Pure Morning 2.0 - Candlestick Pattern Doji StrategyThe new "Pure Morning 2.0 - Candlestick Pattern Doji Strategy" is a trend-following, intraday cryptocurrency trading system authored by devil_machine.
The system identifies Doji and Morning Doji Star candlestick formations above the EMA60 as entry points for long trades.
For best results we recommend to use on 15-minute, 30-minute, or 1-hour timeframes, and are ideal for high-volatility markets.
The strategy also utilizes a profit target or trailing stop for exits, with stop loss set at the lowest low of the last 100 candles. The strategy's configuration details, such as Doji tolerance, and exit configurations are adjustable.
In this new version 2.0, we've incorporated a new selectable filter. Since the stop loss is set at the lowest low, this filter ensures that this value isn't too far from the entry price, thereby optimizing the Risk-Reward ratio.
In the specific case of ALPINE, a 9% Take-Profit and and Stop-Loss at Lowest Low of the last 100 candles were set, with an activated trailing-stop percentage, Max Loss Filter is not active.
Name : Pure Morning 2.0 - Candlestick Pattern Doji Strategy
Author : @devil_machine
Category : Trend Follower based on candlestick patterns.
Operating mode : Spot or Futures (only long).
Trades duration : Intraday
Timeframe : 15m, 30m, 1H
Market : Crypto
Suggested usage : Short-term trading, when the market is in trend and it is showing high volatility .
Entry : When a Doji or Morning Doji Star formation occurs above the EMA60.
Exit : Profit target or Trailing stop, Stop loss on the lowest low of the last 100 candles.
Configuration :
- Doji Settings (tolerances) for Entry Condition
- Max Loss Filter (Lowest Low filter)
- Exit Long configuration
- Trailing stop
Backtesting :
⁃ Exchange: BINANCE
⁃ Pair: ALPINEUSDT
⁃ Timeframe: 30m
⁃ Fee: 0.075%
⁃ Slippage: 1
- Initial Capital: 10000 USDT
- Position sizing: 10% of Equity
- Start: 2022-02-28 (Out Of Sample from 2022-12-23)
- Bar magnifier: on
Disclaimer : Risk Management is crucial, so adjust stop loss to your comfort level. A tight stop loss can help minimise potential losses. Use at your own risk.
How you or we can improve? Source code is open so share your ideas!
Leave a comment and smash the boost button!
Thanks for your attention, happy to support the TradingView community.
Cari dalam skrip untuk "backtesting"
Chandelier Exit ZLSMA StrategyIntroducing a Powerful Trading Indicator: Chandelier Exit with ZLSMA
If you're a trader, you know the importance of having the right tools and indicators to make informed decisions. That's why we're excited to introduce a powerful new trading indicator that combines the Chandelier Exit and ZLSMA: two widely-used and effective indicators for technical analysis.
The Chandelier Exit (CE) is a popular trailing stop-loss indicator developed by Chuck LeBeau. It's designed to follow the price trend of a security and provide an exit signal when the price crosses below the CE line. The CE line is based on the Average True Range (ATR), which is a measure of volatility. This means that the CE line adjusts to the volatility of the security, making it a reliable indicator for trailing stop-losses.
The ZLEMA (Zero Lag Exponential Moving Average) is a type of exponential moving average that's designed to reduce lag and improve signal accuracy. The ZLSMA takes into account not only the current price but also past prices, using a weighted formula to calculate the moving average. This makes it a smoother indicator than traditional moving averages, and less prone to giving false signals.
When combined, the CE and ZLSMA create a powerful indicator that can help traders identify trend changes and make more informed trading decisions. The CE provides the trailing stop-loss signal, while the ZLSMA provides a smoother trend line to help identify potential entry and exit points.
In our indicator, the CE and ZLSMA are plotted together on the chart, making it easy to see both the trailing stop-loss and the trend line at the same time. The CE line is displayed as a dotted line, while the ZLSMA line is displayed as a solid line.
Using this indicator, traders can set their stop-loss levels based on the CE line, while also using the ZLSMA line to identify potential entry and exit points. The combination of these two indicators can help traders reduce their risk and improve their trading performance.
In conclusion, the Chandelier Exit with ZLSMA is a powerful trading indicator that combines two effective technical analysis tools. By using this indicator, traders can identify trend changes, set stop-loss levels, and make more informed trading decisions. Try it out for yourself and see how it can improve your trading performance.
Warning: The results in the backtest are from a repainting strategy. Don't take them seriously. You need to do a dry live test in order to test it for its useability.
-
Here is a description of each input field in the provided source code:
length: An integer input used as the period for the ATR (Average True Range) calculation. Default value is 1.
mult: A float input used as a multiplier for the ATR value. Default value is 2.
showLabels: A boolean input that determines whether to display buy/sell labels on the chart. Default value is false.
isSignalLabelEnabled: A boolean input that determines whether to display signal labels on the chart. Default value is true.
useClose: A boolean input that determines whether to use the close price for extrema calculations. Default value is true.
zcolorchange: A boolean input that determines whether to enable rising/decreasing highlighting for the ZLSMA (Zero-Lag Exponential Moving Average) line. Default value is false.
zlsmaLength: An integer input used as the length for the ZLSMA calculation. Default value is 50.
offset: An integer input used as an offset for the ZLSMA calculation. Default value is 0.
-
Ty for checking this out and good luck on your trading journey! Likes and comments are appreciated. 👍
--
Credits to:
▪ @everget – Chandelier Exit (CE)
▪ @netweaver2022 – ZLSMA
Strategy Creator5 indicators. Backtesting available. Uses ADX, RSI, Stochastic, MACD, and crossing EMAs (1,2, or 3). This strategy creator allows you to turn on or off these indicators and adjust the parameters for each indicator. It allows you to make one trade at a time e.g the next trade doesn't open until the last one closes. (You are also able to enter how many trades in one direction you want for example if you want only 2 long trades in a row, then the strategy waits for the next short position without making anymore long trades. Once there are 2 short positions in a row, it waits for a long position). The code can be edited to for automated trading by editing the comment in the source code for the strategy parameters. This took many hours to finish. ENJOY.
Slight Swing Momentum Strategy.Introduction:
The Swing Momentum Strategy is a quantitative trading strategy designed to capture mid-term opportunities in the financial markets by combining swing trading principles with momentum indicators. It utilizes a combination of technical indicators, including moving averages, crossover signals, and volume analysis, to generate buy and sell signals. The strategy aims to identify market trends and capitalize on price momentum for profit generation.
Highlights:
The strategy offers several key highlights that make it unique and potentially attractive to traders:
Swing Trading with Momentum: The strategy combines the principles of swing trading, which aim to capture short-to-medium-term price swings, with momentum indicators that help identify strong price trends and potential breakout opportunities.
Technical Indicator Optimization: The strategy utilizes a selection of optimized technical indicators, including moving averages and crossover signals, to filter out the noise and focus on high-probability trading setups. This optimization enhances the strategy's ability to identify favourable entry and exit points.
Risk Management: The strategy incorporates risk management techniques, such as position sizing based on equity and dynamic stop loss levels, to manage risk exposure and protect capital. This helps to minimize drawdowns and preserve profits.
Buy Condition:
The buy condition in the strategy is determined by a combination of factors, including A1, A2, A3, XG, and weeklySlope. Let's break it down:
A1 Condition: The A1 condition checks for specific price relationships. It verifies that the ratio of the highest price to the closing price is less than 1.03, the ratio of the opening price to the lowest price is less than 1.03, and the ratio of the highest price to the previous day's closing price is greater than 1.06. This condition looks for a specific pattern indicating potential bullish momentum.
A2 Condition: The A2 condition checks for price relationships related to the closing price. It verifies that the ratio of the closing price to the opening price is greater than 1.05 or that the ratio of the closing price to the previous day's closing price is greater than 1.05. This condition looks for signs of upward price movement and momentum.
A3 Condition: The A3 condition focuses on volume. It checks if the current volume crosses above the highest volume over the last 60 periods. This condition aims to identify increased buying interest and potentially confirms the strength of the potential upward price movement.
XG Condition: The XG condition combines the A1 and A2 conditions and checks if they are true for both the current and previous bars. It also verifies that the ratio of the closing price to the 5-period EMA crosses above the 9-period SMA of the same ratio. This condition helps identify potential buy signals when multiple factors align, indicating a strong bullish momentum and potential entry point.
Weekly Trend Factor: The weekly slope condition calculates the slope of the 50-period SMA over a weekly timeframe. It checks if the slope is positive, indicating an overall upward trend on a weekly basis. This condition provides additional confirmation that the stock is in an upward trend.
When all of these conditions align, the buy condition is triggered, indicating a favourable time to enter a long position.
Sell Condition:
The sell condition is relatively straightforward in the strategy:
Sell Signal: The sell condition simply checks if the closing price crosses below the 10-period EMA. When this condition is met, it indicates a potential reversal or weakening of the upward price momentum, and a sell signal is generated.
Backtest Outcome:
The strategy was backtested over the period from January 22nd, 1999 to May 3rd, 2023, using daily candlestick charts for the NASDAQ: NVDA. The strategy used an initial capital of 1,000,000 USD, The order quantity is defined as 10% of the equity. The strategy allows for pyramiding with 1 order, and the transaction fee is set at 0.03% per trade. Here are the key outcomes of the backtest:
Net Profit: 539,595.84 USD, representing a return of 53.96%.
Percent Profitable: 48.82%
Total Closed Trades: 127
Profit Factor: 2.331
Max Drawdown: 68,422.70 USD
Average Trade: 4,248.79 USD
Average Number of Bars in Trades: 11, indicating the average duration of the trades.
Conclusion:
In conclusion, the Swing Momentum Strategy is a quantitative trading approach that combines swing trading principles with momentum indicators to identify and capture mid term trading opportunities. The strategy has demonstrated promising results during backtesting, including a significant net profit and a favourable profit factor.
[MiV] Trading SessionHello, everyone!
Today I want to present my new script, which I hope will help not only me!
I'm sure that many people, like me, went through such a stage as "building their strategy". This is when you sit and test on the history how you would enter or exit a trade.
Recently I was doing the same thing and realized that my "tests" involve night time, when in reality I would be asleep and not trading! So I decided to create an indicator that would display my "working hours" so that the backtest I conduct would be as realistic as possible.
Also this indicator is able to display sessions of major exchanges and forex working hours, so it will be useful not only for cryptocurrency lovers.
In addition, we don't always trade every day and, for example, I don't trade on Sunday. That's why we added a feature that "turns off" the day and does not highlight it in color if you're not planning to trade on that day.
And finally, I added a notification of the beginning and end of the trading session. A small thing, but it may also be a useful feature for those who like to sit at the chart!
I will be glad to receive any comments and suggestions!
-----
Всем привет!
Хочу сегодня представить свой новый скрипт, который, надеюсь, поможет не только мне!
Уверен, что многие, как и я, проходили такой этап как "постройка своей стратегии". Это когда ты сидишь и тестируешь на истории то как бы ты входил или выходил из сделки.
Вот недавно я ровно также занимался этим и осознал, что мои "тесты" затрагивают и ночное время, когда в реальности я бы спал и не торговал! Поэтому я решил создать индикатор, который будет отображать мои "рабочие часы", чтобы бектест, который я провожу, был максимально реалистичным.
Также данный индикатор умеет отображать сессии крупных бирж и время работы форекса, так что полезным он будет не только для любителей криптовалюты.
Кроме того, мы же не всегда торгуем каждый день и например я не торгую в воскресенье. Поэтому добавлен функционал, который "выключает" день и не подсвечивает его цветом, если ты в этот день не планируешь торговать.
Ну и в заключении, добавил уведомление о начале и завершении торговой сессии. Мелочь, а тоже может быть полезной фичей для тех кто любит засесть за графиком!
Буду рад любым замечаниям и предложениям!
Recessions & crises shading (custom dates & stats)Shades your chart background to flag events such as crises or recessions, in similar fashion to what you see on FRED charts. The advantage of this indicator over others is that you can quickly input custom event dates as text in the menu to analyse their impact for your specific symbol. The script automatically labels, calculates and displays the peak to through percentage corrections on your current chart.
By default the indicator is configured to show the last 6 US recessions. If you have custom events which will benefit others, just paste the input string in the comments below so one can simply copy/paste in their indicator.
Example event input (No spaces allowed except for the label name. Enter dates as YYYY-MM-DD.)
2020-02-01,2020-03-31,COVID-19
2007-12-01,2009-05-31,Subprime mortgages
2001-03-01,2001-10-30,Dot-com bubble
1990-07-01,1991-03-01,Oil shock
1981-07-01,1982-11-01,US unemployment
1980-01-01,1980-07-01,Volker
1973-11-01,1975-03-01,OPEC
Quinn-Fernandes Fourier Transform of Filtered Price [Loxx]Down the Rabbit Hole We Go: A Deep Dive into the Mysteries of Quinn-Fernandes Fast Fourier Transform and Hodrick-Prescott Filtering
In the ever-evolving landscape of financial markets, the ability to accurately identify and exploit underlying market patterns is of paramount importance. As market participants continuously search for innovative tools to gain an edge in their trading and investment strategies, advanced mathematical techniques, such as the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter, have emerged as powerful analytical tools. This comprehensive analysis aims to delve into the rich history and theoretical foundations of these techniques, exploring their applications in financial time series analysis, particularly in the context of a sophisticated trading indicator. Furthermore, we will critically assess the limitations and challenges associated with these transformative tools, while offering practical insights and recommendations for overcoming these hurdles to maximize their potential in the financial domain.
Our investigation will begin with a comprehensive examination of the origins and development of both the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter. We will trace their roots from classical Fourier analysis and time series smoothing to their modern-day adaptive iterations. We will elucidate the key concepts and mathematical underpinnings of these techniques and demonstrate how they are synergistically used in the context of the trading indicator under study.
As we progress, we will carefully consider the potential drawbacks and challenges associated with using the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter as integral components of a trading indicator. By providing a critical evaluation of their computational complexity, sensitivity to input parameters, assumptions about data stationarity, performance in noisy environments, and their nature as lagging indicators, we aim to offer a balanced and comprehensive understanding of these powerful analytical tools.
In conclusion, this in-depth analysis of the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter aims to provide a solid foundation for financial market participants seeking to harness the potential of these advanced techniques in their trading and investment strategies. By shedding light on their history, applications, and limitations, we hope to equip traders and investors with the knowledge and insights necessary to make informed decisions and, ultimately, achieve greater success in the highly competitive world of finance.
█ Fourier Transform and Hodrick-Prescott Filter in Financial Time Series Analysis
Financial time series analysis plays a crucial role in making informed decisions about investments and trading strategies. Among the various methods used in this domain, the Fourier Transform and the Hodrick-Prescott (HP) Filter have emerged as powerful techniques for processing and analyzing financial data. This section aims to provide a comprehensive understanding of these two methodologies, their significance in financial time series analysis, and their combined application to enhance trading strategies.
█ The Quinn-Fernandes Fourier Transform: History, Applications, and Use in Financial Time Series Analysis
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique developed by John J. Quinn and Mauricio A. Fernandes in the early 1990s. It builds upon the classical Fourier Transform by introducing an adaptive approach that improves the identification of dominant frequencies in noisy signals. This section will explore the history of the Quinn-Fernandes Fourier Transform, its applications in various domains, and its specific use in financial time series analysis.
History of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform was introduced in a 1993 paper titled "The Application of Adaptive Estimation to the Interpolation of Missing Values in Noisy Signals." In this paper, Quinn and Fernandes developed an adaptive spectral estimation algorithm to address the limitations of the classical Fourier Transform when analyzing noisy signals.
The classical Fourier Transform is a powerful mathematical tool that decomposes a function or a time series into a sum of sinusoids, making it easier to identify underlying patterns and trends. However, its performance can be negatively impacted by noise and missing data points, leading to inaccurate frequency identification.
Quinn and Fernandes sought to address these issues by developing an adaptive algorithm that could more accurately identify the dominant frequencies in a noisy signal, even when data points were missing. This adaptive algorithm, now known as the Quinn-Fernandes Fourier Transform, employs an iterative approach to refine the frequency estimates, ultimately resulting in improved spectral estimation.
Applications of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform has found applications in various fields, including signal processing, telecommunications, geophysics, and biomedical engineering. Its ability to accurately identify dominant frequencies in noisy signals makes it a valuable tool for analyzing and interpreting data in these domains.
For example, in telecommunications, the Quinn-Fernandes Fourier Transform can be used to analyze the performance of communication systems and identify interference patterns. In geophysics, it can help detect and analyze seismic signals and vibrations, leading to improved understanding of geological processes. In biomedical engineering, the technique can be employed to analyze physiological signals, such as electrocardiograms, leading to more accurate diagnoses and better patient care.
Use of the Quinn-Fernandes Fourier Transform in Financial Time Series Analysis
In financial time series analysis, the Quinn-Fernandes Fourier Transform can be a powerful tool for isolating the dominant cycles and frequencies in asset price data. By more accurately identifying these critical cycles, traders can better understand the underlying dynamics of financial markets and develop more effective trading strategies.
The Quinn-Fernandes Fourier Transform is used in conjunction with the Hodrick-Prescott Filter, a technique that separates the underlying trend from the cyclical component in a time series. By first applying the Hodrick-Prescott Filter to the financial data, short-term fluctuations and noise are removed, resulting in a smoothed representation of the underlying trend. This smoothed data is then subjected to the Quinn-Fernandes Fourier Transform, allowing for more accurate identification of the dominant cycles and frequencies in the asset price data.
By employing the Quinn-Fernandes Fourier Transform in this manner, traders can gain a deeper understanding of the underlying dynamics of financial time series and develop more effective trading strategies. The enhanced knowledge of market cycles and frequencies can lead to improved risk management and ultimately, better investment performance.
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique that has proven valuable in various domains, including financial time series analysis. Its adaptive approach to frequency identification addresses the limitations of the classical Fourier Transform when analyzing noisy signals, leading to more accurate and reliable analysis. By employing the Quinn-Fernandes Fourier Transform in financial time series analysis, traders can gain a deeper understanding of the underlying financial instrument.
Drawbacks to the Quinn-Fernandes algorithm
While the Quinn-Fernandes Fourier Transform is an effective tool for identifying dominant cycles and frequencies in financial time series, it is not without its drawbacks. Some of the limitations and challenges associated with this indicator include:
1. Computational complexity: The adaptive nature of the Quinn-Fernandes Fourier Transform requires iterative calculations, which can lead to increased computational complexity. This can be particularly challenging when analyzing large datasets or when the indicator is used in real-time trading environments.
2. Sensitivity to input parameters: The performance of the Quinn-Fernandes Fourier Transform is dependent on the choice of input parameters, such as the number of harmonic periods, frequency tolerance, and Hodrick-Prescott filter settings. Choosing inappropriate parameter values can lead to inaccurate frequency identification or reduced performance. Finding the optimal parameter settings can be challenging, and may require trial and error or a more sophisticated optimization process.
3. Assumption of stationary data: The Quinn-Fernandes Fourier Transform assumes that the underlying data is stationary, meaning that its statistical properties do not change over time. However, financial time series data is often non-stationary, with changing trends and volatility. This can limit the effectiveness of the indicator and may require additional preprocessing steps, such as detrending or differencing, to ensure the data meets the assumptions of the algorithm.
4. Limitations in noisy environments: Although the Quinn-Fernandes Fourier Transform is designed to handle noisy signals, its performance may still be negatively impacted by significant noise levels. In such cases, the identification of dominant frequencies may become less reliable, leading to suboptimal trading signals or strategies.
5. Lagging indicator: As with many technical analysis tools, the Quinn-Fernandes Fourier Transform is a lagging indicator, meaning that it is based on past data. While it can provide valuable insights into historical market dynamics, its ability to predict future price movements may be limited. This can result in false signals or late entries and exits, potentially reducing the effectiveness of trading strategies based on this indicator.
Despite these drawbacks, the Quinn-Fernandes Fourier Transform remains a valuable tool for financial time series analysis when used appropriately. By being aware of its limitations and adjusting input parameters or preprocessing steps as needed, traders can still benefit from its ability to identify dominant cycles and frequencies in financial data, and use this information to inform their trading strategies.
█ Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
Another significant advantage of the HP Filter is its ability to adapt to changes in the underlying trend. This feature makes it particularly well-suited for analyzing financial time series, which often exhibit non-stationary behavior. By employing the HP Filter to smooth financial data, traders can more accurately identify and analyze the long-term trends that drive asset prices, ultimately leading to better-informed investment decisions.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
█ Combined Application of Fourier Transform and Hodrick-Prescott Filter
The integration of the Fourier Transform and the Hodrick-Prescott Filter in financial time series analysis can offer several benefits. By first applying the HP Filter to the financial data, traders can remove short-term fluctuations and noise, effectively isolating the underlying trend. This smoothed data can then be subjected to the Fourier Transform, allowing for the identification of dominant cycles and frequencies with greater precision.
By combining these two powerful techniques, traders can gain a more comprehensive understanding of the underlying dynamics of financial time series. This enhanced knowledge can lead to the development of more effective trading strategies, better risk management, and ultimately, improved investment performance.
The Fourier Transform and the Hodrick-Prescott Filter are powerful tools for financial time series analysis. Each technique offers unique benefits, with the Fourier Transform being adept at identifying dominant cycles and frequencies, and the HP Filter excelling at isolating long-term trends from short-term noise. By combining these methodologies, traders can develop a deeper understanding of the underlying dynamics of financial time series, leading to more informed investment decisions and improved trading strategies. As the financial markets continue to evolve, the combined application of these techniques will undoubtedly remain an essential aspect of modern financial analysis.
█ Features
Endpointed and Non-repainting
This is an endpointed and non-repainting indicator. These are crucial factors that contribute to its usefulness and reliability in trading and investment strategies. Let us break down these concepts and discuss why they matter in the context of a financial indicator.
1. Endpoint nature: An endpoint indicator uses the most recent data points to calculate its values, ensuring that the output is timely and reflective of the current market conditions. This is in contrast to non-endpoint indicators, which may use earlier data points in their calculations, potentially leading to less timely or less relevant results. By utilizing the most recent data available, the endpoint nature of this indicator ensures that it remains up-to-date and relevant, providing traders and investors with valuable and actionable insights into the market dynamics.
2. Non-repainting characteristic: A non-repainting indicator is one that does not change its values or signals after they have been generated. This means that once a signal or a value has been plotted on the chart, it will remain there, and future data will not affect it. This is crucial for traders and investors, as it offers a sense of consistency and certainty when making decisions based on the indicator's output.
Repainting indicators, on the other hand, can change their values or signals as new data comes in, effectively "repainting" the past. This can be problematic for several reasons:
a. Misleading results: Repainting indicators can create the illusion of a highly accurate or successful trading system when backtesting, as the indicator may adapt its past signals to fit the historical price data. This can lead to overly optimistic performance results that may not hold up in real-time trading.
b. Decision-making uncertainty: When an indicator repaints, it becomes challenging for traders and investors to trust its signals, as the signal that prompted a trade may change or disappear after the fact. This can create confusion and indecision, making it difficult to execute a consistent trading strategy.
The endpoint and non-repainting characteristics of this indicator contribute to its overall reliability and effectiveness as a tool for trading and investment decision-making. By providing timely and consistent information, this indicator helps traders and investors make well-informed decisions that are less likely to be influenced by misleading or shifting data.
Inputs
Source: This input determines the source of the price data to be used for the calculations. Users can select from options like closing price, opening price, high, low, etc., based on their preferences. Changing the source of the price data (e.g., from closing price to opening price) will alter the base data used for calculations, which may lead to different patterns and cycles being identified.
Calculation Bars: This input represents the number of past bars used for the calculation. A higher value will use more historical data for the analysis, while a lower value will focus on more recent price data. Increasing the number of past bars used for calculation will incorporate more historical data into the analysis. This may lead to a more comprehensive understanding of long-term trends but could also result in a slower response to recent price changes. Decreasing this value will focus more on recent data, potentially making the indicator more responsive to short-term fluctuations.
Harmonic Period: This input represents the harmonic period, which is the number of harmonics used in the Fourier Transform. A higher value will result in more harmonics being used, potentially capturing more complex cycles in the price data. Increasing the harmonic period will include more harmonics in the Fourier Transform, potentially capturing more complex cycles in the price data. However, this may also introduce more noise and make it harder to identify clear patterns. Decreasing this value will focus on simpler cycles and may make the analysis clearer, but it might miss out on more complex patterns.
Frequency Tolerance: This input represents the frequency tolerance, which determines how close the frequencies of the harmonics must be to be considered part of the same cycle. A higher value will allow for more variation between harmonics, while a lower value will require the frequencies to be more similar. Increasing the frequency tolerance will allow for more variation between harmonics, potentially capturing a broader range of cycles. However, this may also introduce noise and make it more difficult to identify clear patterns. Decreasing this value will require the frequencies to be more similar, potentially making the analysis clearer, but it might miss out on some cycles.
Number of Bars to Render: This input determines the number of bars to render on the chart. A higher value will result in more historical data being displayed, but it may also slow down the computation due to the increased amount of data being processed. Increasing the number of bars to render on the chart will display more historical data, providing a broader context for the analysis. However, this may also slow down the computation due to the increased amount of data being processed. Decreasing this value will speed up the computation, but it will provide less historical context for the analysis.
Smoothing Mode: This input allows the user to choose between two smoothing modes for the source price data: no smoothing or Hodrick-Prescott (HP) smoothing. The choice depends on the user's preference for how the price data should be processed before the Fourier Transform is applied. Choosing between no smoothing and Hodrick-Prescott (HP) smoothing will affect the preprocessing of the price data. Using HP smoothing will remove some of the short-term fluctuations from the data, potentially making the analysis clearer and more focused on longer-term trends. Not using smoothing will retain the original price fluctuations, which may provide more detail but also introduce noise into the analysis.
Hodrick-Prescott Filter Period: This input represents the Hodrick-Prescott filter period, which is used if the user chooses to apply HP smoothing to the price data. A higher value will result in a smoother curve, while a lower value will retain more of the original price fluctuations. Increasing the Hodrick-Prescott filter period will result in a smoother curve for the price data, emphasizing longer-term trends and minimizing short-term fluctuations. Decreasing this value will retain more of the original price fluctuations, potentially providing more detail but also introducing noise into the analysis.
Alets and signals
This indicator featues alerts, signals and bar coloring. You have to option to turn these on/off in the settings menu.
Maximum Bars Restriction
This indicator requires a large amount of processing power to render on the chart. To reduce overhead, the setting "Number of Bars to Render" is set to 500 bars. You can adjust this to you liking.
█ Related Indicators and Libraries
Goertzel Cycle Composite Wave
Goertzel Browser
Fourier Spectrometer of Price w/ Extrapolation Forecast
Fourier Extrapolator of 'Caterpillar' SSA of Price
Normalized, Variety, Fast Fourier Transform Explorer
Real-Fast Fourier Transform of Price Oscillator
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Fourier Extrapolator of Price
STD-Stepped Fast Cosine Transform Moving Average
Variety RSI of Fast Discrete Cosine Transform
loxfft
Price Action Trading StrategyIn this strategy, we define the high and low of the previous candle, and then check whether the current candle's high or low is higher or lower than the previous candle's high or low, respectively. If there's a new high, we enter a long position, and if there's a new low, we enter a short position. We also set exit conditions to close the position if the price drops below the previous low or rises above the previous high.
Please note that this is a simple example and should not be used as a standalone trading strategy. It is important to conduct thorough backtesting and consider other factors such as risk management before implementing any trading strategy.
SAT_BACKTEST @description TODO: Regroupement of useful functionsLibrary "SAT_BACKTEST"
ex_timezone(tz)
switch case return exact @timezone for timezone input
Parameters:
tz (simple string)
Returns: syminfo.timezone or tz
if_in_date_range(usefromDate, fromDate, usetoDate, toDate, src_timezone, dst_timezone)
if_in_date_range : check if @time_close is range
Parameters:
usefromDate (simple bool)
fromDate (simple int)
usetoDate (simple bool)
toDate (simple int)
src_timezone (simple string)
dst_timezone (simple string)
Returns: true if @time_close is range
if_in_session(useSessionStart, sessionStartHour, sessionStartMinute, useSessionEnd, sessionEndHour, sessionEndMinute, useSessionDay, mon, tue, wed, thu, fri, sat, sun, src_timezone, dst_timezone)
if_in_session : check if @time_close is range
Parameters:
useSessionStart (simple bool)
sessionStartHour (simple int)
sessionStartMinute (simple int)
useSessionEnd (simple bool)
sessionEndHour (simple int)
sessionEndMinute (simple int)
useSessionDay (simple bool)
mon (simple bool)
tue (simple bool)
wed (simple bool)
thu (simple bool)
fri (simple bool)
sat (simple bool)
sun (simple bool)
src_timezone (simple string)
dst_timezone (simple string)
Returns: true if @time_close is range
Yesterday’s High Breakout - Trend Following StrategyYesterday’s High Breakout it is a trading system based on the analysis of yesterday's highs, it works in trend-following mode therefore it opens a long position at the breakout of yesterday's highs even if they occur several times in one day.
There are several methods for exiting a trade, each with its own unique strategy. The first method involves setting Take-Profit and Stop-Loss percentages, while the second utilizes a trailing-stop with a specified offset value. The third method calls for a conditional exit when the candle closes below a reference EMA.
Additionally, operational filters can be applied based on the volatility of the currency pair, such as calculating the percentage change from the opening or incorporating a gap to the previous day's high levels. These filters help to anticipate or delay entry into the market, mitigating the risk of false breakouts.
In the specific case of NULS, a 9% Take-Profit and a 3% Stop-Loss were set, with an activated trailing-stop percentage. To postpone entry and avoid false breakouts, a 1% gap was added to the price of yesterday's highs.
Name : Yesterday's High Breakout - Trend Follower Strategy
Author : @tumiza999
Category : Trend Follower, Breakout of Yesterday's High.
Operating mode : Spot or Futures (only long).
Trade duration : Intraday.
Timeframe : 30M, 1H, 2H, 4H
Market : Crypto
Suggested usage : Short-term trading, when the market is in trend and it is showing high volatility.
Entry : When there is a breakout of Yesterday's High.
Exit : Profit target or Trailing stop, Stop loss or Crossunder EMA.
Configuration :
- Gap to anticipate or postpone the entry before or after the identified level
- Rate of Change for Entry Condition
- Take Profit, Stop Loss and Trailing Stop
- EMA length
Backtesting :
⁃ Exchange: BINANCE
⁃ Pair: NULSUSDT
⁃ Timeframe: 2H
⁃ Fee: 0.075%
⁃ Slippage: 1
- Initial Capital: 10000 USDT
- Position sizing: 10% of Equity
- Start : 2018-07-26 (Out Of Sample from 2022-12-23)
- Bar magnifier: on
Credits : LucF for Pine Coders (f_security function to avoid repainting using security)
Disclaimer : Risk Management is crucial, so adjust stop loss to your comfort level. A tight stop loss can help minimise potential losses. Use at your own risk.
How you or we can improve? Source code is open so share your ideas!
Leave a comment and smash the boost button!
Thanks for your attention, happy to support the TradingView community.
Dual timeframe calculated candlesA script example to show how you can calculate the value of certain indicators from a higher timeframe at the moment that a bar closes on a shorter timeframe.
In this example the base chart is set to 5 mins and the multiplier is set to 6, so the HTF arrays hold data equivalent to that from the 30 minute chart which will hopefully appear below it on this display.
Each time a 5 minute bar completes, the arrays are updated by checking whether a new high or low has been set. The values for the HTF ATR and EMA are also updated by removing the most recent value from that array and replacing it with the value that would have been calculated based on the close at that time. As such for back testing purposes you'd know exactly what the 30 min chart would have been showing you at any one of the 5 minute intervals. Useful for backtesting strategies if you would rather act on the "up to the minute" HTF data, rather than the HTF data from the last HTF close, which could be significantly delayed if you're using a high enough multiplier.
VIX Futures Spread StrategyThis script was an exercise in learning Pinescript and exploring the futures curve of the VIX in relation to SPY. Was deleted by TV, trying to republish it now with updated parameters for slippage and commission and a more detailed description.
"VIX Futures Spread Strategy" is a trading strategy that capitalizes on the spread between the 3-month VIX futures (VIX3M) and the spot VIX index. This strategy is based on the idea that the VIX futures spread can serve as a contrarian indicator of market sentiment, with extreme negative spreads potentially signaling oversold conditions and opportunities for long positions.
Ordinarily the VIX curve is in contango as futures contracts are priced at a premium to the current spot price and are used to hedge future uncertainty in the market. When the spot price of VIX spikes the curve can invert and enter backwardation; this strategy detects this condition and uses it as a trigger to open a long position in SPY. The spread going negative tends to correlate with excessive fear and uncertainty in the short term while expecting lower volatility in the long term, in this case 3 months out.
The strategy is designed to enter a long position when the VIX futures spread is negative and to exit the position when the spread rises above 3 -- when the curve is in contango again. The strategy employs a pyramiding approach, allowing up to 10 additional orders to be placed while the entry condition is met, with each order consisting of 10 contracts. This approach aims to maximize potential profits during periods of favorable market conditions.
In this strategy, the VIX futures spread is calculated as the difference between the 3-month VIX futures (VIX3M) and the spot VIX index. The spread is plotted as a histogram on the chart, with the zero line representing no spread, and horizontal lines at 0 and 3 indicating the entry and exit thresholds, respectively.
The strategy's backtesting settings use an initial capital of HKEX:10 ,000, a commission of 0.5% per trade, and a maximum of 10 pyramiding orders, and a slippage of 2 ticks.
Please note that this strategy is intended for educational purposes and should not be considered as financial advice. Before using this strategy in live trading, make sure to thoroughly test and optimize its parameters to suit your risk tolerance and specific trading conditions.
TradeEasy - KintroThe TradingView script provided is a custom indicator named "TradeEasy - Kintro". It is created by the author Kintro and is designed to help traders identify potential buy and sell signals in the market. The indicator is based on the Exponential Moving Average (EMA) and uses two different EMAs, one with a period of 20 and the other with a period of 50.
The indicator is meant to be used on the 5-minute timeframe and it is recommended to use TradingView in Dark Mode for better appearance. The author also reminds users that no strategy works 100% accurately and backtesting should be done before trading with a real account. The author is not responsible for any losses incurred by traders.
The indicator uses a simple set of rules to generate trading signals. The thick line on the chart represents the 50 EMA while the thin line represents the 20 EMA. When the thin line crosses upwards over the thick line, it indicates a bullish signal. After the crossover, traders are advised to wait for the price to pullback between the two lines. A range should then be created while the price moves through the thin line.
On the break of the range, an entry signal is generated, and the stop loss should be set below the range. The author advises traders to exit their profits according to their own analysis or price action and not to re-enter on the next pullback of the same trend. The same rules apply when the thin line crosses downwards over the thick line.
The author emphasizes that range creation is mandatory on crossing and that traders should not try to go against the trend. If the price is above both lines, traders should only go for buy orders, and vice versa.
If there is no range created while crossing, traders are advised not to enter the market. Traders should wait for the opportunity and not force a trade.
The indicator also includes a plot of the 34 EMA, and a range is created above and below the price action using the "up" and "down" variables. The author uses the "fill" function to color the background of the chart to highlight the range. The "dummy" variable is used to plot circles above or below the price action, depending on the trend.
In summary, the "TradeEasy - Kintro" indicator is a custom indicator designed to help traders identify potential buy and sell signals based on the crossing of two EMAs. The author provides a set of rules to generate trading signals and advises traders to wait for the opportunity and not force a trade. The indicator also includes a visual representation of the range created on the chart. As always, traders are advised to conduct their own research and analysis before entering any trades.
Statistics: High & Low timings of custom session; 1yr historyGet statistics of the Session High and Session Low timings for any custom session; based on around 1yr of data.
//Purpose:
-To get data on the 'time of day' tendencies of an asset.
-Narrow in on a custom defined session and get statistics on that session.
//Notes:
-Input times are always in New York time (but changing the timezone after setting WILL adust both table stats and background highlight correctly.
-For particularly long sessions, make sure text size is set to 'tiny' (very long vertical table), or adjust table to display horizontally.
-You'll notice most assets show higher readings around NY equities open (9:30am NY time). Other assets will have 'hot-spots' at other times too.
-Timings represent the beginning of a 15m candle. i.e. reading for 15:45 represents a high occurring between 15:45 and 1600.
-Premium users should get 20k bars => around 1year's worth of data on a 15minute chart. Days of history is displayed in the top left corner of the table.
//Limitations
-only designed and working on 15minute timeframe (to gather a full year of meaningful/comparable % stats, need 15minute 'buckets' of time.
-sessions cannot cross through midnight, or start at midnight (00:15 is ok). 00:15 >> 23:45 is the max session length. On BTC, same applies but 01:00 instead of midnight (all in NY time).
-if your session crosses through 'dead time' (e.g. 17:00-18:00 S&P NY time); table will correctly omit these non-existent candles, but it will add on the missing hour before the start time.
//Cautionary note:
-Since markets are not uncommonly in a trending state when your defined session starts or ends, the high/low timings % readings for start and end of session may be misleadingly high. Try to look for unusually high readings that are not at the start/end of your session.
Wheat (ZW1!) 15min chart; Table displayed vertically:
Nasdaq (NQ1!) 15m chart; Table displayed horizontally and with smaller text to view a very long custom session:
Jdawg Sentiment Momentum Oscillator EnhancedThe Jdawg Sentiment Momentum Oscillator Enhanced (JSMO_E) is a versatile technical analysis indicator designed to provide traders with insights into potential trend changes and overbought or oversold market conditions. JSMO_E combines the principles of the Relative Strength Index (RSI), the Simple Moving Average (SMA), and the Rate of Change (ROC) to create a comprehensive tool for assessing market sentiment and momentum.
The uniqueness of JSMO_E lies in its ability to integrate the RSI, SMA of RSI, and ROC of RSI, while also allowing users to customize the weight of the ROC component. This combination of features is not commonly found in other indicators, which increases its distinctiveness.
To effectively use JSMO_E, follow these steps:
Apply the JSMO_E indicator to the price chart of the asset you are analyzing.
Observe the plotted JSMO_E line in relation to the zero line, overbought, and oversold levels.
When the JSMO_E line crosses above the zero line, it may signal the beginning of an uptrend or bullish momentum. Conversely, when the JSMO_E line crosses below the zero line, it may indicate the start of a downtrend or bearish momentum.
Overbought and oversold levels, marked by the red and green dashed lines, respectively, can serve as a warning that a trend reversal may be imminent. When the JSMO_E line reaches or surpasses the overbought level, it might indicate that the asset is overvalued and could experience a price decline. Conversely, when the JSMO_E line reaches or goes below the oversold level, it can signal that the asset is undervalued and may experience a price increase.
Adjust the input parameters (RSI Period, SMA Period, ROC Period, and ROC Weight) as needed to optimize the indicator for the specific market and time frame you are analyzing.
The JSMO_E indicator is suitable for various markets, including stocks, forex, commodities, and cryptocurrencies. However, its effectiveness may vary depending on the market conditions and time frames used. It is recommended to use JSMO_E in conjunction with other technical analysis tools and methods to confirm potential trade setups and improve overall trading performance. Always conduct thorough backtesting and forward-testing before employing any indicator in a live trading environment.
Opening Hour/Closing Hour Indices Statistics: high/low times; 5mVery specific indicator designed for 5min timeframe, to show the statistical timings of the highs and lows of Opening hour (9:30-10am) and Closing hour (3pm-4pm) NY time
~~Shown here on SPX 5min chart. Works all variants of the US indices. SPX and SPY typically show more days of history (non-extended session =>> more bars).
//Purpose:
-To get statistics on the timings of the high and low of the opening hour and the high & low of the closing hour.
//Design & Limitations:
- Designed for the 5minute chart ONLY . Need a sweet spot of 'bucket' size for the statistics: to allow meaningful comparison between times.
-Will also display on 1min chart but NOT the statistics panel, only the realtime data (today's opening hour/ closing hour timings).
-Can be slow to load depending on server load at the time. This is becasue of the multiple usage of looping array functions. Please be patient when loading or changing settings.
//User inputs:
-Standard formatting options: highlight color, table text color. Toggle on/off independently
-Decimal % percision (default = 0, i.e. 23%. If set to 1 => 22.8%)
-Show statistics: Show Opening hour statistics, Show Closing hour statistics
//Notes:
-Days of history shown at top of table; this is the size of the dataset. i.e. 254 here (254 trading days) =>> 254 opening hour highs, 254 closing hour lows etc.
--to illustrate with the above: 18% of those 254 closing hour highs occured on the 15:00 5min candle (i.e. between 15:00 and 15:05).
-SPY or SPX offer the largest history/dataset (circa 254 trading days).
-Note that the final timing in each hour is 10:25am and 15:55pm respectively: this is because the 10:25am 5min candle essentially ends at 10:30am =>> we properly captures the opening hour this way
-Pro+ users will get less data history than Premium users (half as much, due to 10k vs 20k bars history limit).
Three-Day Rolling PivotThe three-day rolling pivot is another pivot concept,
which may be used by intermediate positions, for several days or even weeks.
It can be utilized in many ways, such as to determine an entry point or trailing stop.
As the name suggests, this pivot is based on the last three days.
I learned this concept of the book "The logical Trader" by Mark Fisher.
Kudos go to him!
My version of the Three-Day Rolling Pivot uses actual data!
And all similar scripts I have found so far calculate future data and don't take into account the original data.
I hope this script will help some people to do some better decisions.
And I am pleased to get some advice to make this script even better!
Future data vs original data
Pine Script v5 Reference Manual:
Merge strategy for the requested data position... This merge strategy can lead to undesirable effect of getting data from "future" on calculation on history. This is unacceptable in backtesting strategies, but can be useful in indicators.
e2e4 on Stack Overflow said:
Pine v1-v2's security() function is using the lookahead parameter by default, which could be modified in v3-v5...
stackoverflow.com
I haven't found a script which put this into account jet.
I leave this option available for people that wanna more speculated data. But it's disabled by default.
Long/Short Example
You can enter Long when the market cross over the upper line (default color is green) and you should put your trailing stop 1-5 ticks below the lower line (default color is red).
The opposite when Shorting, then the market has to cross down the lower line and your trailing stop should be 1-5 ticks above the upper line.
How does this script work:
First it fetches the highest high of ...
yesterday,
the day before yesterday,
and the day before that.
After that the script looks for the highest high of all three.
Next it does the same for previous lowest low.
Last but not least, it fetches the closing price of the last day.
After that it adds all three prices together and divide them by three.
This result in a three day pivot price.
Then it adds the highest high and lowest low of the three last days and divide it by two.
This gives us the second number we need to calculate the differential.
The differential is the gap between the three day pivot price and the second number.
Sometimes the second number is bigger than the three day pivot price so I took that into account too. Other wise the colors plotted would be on the wrong site.
Finally, the script is rounding the numbers to the nearest minimum tick of that security.
FVGs & CEs + Alerts: simple & efficient methodFair Value Gap indicator: Paints FVGs and their midlines (CEs). Stops painting when CE is hit, or when fully filled; user choice of threshold. This threshold is also used in the Alert conditions.
~~Plotted here on ES1! (CME), on the 15m timeframe~~
-A FVG represents a 'naked' body where the wicks/tails on either side do not meet. This can be seen as a type of 'gap', which price will have a tendency to want to re-fill (in part or in full).
-The midline (CE, or 'Consequent encroachment') of FVGs also tend to show price sensitivity.
-This indicator paints all FVGs until priced into, and should give an idea of which are more meaningful and which are best ignored (based on context: location, Time of day, market structure, etc).
-This is a simpler and more efficient method of painting Fair value gaps which auto-stop painting when price reaches them.
//Aims of Publishing:
-Education of ICT concepts of Fair Value Gaps and their midlines (CEs): To easily see via forward testing or backtesting, the sensitivity that price shows to these areas & levels.
-Demonstration of a much more efficient way of plotting FVGs which terminate at price, thanks to a modification of @Bjorgums's clever looping method referenced below.
//Settings:
-Toggle on/off upward and downward FVGs independently(blue and orange by default).
-Toggle on/off midline (CE).
-Standard color/line formatting options.
-Choose Threshold: CE of FVG or Full Fill of FVG: This will determine both the 'stop-painting' trigger and the 'Alert' trigger.
-Choose number of days lookback to control how many historical FVGs paint on chart.
//On alerts:
-Simple choice of 2 alerts:
~~One for price crossing into/above the nearest untouched 'premium' FVG above ( orange ). Trigger is user choice of CE or full fill.
~~Another for price crossing into/below the nearest untouched 'discount' FVG below (blue). Trigger is user choice of CE or full fill.
-Alerts set via the three dots in indicator status line.
//Cautionary notes:
-Do not use the alerts blindly to find trades. Wait until you have identified a good FVG above/below which you think price may show sensitivity to
-Usage on very low timeframes can cause unexpected results with alerts: due to new FVGs forming in realtime the Alert will always trigger at the most recent FVG above/ below having its threshold hit.
-Big thank you to @Bjorgum for his fantastic extendAndRemove method. Modified here for use with boxes and to integrate Alerts.
-Also Credit to ICT (inner circle trader) for the concepts used here: Fair value gaps and their Consequent Encroachment (CE).
The Flash-Strategy (Momentum-RSI, EMA-crossover, ATR)The Flash-Strategy (Momentum-RSI, EMA-crossover, ATR)
Are you tired of manually analyzing charts and trying to find profitable trading opportunities? Look no further! Our algorithmic trading strategy, "Flash," is here to simplify your trading process and maximize your profits.
Flash is an advanced trading algorithm that combines three powerful indicators to generate highly selective and accurate trading signals. The Momentum-RSI, Super-Trend Analysis and EMA-Strategy indicators are used to identify the strength and direction of the underlying trend.
The Momentum-RSI signals the strength of the trend and only generates trading signals in confirmed upward or downward trends. The Super-Trend Analysis confirms the trend direction and generates signals when the price breaks through the super-trend line. The EMA-Strategy is used as a qualifier for the generation of trading signals, where buy signals are generated when the EMA crosses relevant trend lines.
Flash is highly selective, as it only generates trading signals when all three indicators align. This ensures that only the highest probability trades are taken, resulting in maximum profits.
Our trading strategy also comes with two profit management options. Option 1 uses the so-called supertrend-indicator which uses the dynamic ATR as a key input, while option 2 applies pre-defined, fixed SL and TP levels.
The settings for each indicator can be customized, allowing you to adjust the length, limit value, factor, and source value to suit your preferences. You can also set the time period in which you want to run the backtest and how many dollar trades you want to open in each position for fully automated trading.
Choose your preferred trade direction and stop-loss/take-profit settings, and let Flash do the rest. Say goodbye to manual chart analysis and hello to consistent profits with Flash. Try it now!
General Comments
This Flash Strategy has been developed in cooperation between Baby_whale_to_moon and JS-TechTrading. Cudos to Baby_whale_to_moon for doing a great job in transforming sophisticated trading ideas into pine scripts.
Detailed Description
The “Flash” script considers the following indicators for the generation of trading signals:
1. Momentum-RSI
2. ‘Super-Trend’-Analysis
3. EMA-Strategy
1. Momentum-RSI
• This indicator signals the strength of the underlying upward- or downward-trend.
• The signal range of this indicator is from 0 to 100. Values > 60 indicate a confirmed upward- or downward-trend.
• The strategy will only generate trading signals in case the stock (or any other financial security) is in a confirmed upward- (long entry signals) or downward-trend (short entry signals).
• This indicator provides information with regards to the strength of the underlying trend and it does not give any insight with regard to the direction of the trend. Therefore, this strategy also considers other indicators which provide technical confirmation with regards to the direction of the underlying trend.
Graph 1 shows this concept:
• The Momentum-RSI indicator gives lower readings during consolidation phases and no trading signals are generated during these periods.
Example (graph 2):
2. Super-Trend Analysis
• The red line in the graph below represents the so-called super-trend-line. Trading signals are only generated in case the price action breaks through this super-trend-line indicating a new confirmed upward-trend (or downward-trend, respectively).
• If that happens, the super trend-line changes its color from red to green, giving confirmation that the trend changed from bearish to bullish and long-entries can be considered.
• The vice-versa approach can be considered for short entries.
Graph 3 explains this concept:
3. Exponential Moving Average / EMA-Strategy
The functionality of this EMA-element of the strategy has been programmed as follows:
• The exponential moving average and two other trend lines are being used as qualifiers for the generation of trading-signals.
• Buy-signals for long-entries are only considered in case the EMA (yellow line in the graph below) crosses the red line.
• Sell-signals for short-entries are only considered in case the EMA (yellow line in the graph below) crosses the green line.
An example is shown in graph 4 below:
We use this indicator to determine the new trend direction that may occur by using the data of the price's past movement.
4. Bringing it all together
This section describes in detail, how this strategy combines the Momentum-RSI, the super-trend analysis and the EMA-strategy.
The strategy only generates trading-signals in case all of the following conditions and qualifiers are being met:
1. Momentum-RSI is higher than the set value of this strategy. The standard and recommended value is 60 (graph 5):
2. The super-trend analysis needs to indicate a confirmed upward-trend (for long-entry signals) or a confirmed downward-trend (for short-entry signals), respectively.
3. The EMA-strategy needs to indicate that the stock or financial security is in a confirmed upward-trend (long-entries) or downward-trend (short-entries), respectively.
The strategy will only generate trading signals if all three qualifiers are being met. This makes this strategy highly selective and is the key secret for its success.
Example for Long-Entry (graph 6):
When these conditions are met, our Long position is opened.
Example for Short-Entry (graph 7):
Trade Management Options (graph 8)
Option 1
In this dynamic version, the so-called supertrend-indicator is being used for the trade exit management. This supertrend-indicator is a sophisticated and optimized methodology which uses the dynamic ATR as one of its key input parameters.
The following settings of the supertrend-indicator can be changed and optimized (graph 9):
The dynamic SL/TP-lines of the supertrend-indicator are shown in the charts. The ATR-length and the supertrend-factor result in a multiplier value which can be used to fine-tune and optimize this strategy based on the financial security, timeframe and overall market environment.
Option 2 (graph 10):
Option 2 applies pre-defined, fixed SL and TP levels which will appear as straight horizontal lines in the chart.
Settings options (graph 11):
The following settings can be changed for the three elements of this strategy:
1. (Length Mom-Rsi): Length of our Mom-RSI indicator.
2. Mom-RSI Limit Val: the higher this number, the more momentum of the underlying trend is required before the strategy will start creating trading signals.
3. The length and factor values of the super trend indicator can be adjusted:ATR Length SuperTrend and Factor Super Trend
4. You can set the source value used by the ema trend indicator to determine the ema line: Source Ema Ind
5. You can set the EMA length and the percentage value to follow the price: Length Ema Ind and Percent Ema Ind
6. The backtesting period can be adjusted: Start and End time of BackTest
7. Dollar cost per position: this is relevant for 100% fully automated trading.
8. Trade direction can be adjusted: LONG, SHORT or BOTH
9. As we explained above, we can determine our stop-loss and take-profit levels dynamically or statically. (Version 1 or Version 2 )
Display options on the charts graph 12):
1. Show horizontal lines for the Stop-Loss and Take-profit levels on the charts.
2. Display relevant Trend Lines, including color setting options for the supertrend functionality. In the example below, green lines indicate a confirmed uptrend, red lines indicate a confirmed downtrend.
Other comments
• This indicator has been optimized to be applied for 1 hour-charts. However, the underlying principles of this strategy are supply and demand in the financial markets and the strategy can be applied to all timeframes. Daytraders can use the 1min- or 5min charts, swing-traders can use the daily charts.
• This strategy has been designed to identify the most promising, highest probability entries and trades for each stock or other financial security.
• The combination of the qualifiers results in a highly selective strategy which only considers the most promising swing-trading entries. As a result, you will normally only find a low number of trades for each stock or other financial security per year in case you apply this strategy for the daily charts. Shorter timeframes will result in a higher number of trades / year.
• Consequently, traders need to apply this strategy for a full watchlist rather than just one financial security.
Opening Range with FibsThe indicator uses a time range and another instrument for time reference, so that it works in the time zone you care about. I have set the default to SPX500USD since it is in EST ( SPX , ES and many futures are on Chicago time and opening range gets confused). You can change the reference instrument in the settings.
You can also change the multipliers and use other values instead of 1.272 and 1.618 for Fib extensions.
TradingView has a limit as to how many objects an indicator can create so if you want to go back further for visual backtesting - use the replay tool - it will be able to draw up to the time you selected within its limits.
Let me know if you need anything else...
Happy Trading!
BankNifty 5min Supertrend Based StrategyBankNifty 5min Supertrend Based Strategy, Intraday.
Work Best at 5mint chart on BankNifty.
The strategy is designed to trade using the Supertrend indicator with session-based rules, and risk management. It allows for customization through input variables and aims to provide a clear visual representation of the Supertrend by changing the color .
The script also includes input variables for the trading session and date range, which allows the trader to specify the time period in which they want the strategy to run. The session variable specifies the start and end times of the trading session, which in this case is set to the Indian trading session from 9:15 am to 3:10 pm.
The strategy starts by defining input variables such as the session time, start and end date for the backtesting, the length of the ATR, and the Supertrend factor. It also includes options for delay at session start and stop loss points and trail percentage .
The code then checks if the current time is within the specified session and date range . If it is, the Supertrend and its direction are calculated using the defined input variables. The strategy then waits for N numbers of candles (defined by the User) to form at the start of every session i.e. 09:15 AM before entering a trade.
The entry and exit conditions for long and short trades are defined based on the change in the Supertrend direction and the number of candles formed at the session start i.e. 09:15 AM . After that, it takes entry and exit for long and short trades on the change in the Supertrend direction . Stop-loss and trailing stop-loss are also defined based on the input variables.
Stop-loss (Defined by the user) is fixed points either below or above the Entry Price for Long and Short entries.
The Supertrend plot is displayed with changing colors depending on the direction. Finally, the strategy closes all trades at the end of the session if there are any open trades.
Overall, this strategy aims to trade with the Supertrend indicator using session-based rules and risk management.
However, as with any trading strategy, it is important to thoroughly test it before using it in live trading .
Multi-Asset Month/Month % change 10yr Averages10 Year Averages of Month-on-Month % change: Shows current asset, and 3x user input assets
-For comparing seasonal tendencies among different assets.
-Choose from a variety of monthly average measures as source: sma(close, length), sma(ohlc4, length); as well as sma's of vwap, vwma, volume, volatility. (sma = simple moving average).
-Averages based on month cf previous month: i.e. Feb % = Feb compared to Jan; Jan % = Jan compared to prev year's Dec. Average of the last 10yrs of these values is the printed value.
-Plot on current year (2023), or previous year (2022). If Plotting on current year, and a month of year has not yet occured, a 9yr average will be printed.
/// notes ///
-daily bars in month is a global setting; so choose assets which have similar trading days per month. i.e. Crypto: length = 30 (days per month); Stocks/FX/Indices: length = 21 (days per month).
-only plots on Daily timeframe.
10yr Avgs; Plotting with Year = 2022; using sma(close, 21) as source for average M/M change
Tomorrow's Custom Sessions: 4x Sessions projected into next dayTool to visualize your favored sessions for the day ahead (4x custom sessions)
-Input four custom sessions; they will be projected into the next day.
-Toggle on/off each session independently.
-Adjust multiplier UP to show more days of history (setting 0 = tomorrow only; setting 1 = today and tomorrow).
-Increase number from 1>>125 to show historical sessions/days, for backtesting purposes.
-If Set to 0, showing tomorrow only; the projected session boxes will disappear as price passes through their right hand edge.
--note: the session defaults i've written are arbitrary/not special; please choose your own--
Setting '0'; shows next day's sessions only
