their risk tolerance, trading style, and market outlook, providing a personalized trading experience.
█ Usage
To utilize the "Vegas SuperTrend - Enhanced" strategy effectively, traders should first adjust the input settings to align with their trading preferences and the specific characteristics of the asset being traded. Monitoring the strategy's signals within the context of overall market conditions and combining its insights with other forms of analysis can further enhance its effectiveness.
█ Default Settings
- Trade Direction: Both (allows trading in both directions)
- ATR Period for SuperTrend: 10 (determines the length of the ATR for volatility measurement)
- Vegas Window Length: 100 (sets the length of the SMA for the Vegas Channel)
- SuperTrend Multiplier Base: 5 (base multiplier for SuperTrend calculation)
- Volatility Adjustment Factor: 5.0 (adjusts SuperTrend sensitivity based on Vegas Channel width)
These default settings provide a balanced approach suitable for various market conditions but can be adjusted to meet individual trading needs and objectives.
Fine-Tune Inputs: Fourier Smoothed Hybrid Volume Spread AnalysisUse this Strategy to Fine-tune inputs for the HSHVSA Indicator.
Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data.
I suggest using " Close all " input False when fine-tuning Inputs for 1 TimeFrame. When you export data to Excel/Numbers/GSheets I suggest using " Close all " input as True , except for the lowest TimeFrame.
MEANINGFUL DESCRIPTION:
The Fourier Smoothed Hybrid Volume Spread Analysis (FSHVSA) Strategy/Indicator is an innovative trading tool designed to fuse volume analysis with trend detection capabilities, offering traders a comprehensive view of market dynamics.
This Strategy/Indicator stands apart by integrating the principles of the Discrete Fourier Transform (DFT) and volume spread analysis, enhanced with a layer of Fourier smoothing to distill market noise and highlight trend directions with unprecedented clarity.
This smoothing process allows traders to discern the true underlying patterns in volume and price action, stripped of the distractions of short-term fluctuations and noise.
The core functionality of the FSHVSA revolves around the innovative combination of volume change analysis, spread determination (calculated from the open and close price difference), and the strategic use of the EMA (default 10) to fine-tune the analysis of spread by incorporating volume changes.
Trend direction is validated through a moving average (MA) of the histogram, which acts analogously to the Volume MA found in traditional volume indicators. This MA serves as a pivotal reference point, enabling traders to confidently engage with the market when the histogram's movement concurs with the trend direction, particularly when it crosses the Trend MA line, signalling optimal entry points.
It returns 0 when MA of the histogram and EMA of the Price Spread are not align.
WHAT IS FSHVSA INDICATOR:
The FSHVSA plots a positive trend when a positive Volume smoothed Spread and EMA of Volume smoothed price is above 0, and a negative when negative Volume smoothed Spread and EMA of Volume smoothed price is below 0. When this conditions are not met it plots 0.
HOW TO USE THE STRATEGY:
Here you fine-tune the inputs until you find a combination that works well on all Timeframes you will use when creating your Automated Trade Algorithmic Strategy. I suggest 4h, 12h, 1D, 2D, 3D, 4D, 5D, 6D, W and M.
ORIGINALITY & USEFULNESS:
The FSHVSA Strategy is unique because it applies DFT for data smoothing, effectively filtering out the minor fluctuations and leaving traders with a clear picture of the market's true movements. The DFT's ability to break down market signals into constituent frequencies offers a granular view of market dynamics, highlighting the amplitude and phase of each frequency component. This, combined with the strategic application of Ehler's Universal Oscillator principles via a histogram, furnishes traders with a nuanced understanding of market volatility and noise levels, thereby facilitating more informed trading decisions.
DETAILED DESCRIPTION:
My detailed description of the indicator and use cases which I find very valuable.
What is the meaning of price spread?
In finance, a spread refers to the difference between two prices, rates, or yields. One of the most common types is the bid-ask spread, which refers to the gap between the bid (from buyers) and the ask (from sellers) prices of a security or asset.
We are going to use Open-Close spread.
What is Volume spread analysis?
Volume spread analysis (VSA) is a method of technical analysis that compares the volume per candle, range spread, and closing price to determine price direction.
What does this mean?
We need to have a positive Volume Price Spread and a positive Moving average of Volume price spread for a positive trend. OR via versa a negative Volume Price Spread and a negative Moving average of Volume price spread for a negative trend.
What if we have a positive Volume Price Spread and a negative Moving average of Volume Price Spread?
It results in a neutral, not trending price action.
Thus the Indicator/Strategy returns 0 and Closes all long and short positions.
In the next Image you can see that trend is negative on 4h, we just move Negative on 12h and Positive on 1D. That means trend/Strategy flipped negative .
I am sorry, the chart is a bit messy. The idea is to use the indicator/strategy over more than 1 Timeframe.
Use this Strategy to fine-tune inputs for the HSHVSA Indicator.
(Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data)
I suggest using " Close all " input False when fine-tuning Inputs for 1 TimeFrame. When you export data to Excel/Numbers/GSheets I suggest using " Close all " input as True , except for the lowest TimeFrame. I suggest using 100% equity as your default quantity for fine-tune purposes. I have to mention that 100% equity may lead to unrealistic backtesting results. Be avare. When backtesting for trading purposes use Contracts or USDT.
Market Volatility Strategy (MVS)/Introduction
The Market Volatility Strategy (MVS) is based on volatility as an anomaly for making abnormal returns in the stock market. It uses the VIX, often referred to as the "fear gauge" which measures the market's expectation of 30-day volatility based on S&P 500 index options, as it's measure of volatility.
/Design
The VIX term structure refers to the relationship between the volatility index (VIX) values across different expiration dates. The term structure is important because it provides insights into market sentiment, risk expectations, and potential volatility in the future. The VIX term structure can take on three main shapes:
1. Contango: This occurs when longer-term VIX futures are priced higher than shorter-term futures. It is the most common shape for the VIX term structure.
2. Backwardation: This occurs when shorter-term VIX futures are priced higher than longer-term futures, indicating that the market expects volatility to decrease over time. Backwardation is less common and is typically seen in periods of high market stress or volatility.
3. Flat: A flat term structure happens when the VIX futures are priced similarly across different expiration dates. This indicates that the market's expectations for volatility are consistent over time.
/Trading
The strategy uses an understanding of the VIX term structure to generate buy and sell signals, as it provides valuable information about future volatility expectations and potential risk.
- Buy Signal
Contango suggests that the market expects volatility to increase over time. In a contango environment, the strategy looks for long volatility trades.
- Sell Signal
Backwardation suggests that investors are concerned about the near term and are willing to pay more for immediate protection. In such scenarios, the strategy looks for short volatility trades.
- Cash
A flat term structure can be transitional, moving from contango to backwardation or vice versa, or it may occur when the market is uncertain about future volatility. The strategy is in cash in this environment.
/Signals
The strategy has three signals:
1) Volatility
2) Volatility+
3) Volatility*
This means a maximum of three positions, one for each signal, can be opened simultaneously to maximize gains from volatility.
/Results
The backtest results are based on a starting capital of $13,700 (convenient amount for retail traders) with 5% of equity for the position size and pyramiding of 3 to allow one open position at a time for each signal. Commissions vary from broker to broker and they are calculated in different ways so a simple but very high commission of $3 per order is used in backtesting this strategy. Slippage of 3 ticks is used to ensure the results are representative of real world, market order trading. Trades are generated on the close of the candle to avoid bias. The backtest results are available to view at the bottom of this page.
Note:
Past performance in backtesting does not guarantee future results. Broker execution, market changes and trader psychology can significantly affect strategy performance in live trading.
Originality:
The MVS strategy is unique because it is based on data from the futures and options markets. This is data that is not usually accessible or understood by the retail trader hence, volatility strategies are difficult for them to design. The strategy gives retail traders access to a volatility strategy with no parameters, this greatly reduces the risk of curve fitting while increasing robustness.
/Tickers
This strategy has been backtested primarily on SPXL but it is suitable for use on the VIX ETFs.
Universal Algorithm [BackQuant]Universal Algorithm
It is a trading strategy designed CLEAR TREND DETECTION . This script is the culmination of extensive research and development efforts aimed at providing traders with a robust tool capable of adapting to a wide array of market conditions. This description delves into the core components, methodologies, and operational parameters of Universal Algo to offer potential users a clear understanding of its functionalities and the principles underpinning its design.
Core Methodologies and Features:
Integrated Systems: Universal Algo is built around six core systems, each contributing unique analytical perspectives to enhance trade signal reliability. These systems are designed to identify clear trend opportunities for significant gains, while also employing logic to navigate through ranging markets effectively.
Adaptive Market Logic: By incorporating volatility metrics, the algorithm dynamically adjusts to changing market conditions. This ensures that the strategy remains effective across different market regimes, aiming to reduce market noise and improve signal quality.
Selective Shorting Mechanism: While the primary focus is on capturing long positions, it includes an optional shorting feature. This can be activated by users to adapt the strategy during macro downtrends, thus providing a flexible approach to market participation.
Backtesting and Forward-Testing Rigor : The strategy has undergone rigorous testing to validate its performance and reliability. It demonstrates prudent risk management by optimizing conditions under which short positions are considered, aiming to mitigate drawdowns and preserve capital.
Operational Parameters:
Customization Options: The script offers a range of user inputs, allowing for customization of the backtesting starting date, the decision to display the strategy equity curve, among other settings. These inputs cater to diverse trading needs and preferences, offering users control over their strategy implementation.
Transparency and Logic Insight: While specific calculation details and proprietary indicators are integral to maintaining the uniqueness of Universal Algo , the strategy is grounded on well-established financial analysis techniques. These include momentum analysis, volatility assessments, and adaptive thresholding, among others, to formulate its trade signals.
Realistic Trading Conditions : Backtesting, considered realistic trading conditions, including appropriate account size, commission, slippage, and sustainable risk levels per trade. The strategy is designed and tested with a focus on achieving a balance between risk and reward, striving for robustness and reliability rather than unrealistic profitability promises.
Concluding Thoughts:
Universal Algo is offered to the TradingView community as a tool for traders seeking to enhance their market analysis and trading strategies. Its development is driven by a commitment to quality, innovation, and adaptability, aiming to provide valuable insights and decision-support in various market conditions. Potential users are encouraged to evaluate Universal Algo within the context of their overall trading approach and objectives.
TrippleMACDCryptocurrency Scalping Strategy for 1m Timeframe
Introduction:
Welcome to our cutting-edge cryptocurrency scalping strategy tailored specifically for the 1-minute timeframe. By combining three MACD indicators with different parameters and averaging them, along with applying RSI, we've developed a highly effective strategy for maximizing profits in the cryptocurrency market. This strategy is designed for automated trading through our bot, which executes trades using hooks. All trades are calculated for long positions only, ensuring optimal performance in a fast-paced market.
Key Components:
MACD (Moving Average Convergence Divergence):
We've utilized three MACD indicators with varying parameters to capture different aspects of market momentum.
Averaging these MACD indicators helps smooth out noise and provides a more reliable signal for trading decisions.
RSI (Relative Strength Index):
RSI serves as a complementary indicator, providing insights into the strength of bullish trends.
By incorporating RSI, we enhance the accuracy of our entry and exit points, ensuring timely execution of trades.
Strategy Overview:
Long Position Entries:
Initiate long positions when all three MACD indicators signal bullish momentum and the RSI confirms bullish strength.
This combination of indicators increases the probability of successful trades, allowing us to capitalize on uptrends effectively.
Utilizing Linear Regression:
Linear regression is employed to identify consolidation phases in the market.
Recognizing consolidation periods helps us avoid trading during choppy price action, ensuring optimal performance.
Suitability for Grid Trading Bots:
Our strategy is well-suited for grid trading bots due to frequent price fluctuations and opportunities for grid activation.
The strategy's design accounts for price breakthroughs, which are advantageous for grid trading strategies.
Benefits of the Strategy:
Consistent Performance Across Cryptocurrencies:
Through rigorous testing on various cryptocurrency futures contracts, our strategy has demonstrated favorable results across different coins.
Its adaptability makes it a versatile tool for traders seeking consistent profits in the cryptocurrency market.
Integration of Advanced Techniques:
By integrating multiple indicators and employing linear regression, our strategy leverages advanced techniques to enhance trading performance.
This strategic approach ensures a comprehensive analysis of market conditions, leading to well-informed trading decisions.
Conclusion:
Our cryptocurrency scalping strategy offers a sophisticated yet user-friendly approach to trading in the fast-paced environment of the 1-minute timeframe. With its emphasis on automation, accuracy, and adaptability, our strategy empowers traders to navigate the complexities of the cryptocurrency market with confidence. Whether you're a seasoned trader or a novice investor, our strategy provides a reliable framework for achieving consistent profits and maximizing returns on your investment.
NASDAQ 100 Peak Hours StrategyNASDAQ 100 Peak Hours Trading Strategy
Description
Our NASDAQ 100 Peak Hours Trading Strategy leverages a carefully designed algorithm to trade within specific hours of high market activity, particularly focusing on the first two hours of the trading session from 09:30 AM to 11:30 AM GMT-5. This period is identified for its increased volatility and liquidity, offering numerous trading opportunities.
The strategy incorporates a blend of technical indicators to identify entry and exit points for both long and short positions. These indicators include:
Exponential Moving Averages (EMAs) : A short-term 9-period EMA and a longer-term 21-period EMA to determine the market trend and momentum.
Relative Strength Index (RSI) : A 14-period RSI to gauge the market's momentum.
Average True Range (ATR) : A 14-period ATR to assess market volatility and to set dynamic stop losses and trailing stops.
Volume Weighted Average Price (VWAP) : To identify the market's average price weighted by volume, serving as a benchmark for the trading day.
Our strategy uniquely applies a volatility filter using the ATR, ensuring trades are only executed in conditions that favor our setup. Additionally, we consider the direction of the EMAs to confirm the market's trend before entering trades.
Originality and Usefulness
This strategy stands out by combining these indicators within the NASDAQ 100's peak hours, exploiting the specific market conditions that prevail during these times. The inclusion of a volatility filter and dynamic stop-loss mechanisms based on the ATR provides a robust method for managing risk.
By focusing on the early trading hours, the strategy aims to capture the initial market movements driven by overnight news and the opening rush, often characterized by higher volatility. This approach is particularly useful for traders looking to maximize gains from short-term fluctuations while limiting exposure to longer-term market uncertainty.
Strategy Results
To ensure the strategy's effectiveness and reliability, it has undergone rigorous backtesting over a significant dataset to produce a sample size of more than 100 trades. This testing phase helps in identifying the strategy's potential in various market conditions, its consistency, and its risk-to-reward ratio.
Our backtesting adheres to realistic trading conditions, accounting for slippage and commission to reflect actual trading scenarios accurately. The strategy is designed with a conservative approach to risk management, advising not to risk more than 5-10% of equity on a single trade. The default settings in the script align with these principles, ensuring that users can replicate our tested conditions.
Using the Strategy
The strategy is designed for simplicity and ease of use:
Trade Hours : Focuses on 09:30 AM to 11:30 AM GMT-5, during the NASDAQ 100's peak activity hours.
Entry Conditions : Trades are initiated based on the alignment of EMAs, RSI, VWAP, and the ATR's volatility filter within the designated time frame.
Exit Conditions : Includes dynamic trailing stops based on ATR, a predefined time exit strategy, and a trend reversal exit condition for risk management.
This script is a powerful tool for traders looking to leverage the NASDAQ 100's peak hours, providing a structured approach to navigating the early market hours with a robust set of criteria for making informed trading decisions.
CVD Divergence Strategy.1.mmThis is the matching Strategy version of Indicator of the same name.
As a member of the K1m6a Lions discussion community we often use versions of the Cumulative Volume Delta indicator
as one of our primary tools along with RSI, RSI Divergences, Open interest, Volume Profile, TPO and Fibonacci levels.
We also discuss visual interpretations of CVD Divergences across multiple time frames much like RSI divergences.
RSI Divergences can be identified as possible Bullish reversal areas when the RSI is making higher low points while
the price is making lower low points.
RSI Divergences can be identified as possible Bearish reversal areas when the RSI is making lower high points while
the price is making higher high points.
CVD Divergences can also be identified the same way on any timeframe as possible reversal signals. As with RSI, these Divergences
often occur as a trend's momentum is giving way to lower volume and areas when profits are being taken signaling a possible reversal
of the current trending price movement.
Hidden Divergences are identified as calculations that may be signaling a continuation of the current trend.
Having not found any public domain versions of a CVD Divergence indicator I have combined some public code to create this
indicator and matching strategy. The calculations for the Cumulative Volume Delta keep a running total for the differences between
the positive changes in volume in relation to the negative changes in volume. A relative upward spike in CVD is created when
there is a large increase in buying vs a low amount of selling. A relative downward spike in CVD is created when
there is a large increase in selling vs a low amount of buying.
In the settings menu, the is a drop down to be used to view the results in alternate timeframes while the chart remains on current timeframe. The Lookback settings can be adjusted so that the divs show on a more local, spontaneous level if set at 1,1,60,1. For a deeper, wider view of the divs, they can be set higher like 7,7,60,7. Adjust them all to suit your view of the divs.
To create this indicator/strategy I used a portion of the code from "Cumulative Volume Delta" by @ contrerae which calculates
the CVD from aggregate volume of many top exchanges and plots the continuous changes on a non-overlay indicator.
For the identification and plotting of the Divergences, I used similar code from the Tradingview Technical "RSI Divergence Indicator"
This indicator should not be used as a stand-alone but as an additional tool to help identify Bullish and Bearish Divergences and
also Bullish and Bearish Hidden Divergences which, as opposed to regular divergences, may indicate a continuation.
Inside Candle StrategyIntroduction
The Inside Candle Breakout Strategy leverages the concept of inside candles as a primary signal for potential breakouts. Unlike common trend-following or scalping strategies, this method focuses on the volatility squeeze indicated by inside candles and aims to capture the momentum that follows these periods of consolidation. The strategy's originality lies in its specific integration of timeframes for signal detection and its application across diverse market conditions without relying on conventional trend indicators.
Strategy Description and Mechanics
Inside Candle Identification: At the heart of this strategy is the detection of inside candles, defined as candles fully contained within the range of the preceding candle. This pattern signifies a temporary balance between buyers and sellers, often preceding significant price movements. The strategy scans for these candles within a user-specified timeframe in the input section of the settings of the strategy, allowing for tailored signal generation based on individual trading preferences.
Entry Points and Market Entries: Upon identifying an inside candle and only once this candle closes, the strategy prepares to enter a trade in the direction of the breakout. Trades are executed in the timeframe selected on the chart, ensuring that entry points are aligned with real-time market movements. This process highlights the strategy's adaptability, making it suitable for various trading styles, from day trading to swing trading.
Overlay Indicator for Enhanced Market Analysis: Accompanying the breakout signals is an overlay indicator comprising two moving averages and a volatility cloud. This feature serves as a secondary tool for market analysis, offering insights into the prevailing market trend and volatility levels. While it doesn't influence the entry or exit signals directly, it provides traders with additional context for refining their decisions, enhancing the strategy's utility. This assistance tool is composed by one moving average and a second line which is calculated adding or subtracting the historical volatility of the asset on the moving average, depending on his momentum.
Strategy Results and Commitment to Realism
Backtesting Protocol: In our commitment to transparency and realism, backtesting results are derived from a dataset that ensures a sufficient number of trades (over 100) to validate the strategy's effectiveness. This approach underscores our dedication to providing traders with reliable and actionable insights.
Risk Management and Trade Sizing: Recognizing the importance of sustainable trading practices, the strategy incorporates strict risk management guidelines. Trades are sized to ensure that only a small percentage of equity is risked on a single trade, adhering to widely accepted risk tolerance levels. The initial account size for this script is set to 10000$.
Strategy Defaults and Justification: The default properties of the strategy, including the risk-reward ratio, average length for moving averages, and other parameters, are carefully chosen based on extensive testing and analysis. These settings represent a balanced approach, aiming to optimize the strategy's performance across a variety of market conditions.
Strategy Components:
- Inside Candles: An inside candle occurs when a candle's high and low are completely contained within the high and low of the previous candle. This pattern indicates a period of consolidation or indecision in the market, often preceding a significant price movement. The strategy detects inside candles based on the user-selected timeframe, allowing traders to capture potential breakouts.
Indicator Overlays:
- Moving Average: A simple moving average (SMA) is calculated over a user-defined length (`Average Length`), providing a dynamic baseline to gauge the market's direction. The strategy offers an option (`Show Moving Average`) to display or hide this moving average on the chart, giving traders control over the visual complexity.
- Volatility Measurement: Alongside the moving average, the strategy assesses market volatility using the standard deviation of the closing prices over the same period defined by the `Average Length`. The moving average is adjusted upwards or downwards by this volatility measure, creating a dynamic channel that reflects the current market conditions.
- Color Gradients for Volatility: The strategy uses a color gradient to fill the area between the moving average and its volatility-adjusted counterpart. This gradient visually represents the volatility level, transitioning from gray (low volatility) to a lighter shade (higher volatility), aiding in the assessment of market sentiment and volatility.
Trading Entries:
- Long Entry: A long position is triggered when the closing price exceeds the high of an inside candle, indicating potential bullish momentum. The strategy places a stop-loss at the low of the inside candle and sets a take-profit level based on the predefined risk-reward ratio (`RR Ratio`).
- Short Entry: Conversely, a short position is initiated when the closing price falls below the low of an inside candle, suggesting bearish pressure. A stop-loss is set at the high of the inside candle, with the take-profit level adjusted according to the risk-reward ratio.
Customization Settings:
- Timeframe: Traders can select the desired timeframe for inside candle detection, tailoring the strategy to fit various trading styles and time horizons.
- RR Ratio: The risk-reward ratio is adjustable, allowing traders to manage the potential risk and return of each trade according to their risk tolerance.
- Average Length: This setting determines the period over which the moving average and volatility are calculated, affecting the sensitivity of the strategy to price movements.
- Visual Settings: Users can customize the appearance of the strategy on their charts, including the colors of the moving average and volatility lines, as well as the line width, enhancing chart readability and personal preference adherence.
Disclaimer
Trading involves significant risk, and it is crucial for traders to conduct their own due diligence before engaging with any strategy. The Inside Candle Breakout Strategy is presented for informational purposes only and does not constitute financial advice.
