QuantBuilder | FractalystWhat's the strategy's purpose and functionality?
QuantBuilder is designed for both traders and investors who want to utilize mathematical techniques to develop profitable strategies through backtesting on historical data.
The primary goal is to develop profitable quantitive strategies that not only outperform the underlying asset in terms of returns but also minimize drawdown.
For instance, consider Bitcoin (BTC), which has experienced significant volatility, averaging an estimated 200% annual return over the past decade, with maximum drawdowns exceeding -80%. By employing this strategy with diverse entry and exit techniques, users can potentially seek to enhance their Compound Annual Growth Rate (CAGR) while managing risk to maintain a lower maximum drawdown.
While this strategy employs quantitative techniques, including mathematical methods such as probabilities and positive expected values, it demonstrates exceptional efficacy across all markets. It particularly excels in futures, indices, stocks, cryptocurrencies, and commodities, leveraging their inherent trending behaviors for optimized performance.
In both trending and consolidating market conditions, QuantBuilder employs a combination of multi-timeframe probabilities, expected values, directional biases, moving averages and diverse entry models to identify and capitalize on bullish market movements.
How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
1. Trading:
- Designed for traders looking to capitalize on bullish markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for both swing and intraday trading with a focus on probabilities and risk per trade approach.
2. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully/partially investing in the asset during bullish conditions.
How does the strategy identify market structure? What are the underlying calculations?
The strategy utilizes an efficient logic with for loops to pinpoint the first swing candle featuring a pivot of 2, establishing the point at which the break of structure begins.
What entry criteria are used in this script? What are the underlying calculations?
The script utilizes two entry models: BreakOut and fractal.
Underlying Calculations:
Breakout: The script assigns the most recent swing high to a variable. When the price closes above this level and all other conditions are met, the script executes a breakout entry (conservative approach).
Fractal: The script identifies a swing low with a period of 2. Once this condition is met, the script executes the trade (aggressive approach).
How does the script calculate probabilities? What are the underlying calculations?
The script calculates probabilities by monitoring price interactions with liquidity levels. Here’s how the underlying calculations work:
Tracking Price Hits: The script counts the number of times the price taps into each liquidity side after the EQM level is activated. This data is stored in an array for further analysis.
Sample Size Consideration: The total number of price interactions serves as the sample size for calculating probabilities.
Probability Calculation: For each liquidity side, the script calculates the probability by taking the average of the recorded hits. This allows for a dynamic assessment of the likelihood that a particular side will be hit next, based on historical performance.
Dynamic Adjustment: As new price data comes in, the probabilities are recalculated, providing real-time aduptive insights into market behavior.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
How does the script calculate expected values? What are the underlying calculations?
The script calculates expected values by leveraging the probabilities of winning and losing trades, along with their respective returns. The process involves the following steps:
This quantitative methodology provides a robust framework for assessing the expected performance of trading strategies based on historical data and backtesting results.
How is the contextual bias calculated? What are the underlying calculations?
The contextual bias in the QuantBuilder script is calculated through a structured approach that assesses market structure based on swing highs and lows. Here’s how it works:
Identification of Swing Points: The script identifies significant swing points using a defined pivot logic, focusing on the first swing high and swing low. This helps establish critical levels for determining market structure.
Break of Structure (BOS) Assessment:
Bullish BOS: The script recognizes a bullish break of structure when a candle closes above the first swing high, followed by at least one swing low.
Bearish BOS: Conversely, a bearish break of structure is identified when a candle closes below the first swing low, followed by at least one swing high.
Bias Assignment: Based on the identified break of structure, the script assigns directional biases:
A bullish bias is assigned if a bullish BOS is confirmed.
A bearish bias is assigned if a bearish BOS is confirmed.
Quantitative Evaluation: Each identified bias is quantitatively evaluated, allowing the script to assign numerical values representing the strength of each bias. This quantification aids in assessing the reliability of market sentiment across multiple timeframes.
What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
- Initial Stop-loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14)
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
- Trailing Stop-Loss:
One of the key elements of this strategy is its ability to detect structural liquidity and structural invalidation levels across multiple timeframes to trail the stop-loss once the trade is in running profits.
By utilizing this approach, the strategy allows enough room for price to run.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
What tables are available in this script?
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Total Commission: Displays the cumulative commissions incurred from all trades executed within the selected backtesting window. This value is derived by summing the commission fees for each trade on your chart.
Average Commission: Represents the average commission per trade, calculated by dividing the Total Commission by the total number of closed trades. This metric is crucial for assessing the impact of trading costs on overall profitability.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month and year.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- UI Table: A user-friendly table that allows users to view and save the selected strategy parameters from user inputs. This table enables easy access to key settings and configurations, providing a straightforward solution for saving strategy parameters by simply taking a screenshot with Alt + S or ⌥ + S.
User-input styles and customizations:
To facilitate studying historical data, all conditions and filters can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
How to Use This Quantitive Strategy Builder to Create a Profitable Edge and System?
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker/prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 200 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
What makes this strategy original?
QuantBuilder stands out due to its unique combination of quantitative techniques and innovative algorithms that leverage historical data for real-time trading decisions. Unlike most algorithmic strategies that work based on predefined rules, this strategy adapts to real-time market probabilities and expected values, enhancing its reliability. Key features include:
Mathematical Framework: The strategy integrates advanced mathematical concepts, such as probabilities and expected values, to assess trade viability and optimize decision-making.
Multi-Timeframe Analysis: By utilizing multi-timeframe probabilities, QuantBuilder provides a comprehensive view of market conditions, enhancing the accuracy of entry and exit points.
Dynamic Market Structure Identification: The script employs a systematic approach to identify market structure changes, utilizing a blend of swing highs and lows to detect contextual/direction bias of the market.
Built-in Trailing Stop Loss: The strategy features a dynamic trailing stop loss based on multi-timeframe analysis of market structure. This allows traders to lock in profits while adapting to changing market conditions, ensuring that exits are executed at optimal levels without prematurely closing positions.
Robust Performance Metrics: With detailed performance tables and visualizations, users can easily evaluate strategy effectiveness and adjust parameters based on historical performance.
Adaptability: The strategy is designed to work across various markets and timeframes, making it versatile for different trading styles and objectives.
Suitability for Investors and Traders: QuantBuilder is ideal for both investors and traders looking to rely on mathematically proven data to create profitable strategies, ensuring that decisions are grounded in quantitative analysis.
These original elements combine to create a powerful tool that can help both traders and investors to build and refine profitable strategies based on algorithmic quantitative analysis.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Quanttrading
Matrix Glitch | FractalystThe Matrix Glitch indicator is a visually engaging tool for traders, inspired by the iconic Matrix movie effects. It overlays price charts with dynamic, multi-colored glitches that sync with market data, creating a striking, almost surreal visual experience.
The indicator uses characters from various languages (e.g., Japanese, Chinese, Russian, English) to mimic the digital rain effect from the movies. Users can select a language, which activates a corresponding array of characters. These characters are randomly picked from the chosen array and displayed on the chart.
Underlying Calculations and Logic
Arrays in the Indicator
1- Character Management:
The script uses arrays to store sets of characters representing different symbols or alphabets. These arrays allow the indicator to dynamically select and update characters for display. Each element in these arrays corresponds to a specific character that will be used to populate the grid.
2- Current and Previous States:
Arrays are employed to keep track of the current state of characters that are displayed on the grid. Simultaneously, another set of arrays records the previous state of these characters. This dual-state management allows the script to smoothly transition between updates, handling changes in characters and visual effects like fading.
3- Transparency Control:
Transparency levels for each character in the grid are managed through arrays. These arrays store the opacity values, ensuring that each character has the appropriate level of transparency. By comparing the current and previous transparency states, the script can create effects like gradual fading or intensifying visibility.
4- Rain Effect Simulation:
To create the "rain" effect, the script maintains arrays that simulate the falling text by continuously updating the position and visibility of characters. As new characters enter the top of the grid, older ones are removed from the bottom, with their transparency levels adjusted to simulate movement.
5- Operational Flow:
Initialization : Arrays are initialized to manage both the characters and their transparency. This setup allows the script to handle the dynamic display efficiently.
Updates : During each cycle, new characters are selected and old characters are shifted accordingly. The arrays ensure that both the content and appearance of the grid are updated seamlessly.
Rendering : The arrays dictate how characters and their transparency are rendered on the grid, ensuring a cohesive and visually appealing effect.
Here's how to use the indicator step-by-step:
1- Apply the Indicator to Your Charts:
Begin by adding the indicator to your chart. This will activate the visual effect on your selected trading instrument or time frame.
Select Your Preferred Language of the Matrix Characters:
In the settings, choose the language or symbol set you want the matrix characters to display. This could be anything from traditional matrix-style characters to different alphabets or custom symbols.
2- Choose the Matrix Effect (Rain, Burst):
Decide on the type of visual effect you prefer. You can select from options like the classic "rain" effect, where characters fall from the top of the screen, or a "burst" effect, where characters explode outward or appear in a different dynamic pattern.
3- Adjust the Color According to Your Preference:
Customize the color of the matrix characters to suit your aesthetic or chart theme. You can select from a range of colors or even set up a gradient for more complex visual effects.
4- Adjust the Width and Height of the Matrix According to Your Screen:
Fine-tune the dimensions of the matrix display. Set the width and height so that the matrix fits perfectly on your screen, ensuring that it aligns well with other chart elements and doesn't obstruct your view.
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What Makes the Matrix Glitch Indicator Unique?
Language Selection:
Customizable Language: Unlike many indicators that might offer static or limited visual elements, the Matrix Glitch Indicator allows users to choose from a variety of languages for the characters displayed. This feature not only personalizes the user experience but also adds a cultural or linguistic element to trading charts. Users can select languages like Japanese, Chinese, Russian, or English, and many more.
This flexibility ensures that traders from different backgrounds can feel a connection with their charts through familiar or exotic scripts.
Dynamic Effects:
Effect Modes: The indicator offers two distinct modes - Rain Mode and Burst Mode. In Rain Mode, characters fall from the top of the chart, mimicking the iconic digital rain from the Matrix films.
In Burst Mode, characters radiate outward from a central point, creating a unique visual effect that can be synchronized with market volatility.
This dual-mode functionality allows traders to choose how they want their data to be visually represented, providing both aesthetic variety and potentially different insights into market behavior.
Color Customization:
Full Color Control: The ability to fully customize the color of the characters is a standout feature. Traders can match the indicator's colors to their trading platform's theme, their mood, or even specific market conditions (e.g., red for downturns, green for upturns). This level of customization not only aids in creating a personalized trading environment but can also serve as a visual cue for different market states.
Universal Display Compatibility:
Adjustability for All Displays: The indicator is designed to be fully adjustable for various screen resolutions and sizes. This ensures that whether you're trading on a high-resolution monitor, a laptop, or even a mobile device, the Matrix Glitch effect remains clear and impactful without compromising on the functionality of the trading chart. This adaptability is crucial in an era where trading can happen anywhere, making the indicator a versatile tool for traders on the go or in a static setup.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
AlgoBuilder [Mean-Reversion] | FractalystWhat's the strategy's purpose and functionality?
This strategy is designed for both traders and investors looking to rely and trade based on historical and backtested data using automation.
The main goal is to build profitable mean-reversion strategies that outperform the underlying asset in terms of returns while minimizing drawdown.
For example, as for a benchmark, if the S&P 500 (SPX) has achieved an estimated 10% annual return with a maximum drawdown of -57% over the past 20 years, using this strategy with different entry and exit techniques, users can potentially seek ways to achieve a higher Compound Annual Growth Rate (CAGR) while maintaining a lower maximum drawdown.
Although the strategy can be applied to all markets and timeframes, it is most effective on stocks, indices, future markets, cryptocurrencies, and commodities and JPY currency pairs given their trending behaviors.
In trending market conditions, the strategy employs a combination of moving averages and diverse entry models to identify and capitalize on upward market movements. It integrates market structure-based moving averages and bands mechanisms across different timeframes and provides exit techniques, including percentage-based and risk-reward (RR) based take profit levels.
Additionally, the strategy has also a feature that includes a built-in probability function for traders who want to implement probabilities right into their trading strategies.
Performance summary, weekly, and monthly tables enable quick visualization of performance metrics like net profit, maximum drawdown, profit factor, average trade, average risk-reward ratio (RR), and more.
This aids optimization to meet specific goals and risk tolerance levels effectively.
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How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
Trading:
1. Trading:
- Designed for traders looking to capitalize on bullish trending markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for active trading with a focus on mean-reversion and risk per trade approach.
◓: Mode | %: Risk percentage per trade
3. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully investing in the asset during bullish conditions.
- ◓: Mode | %: Risk not applied (In investing mode, the strategy uses 10% of equity to buy the asset)
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What's is FRMA? How does the triple bands work? What are the underlying calculations?
Middle Band (FRMA):
The middle band is the core of the FRMA system. It represents the Fractalyst Moving Average, calculated by identifying the most recent external swing highs and lows in the market structure.
By determining these external swing pivot points, which act as significant highs and lows within the market range, the FRMA provides a unique moving average that adapts to market structure changes.
Upper Band:
The upper band shows the average price of the most recent external swing highs.
External swing highs are identified as the highest points between pivot points in the market structure.
This band helps traders identify potential overbought conditions when prices approach or exceed this upper band.
Lower Band:
The lower band shows the average price of the most recent external swing lows.
External swing lows are identified as the lowest points between pivot points in the market structure.
The script utilizes this band to identify potential oversold conditions, triggering entry signals as prices approach or drop below the lower band.
Adjustments Based on User Inputs:
Users can adjust how the upper and lower bands are calculated based on their preferences:
Upper/Lower: This method calculates the average bands using the prices of external swing highs and lows identified in the market.
Percentage Deviation from FRMA: Alternatively, users can opt to calculate the bands based on a percentage deviation from the middle FRMA. This approach provides flexibility to adjust the width of the bands relative to market conditions and volatility.
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What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
This comparison filter can be turned on (>) or off.
For example, you can set the filter so that MA#1 > MA#2, meaning the first moving average must be above the second one before the script looks for entry conditions. This adds an extra layer of trend confirmation, ensuring that trades are only taken in more favorable market conditions.
⍺: MA Period | Σ: MA Timeframe
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What entry modes are used in this strategy? What are the underlying calculations?
The strategy by default uses two different techniques for the entry criteria with user-adjustable left and right bars: Breakout and Fractal.
1. Breakout Entries :
- The strategy looks for pivot high points with a default period of 3.
- It stores the most recent high level in a variable.
- When the price crosses above this most recent level, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot high left bars period | ◨: Pivot high right bars period
2. Fractal Entries :
- The strategy looks for pivot low points with a default period of 3.
- When a pivot low is detected, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot low left bars period | ◨: Pivot low right bars period
2. Hunt Entries :
- The strategy identifies a candle that wicks through the lower FRMA band.
- It waits for the next candle to close above the low of the wick candle.
- When this condition is met and the bar is closed, the strategy takes the buy entry.
By utilizing these entry modes, the strategy aims to capitalize on bullish price movements while ensuring that the necessary conditions are met to validate the entry points.
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What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
Initial Stop-Loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14).
Example - ATR (14) * 2
⍺: ATR period | Σ: ATR Multiplier
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
Example - ADR (20) * 2
⍺: ADR period | Σ: ADR Multiplier
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
Application in Strategy (ATR/ADR):
- The strategy calculates the current bar's ADR/ATR with a user-defined period.
- It then multiplies the ADR/ATR by a user-defined multiplier to determine the initial stop-loss level.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
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What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
Example:
If the entry price is $100, the initial risk is $10, and the RR ratio is 2, the break-even level is $100 + ($10 * 2) = $120.
FRMA Based:
Moves the stop-loss to break-even when the price hits the FRMA level at which the entry was taken.
Calculation:
Break-even level = FRMA level at the entry
Example:
If the FRMA level at entry is $102, the break-even level is set to $102 when the price reaches $102.
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
When Both Percentage (%) Based and RR Based Take Profit Levels Are Off:
The script will adjust the take profit level to the higher FRMA band set within user inputs.
Calculation:
Take profit level = Higher FRMA band length/timeframe specified by the user.
This ensures that when neither percentage-based nor risk-to-reward-based take profit methods are enabled, the strategy defaults to using the higher FRMA band as the take profit level, providing a consistent and structured approach to profit-taking.
For TP1 and TP2, it's specifying the price levels at which the position is partially or fully closed based on the chosen method (percentage or RR) above the entry price.
These calculations are crucial for managing risk and optimizing profitability in the strategy.
⍺: BE/TP type (%/RR) | Σ: how many RR/% above the current price
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What's the ADR filter? What does it do? What are the underlying calculations?
The Average Day Range (ADR) measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
The period of the ADR filter used in this strategy is tied to the same period you've used for your initial stop-loss.
Users can define the minimum ADR they want to be met before the script looks for entry conditions.
ADR Bias Filter:
- Compares the current bar ADR with the ADR (Defined by user):
- If the current ADR is higher, it indicates that volatility has increased compared to ADR (DbU).(⬆)
- If the current ADR is lower, it indicates that volatility has decreased compared to ADR (DbU).(⬇)
Calculations:
1. Calculate ADR:
- Average the high prices over the specified period.
- Average the low prices over the same period.
- Find the difference between these average values in %.
2. Current ADR vs. ADR (DbU):
- Calculate the ADR for the current bar.
- Calculate the ADR (DbU).
- Compare the two values to determine if volatility has increased or decreased.
By using the ADR filter, the strategy ensures that trades are only taken in favorable market conditions where volatility meets the user's defined threshold, thus optimizing entry conditions and potentially improving the overall performance of the strategy.
>: Minimum required ADR for entry | %: Current ADR comparison to ADR of 14 days ago.
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What's the probability filter? What are the underlying calculations?
The probability filter is designed to enhance trade entries by using buyside liquidity and probability analysis to filter out unfavorable conditions.
This filter helps in identifying optimal entry points where the likelihood of a profitable trade is higher.
Calculations:
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Equilibrium levels.
3. Understanding probability calculations
1. Upon the formation of a new range, the script waits for the price to reach and tap into equilibrium or the 50% level. Status: "⏸" - Inactive
2. Once equilibrium is tapped into, the equilibrium status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
5. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
Example - BSL > 55%
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What's the range length Filter? What are the underlying calculations?
The range length filter identifies the price distance between buyside and sellside liquidity levels in percentage terms. When enabled, the script only looks for entries when the minimum range length is met. This helps ensure that trades are taken in markets with sufficient price movement.
Calculations:
Range Length (%) = ( ( Buyside Level − Sellside Level ) / Current Price ) ×100
Range Bias Identification:
Bullish Bias: The current range price has broken above the previous external swing high.
Bearish Bias: The current range price has broken below the previous external swing low.
Example - Range length filter is enabled | Range must be above 1%
>: Minimum required range length for entry | %: Current range length percentage in a (Bullish/Bearish) range
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What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
Increased Flexibility: The filter provides increased flexibility, allowing traders to adapt the strategy to their specific needs and preferences.
Example - Day filter | Session Filter
θ: Session time | Exchange time-zone
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What tables are available in this script?
Table Type:
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- OFF: Hides the performance table.
Profit Color:
- Allows users to set the color for representing profit in the performance table, helping to quickly distinguish profitable periods.
Loss Color:
- Allows users to set the color for representing loss in the performance table, helping to quickly identify loss-making periods.
These customizable tables provide traders with flexible and detailed performance analysis, aiding in better strategy evaluation and optimization.
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User-input styles and customizations:
To facilitate studying historical data, all conditions and rules can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
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How to Use This Algobuilder to Create a Profitable Edge and System:
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker or prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 100 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade value is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
Automation:
- Once you’re confident in your strategy, you can use the automation section to connect the algorithm to your broker or prop firm.
- Trade a fully automated and backtested trading strategy, allowing for hands-free execution and management.
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What makes this strategy original?
1. Incorporating direct integration of probabilities into the strategy.
2. Utilizing built-in market structure-based moving averages across various timeframes.
4. Offering both investing and trading strategies, facilitating optimization from different perspectives.
5. Automation for efficient execution.
6. Providing a summary table for instant access to key parameters of the strategy.
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How to use automation?
For Traders:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Enter your PineConnector License ID in the designated field.
3. Specify the desired risk level.
4. Provide the Metatrader symbol.
5. Check for chart updates to ensure the automation table appears on the top right corner, displaying your License ID, risk, and symbol.
6. Set up an alert with the strategy selected as Condition and the Message as {{strategy.order.alert_message}}.
7. Activate the Webhook URL in the Notifications section, setting it as the official PineConnector webhook address.
8. Double-check all settings on PineConnector to ensure the connection is successful.
9. Create the alert for entry/exit automation.
For Investors:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Choose "Investing" in the user-input settings.
3. Create an alert with a specified name.
4. Customize the notifications tab to receive alerts via email.
5. Buying/selling alerts will be triggered instantly upon entry or exit order execution.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
OptiRange | FractalystWhat’s the purpose of this indicator?
This indicator is designed to integrate probabilities with liquidity levels, while also providing a mechanical method for identifying market structure by using Fractals by Williams.
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How does this indicator identify market structure?
This script identifies breaks of market structure by analyzing candle closures above or below swing levels.
As soon as a candle has closed above or below the initial swing on your charts, the script validates that there is at least one swing preceding the break before confirming it as a structural break.
Once a break is occured then it assigns a numeric ID to the break starting from 1 and draws two extremities: one as liquidity and the other as invalidation (LIQ/INV).
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What do the extremities show us on the charts?
you'll see two clear extremities on your charts:
1. The first extremity represents the structural liquidity level. (LIQ)
2. The other extremity indicates the level that, if price breaks through it, results in a structural shift to the opposite side. (INV)
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How does it calculate probabilities?
Each break of market structure, denoted as X, is assigned a unique ID, starting from X1 for the first break, X2 for the second, and so on.
The probabilities are calculated based on breaks holding, meaning price closing through the liquidity level, rather than invalidation. This probability is then divided by the total count of similar numeric breaks.
For example, if 75 out of 100 bullish X1s become X2, then the probability of X1 becoming X2 on your charts will be displayed as 80% in the following format: ⬆ 75%
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What are the Fractal blocks?
Fractal blocks refer to the most extreme swing candle within the latest break. They can serve as significant levels for price rejection and may guide movements toward the next break, often in confluence with probability analysis for added confirmation.
If the price retraces back to a bullish fractal block, we aim to look for buy/long positions. Conversely, if the price retraces back to a bearish fractal block, we aim to look for sell/short positions.
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What are mitigations?
Mitigations refer to specific price action occurrences identified by the script:
1- When the price reaches the most recent fractal block and confirms a swing candle, the script automatically draws a line from the swing to the fractal block bar and labels it with a checkmark.
1- If the price wicks through the invalidation level and then retraces back to the fractal block while forming a swing candle, the script labels this as a double mitigation on the chart.
This level will serve as the next potential invalidation level if a break occurs in the same direction.
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What does the bottom table display?
The bottom table presents numeric breaks across multiple timeframes, with the text color indicating the trend direction. Enabling traders to assess the higher timeframes market trend without needing to switch between timeframes manually.
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How to use the indicator?
1. Add "OptiRange | Fractalyst" to your TradingView chart.
2. Choose the pair you want to analyze or trade.
3. Start with the 12-month timeframe.
4. Use the table bias with the maximal settings to find the lowest timeframe that’s showing you the mitigation (✓)
5. Confirm that the probability of the current liquidity is higher than 50%.
6. Place your limit order at the Fibonacci level of 0.618 of the mitigation candle.
7. Set your stop-loss at the mitigation level.
8. Determine your take profit based on the liquidity of the current timeframe, or if possible, the liquidity of a higher timeframe in the same direction; otherwise, use the liquidity of the current timeframe.
9. Risk adjustment and Trade management based on your personal preferences.
Example:
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User-input settings and customizations
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What makes this indicator original?
- This script leverages Fractals, a fundamental concept in many trading methodologies.
- For a break to be considered valid, price must have at least two swings:
a swing high followed by a swing low for bullish breaks and a swing low follow by a swing high for bearish breaks.
- This means that each swing point is confirmed by the formation of two candles on its left and two candles on its right, totaling 5 candles for each swing high and swing low, thus requiring 10 candles overall. (This strict rule ensures a thorough assessment of market structure before confirming a break.)
- The script assigns a unique numerical ID to each break of structure, starting from 1.
This numbering system enables the script to calculate the probability of the most recent break becoming the next break, while also factoring in the trend direction.
- Additionally, this script provides insights into higher timeframes' break IDs in the bottom/top centre table, keeping traders informed about the overall higher timeframe picture.
- By integrating these methodologies, the script introduces a unique and systematic method for identifying market structure, thereby enhancing its originality in guiding trading decisions.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data. By utilizing our charting tools, the buyer acknowledges that neither the seller nor the creator assumes responsibility for decisions made using the information provided. The buyer assumes full responsibility and liability for any actions taken and their consequences, including potential financial losses. Therefore, by purchasing these charting tools, the customer acknowledges that neither the seller nor the creator is liable for any unfavorable outcomes resulting from the development, sale, or use of the products.
The buyer is responsible for canceling their subscription if they no longer wish to continue at the full retail price. Our policy does not include reimbursement, refunds, or chargebacks once the Terms and Conditions are accepted before purchase.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer.
Real Cummulative Delta (New TV Function)Thanks to the new TradingView indicator Up/Down Volume, it is now possible to get accurate information on Agression (market buying vs market selling)
However, as they only provide the value of delta, I've made this indicator to show the cummulative value, in the form of candles.
It is great to detect divergences in the macro and in the micro scale (As in divergences in each candle and divergences in higher or lower tops or bottoms)
Hope you can make good use of it!
Yield Trend Indicator - The Quant ScienceYield Trend Indicator - The Quant Science™ is a quantitative indicator representing percentage yields and average percentage yields of three different assets.
Percentage yields are fundamental data for all quantitative analysts. This indicator was created to offer immediate calculations and represent them through an indicator consisting of lines and columns. The columns represent the percentage yield of the current timeframe, for each asset. The lines represent the average percentage yield, of the current timeframe, for each asset.
The user easily adds tickers from the user interface and the algorithm will automatically create the quantitative data of the chosen assets.
The blue refers to the main asset, the main set on the chart.
The yellow refers to the second asset, added by the user interface.
The red refers to the third asset, added by the user interface.
The timeframe is for all assets the one set to the chart, if you use a chart with timeframe D, all data is processed on this timeframe. You can use this indicator on all timeframes without any restrictions.
The user can change the type of formula for calculating the average yield easily via the user interface. This software includes the following formulas:
1. SMA (Simple Moving Average)
2. EMA (Exponential Moving Average)
3. WMA (Weighted Moving Average)
4. VWMA (Volume Weighted Moving Average)
The user can customize the indicator easily through the user interface, changing colours and many other parameters to represent the data on the chart.
Ethereum OnChain Data Indicator - The Quant ScienceEthereum On Chain Data Indicator - The Quant Science™ is a quantitative indicator created for mid-long term analysis.
The indicator uses quantitative statistics to recreate a model that represents the most important data from the on-chain analysis for the Ethereum blockchain.
The on-chain data used to create this model are:
1. Total weekly transactions
2. Total monthly transactions
3. Frequency of transactions per second on a daily scale
4. Frequency of transactions per second on a weekly scale
5. Amount of Ethereum burned on a daily scale
6. Amount of Ethereum burned on a weekly scale
7. Volume of short positions on a daily scale
8. Volume of short positions on a weekly scale
9. Volume of short positions more/less than average on a daily scale
10. Volume of short positions more/less than average on a weekly scale
All these data were extrapolated and manipulated using the mean and standard deviation.
The end result is a powerful tool that enables mid-long term investors and traders to analyze on-chain data through quantitative analysis.
FEATURES
The blue color area refers to the average change in data on a weekly scale. The light blue colored area indicates the monthly changes in the data. It is interesting to observe the correlation relationship between price and times when short-run data increases compared to long-run data and vice versa.
The more intense purple histograms refer to the standard deviation of the mean change in data on an annual scale. Histograms of less intense purple color refer to the standard deviation of the mean variation of data on a monthly scale. It is interesting to observe the ratio of the standard deviation between two different time periods.
This indicator can be used to perform statistical comparative analysis for manual and mid-long term investments. It can also be used to create auto trading strategies when used and integrated within an algorithm.
On-chain data are updated every 24 hours, so the timeframes to be used for analysis with this indicator are: D, 4H, 1H.
SIMPLE CANDLESTICK PATTERN ALGO BACKTESTING - TESLA 4HMany traders spend a lot of time to create algorithms full of unrealistic and far from reality indicators and market conditions. With this script I want to help traders understand the advantage of the Pine language. Using indicators with no statistical foundation and creating algorithms with technical indicators and thousands of conditions is not always the right way to create an efficient tool.
With this script that we have called "SimpleBarPattern_LongOnly" we analyse the market through a simple condition, using bars or candles.
How it works
The condition is constructed as follows. You go long with 100% of the established capital and 0.03% commission. The first condition is that the minimum of the period under analysis falls below the opening level. The second condition is that the low of the period is below the low of the previous period. The third condition is that the close of the period is above the opening level. The final condition wants the current close to be higher than the previous open and higher than the previous close. We used a statistical approach in the creation of this script, some candlestick patterns that reflect these conditions are: Bullish Engulfing, Bullish Hammer and Morning Star .
This strategy aims to help traders make more accurate decisions while using candlesticks for their trading and scientifically demonstrates that candlesticks are valid statistical tools for financial analysis.
"SimpleBarPattern_LongOnly" is a very lightweight script created with Pine v5. We developed a user interface that can adjust the analysis period from a few days to several years.
The initial capital set is €1,000 (You can change this from the "Properties" section of the user interface).
Each individual trade uses 100% of the set capital, in this case €1,000.
The default commission per trade is 0.03% (You can change this in the "Properties" section of the user interface).
User Interface
1) General backtest time settings: Set the history period to be analysed
StartDate: backtest start date
StartMonth: backtest start month
StartYear: backtest start year
EndDate: backtest end day
EndMonth: backtest end month
EndYear: backtest end year
3) Stop Loss
4) Take Profit
Please do not hesitate to contact us for any questions or information.
Disclaimer
Be careful, the past is not a guarantee of future performance, so remember to use the script as a pure analysis tool. The developer takes no responsibility for any use other than research and analysis and can in no way be held liable for damages resulting from wrong use of this code.
Prime Distance Frame Quant Model for Risk Reward & Pivot PointsIn this script we take all of the prime numbers up to 100 and plot them as olive lines and then consider the distance between two adjacent plots and color code these distances with the fill function. This allows us to find higher and lower prime gaps allowing us to make much more informed decisions on our risk reward for a given trade and the levels where we should consider taking profit.
The Script includes scaling for all assets and is intended to be used for crypto trading.
Terminal : Important U.S Indices Change (%) DataHello.
This script is a simple U.S Indices Data Terminal.
You can also set the period to look back manually in the menu.
In this way, an idea can be obtained about Major U.S Indices.
Features
Value changes on a percentage basis (%)
Recently, due to increasing interest, the NQNACE index has been added.
Index descriptions are printed on the information panel.
Sentiment NYSE ARCA and AMEX indices added.
Indices
SP1! : S&P 500 Futures Index
DJI : Dow Jones Industrial Average Index
NDX : Nasdaq 100 Index
RUT : Russell 2000 Index
NYA : NYSE Composite Index
OSX : PHLX Oil Service Sector Index
HGX : PHLX Housing Sector Index
UTY : PHLX Utility Sector Index
SOX : PHLX Semiconductor Sector Index
SPSIBI : S&P Biotechnology Select Industry Index
XNG : NYSE ARCA Natural Gas Index
SPGSCI : S&P Goldman Sachs Commodity Index
XAU : PHLX Gold and Silver Sector Index
SPSIOP : S&P Oil and Gas Exploration and Production Select Industry Index
GDM : NYSE ARCA Gold Miners Index
DRG : NYSE ARCA Pharmaceutical Index
TOB : NYSE ARCA Tobacco Index
DFI : NYSE ARCA Defense Index
NWX : NYSE ARCA Networking Index
XCI : NYSE ARCA Computer Technology
XOI : AMEX Oil Index
XAL : AMEX Airline Index
NQNACE : Nasdaq Yewno North America Cannabis Economy Index
Terminal : USD Based Stock Markets Change (%)Hello.
This script is a simple USD Based Stock Markets Change (%) Data Terminal.
You can also set the period to look back manually in the menu.
In this way, an idea can be obtained about Countries' Stock Markets.
And you can observe the stock exchanges of relatively positive and negative countries from others.
Features
Value changes on a percentage basis (%)
Stock exchange values are calculated in dollar terms.
Due to the advantage of movement, future data were chosen instead of spot values on the required instruments.
Stock Markets
Usa : S&P 500 Futures
Japan: Nikkei 225 Futures
England: United Kingdom ( FTSE ) 100
Australia: Australia 200
Canada: S&P / TSX Composite
Switzerland: Swiss Market Index
New Zealand: NZX 50 Index
China: SSE Composite (000001)
Denmark: OMX Copenhagen 25 Index
Hong-Kong: Hang Seng Index Futures
India: Nifty 50
Norway: Oslo Bors All Share Index
Russia: MOEX Russia Index
Sweden: OMX Stockholm Index
Singapore: Singapore 30
Turkey: BIST 100
South Africa: South Africa Top 40 Index
Spain: IBEX 35
France: CAC 40
Italy: FTSE MIB Index
Netherlands: Netherlands 25
Germany : DAX
Regards.
General Data TerminalHello.
This script is a simple General Data Terminal.
You can also set the period to look back manually in the menu.
In this way, an idea can be obtained about Global Markets.
Note : TIO = Iron Ore
Regards.
Basic Forex TerminalHello,
This script is a simple Forex terminal.
It serves the same purpose as Heatmaps.
You can also set the period to look back manually in the menu.
Major indicators are taken into account.
In this way, an idea can be obtained about all major and minor currencies.
Best regards.
Hull Strategy [Bitduke]Description
The Hull Moving Average (HMA) was developed by Alan Hull for the purpose of reducing lag, increasing responsiveness while at the same time eliminating noise. Its calculation is elaborate and makes use of the Weighted Moving Average (WMA).
It uses two lagged hull moving averages at the intersection of which a change in trend is determined.
Risk Management
Risk is managed by limiting the loss per trade (in%) using stop loss variable.
Improvements
Can be improved by experiments with stop loss and take profit.
Backtesting
Bitmex XBTUSD
Timeframe 3H
Stop 2%, take profit : n/a
193.5% profit
22.42% drawdown
FTX BTC-PERP
Timeframe 3H
Stop 2%, take profit : n/a
187.5% profit
14.79% (!) drawdown
FTX SHIT-PERP
Timeframe 3H
Stop 2%, take profit : n/a
112.5% profit
13.79% (!) drawdown
Strategy Builder Crypto (Single Trend/Plots)Hi everyone
Big program for the daily indicator
This one will be free on trial only for a week because it has an immense value and required quite a lot of work. For more info to use it long-term, please DM me
That out of the way, let's dive right in...
This is a huge upgrade from that script Ultimate-Algorithm-Builder-Single-Trend
The Tradingview non-pro users will appreciate it because it allows to add the selected subsequent indicators as well. The Pro users too will likely like this feature equally, what the H*** I'm saying :)
This indicator will transform you into what I was in the past... into a quant trader. You'll build your own trading algorithm in a few clicks only
Which timeframe and which assets ?
Short answer : ALL and ALL
You'll have to define the configuration of the tool based on your capital, psychology. For custom configuration of the tool, please DM me directly so that we can discuss further
But a few words of advices anyway :
the bigger the timeframe, the lower the inputs (and vice-versa)
Think about how much $$ you want to make per trade and define your entries from there
Think about how much $$ you can afford to lose per trade and define the supertrend from there
...
Your golden configuration might not work for all assets.
You might have to create some tweaks - for instance you found a great config for BTCUSD but it's not working for ETHUSD, then you can create a copy of your BTCUSD chart and set a new config for ETHUSD
What are the indicators inside :
This fantastic tool that I personally use for my trading detects convergence between the following indicators :
Overlay - meaning if the price close above/below a moving average
Trend Signal - to detect if the the DOW law is broken and predict a possible reversal - en.wikipedia.org/wiki/Dow_theory
In other words, it detects if the higher highs or lower lows sequence is broken
MACD or MACD Zero Lag
MA Cross - Cross of moving averages
Ichimoku - if the price closes below/above the cloud
Supertrend - used to detect polarity zones
TSI Shadow -
Pullback
You'll also have the possibility to define a pullback on a given MA. That means basically that you'll get a convergence and it will only display a signal when it will pullback first
This will reduce your losses in case of invalidation and maximize your gains as it will enter the trade in a better position.
You can define your pullback either based an absolute value or based on a percent distance from the MA
+Example:
Pullback value = 100 means I want a 100 pip/USD distance between the MA pullback and the candle
Pullback percent = 2 means I want a 2% distance between the MA pullback and the candle
The percent option is more generic in my opinion but I let the other available for those who might like it
That's it ?
Almost....You can also setup alerts on the indicator signals so that you won't have to stay days in front of the chart to wait for a signal.
You receive the alert, you check real quick if we're not in front of a support/resistance, if no then take the trade. if yes, I advice waiting for a big pullback - better to be safe than sorry in trading
What If you want a custom version ?
Here are a few custom ideas I could add just for you :
re-enter everytime there is a convergence. So far the indicator is only taking the first convergence. This would give more entries
add the resistances/supports (fibo, pivot)
add the take profit targets and trailing stop loss
..
Please hit me up directly so we can discuss further. Any custom dev will require quite some time so it won't be free
Enjoy that one as I really think it will improve your analyst skills and trading and hopefully make you a few gains (which will make me very happy as I want to help most of you to at least not losing your capital)
Dave
HFT Quant AlgoPrivate Fund Algo - HFT/Quant Approach Beta I
2019 Performance
1/Jan/2019 ~ 8/May/2019
Included:
Commission 0.1%
Slippage 100 ticks
Statistical Trend Length Analysis (Quant indicator)This is the only Quantitative type indicator I can find on TradingView (which means it uses automated back testing to determine probability in a mathematical way), although there could be some I just haven't seen them.
This indicator back-tests ALL of the data, calculates the length of all past trends, and does a statistical analysis of trend changes at different levels. The more recent data is more accurate as it learns as the indicator goes along.
These levels can be used in regression to the mean trading, as it gives you an idea of the statistical likeliness of a trend change or pullback occurring in each zone. An average trend length is a very good point to enter when trading a pullback within a trend, although without a complex analysis like this it would be impossible to determine where that is.
PM me for access, and more details on strategies that can be implemented using this indicator.
Zos Ichimoku Trading SystemFully automated Ichimoku Cloud based trading system. Automated entry and exits.
Message me on Telegram for access. @zacharyo
(c) Bronte Capital