Extended Moving Average (MA) LibraryThis Extended Moving Average Library is a sophisticated and comprehensive tool for traders seeking to expand their arsenal of moving averages for more nuanced and detailed technical analysis.
The library contains various types of moving averages, each with two versions - one that accepts a simple constant length parameter and another that accepts a series or changing length parameter.
This makes the library highly versatile and suitable for a wide range of strategies and trading styles.
Moving Averages Included:
Simple Moving Average (SMA): This is the most basic type of moving average. It calculates the average of a selected range of prices, typically closing prices, by the number of periods in that range.
Exponential Moving Average (EMA): This type of moving average gives more weight to the latest data and is thus more responsive to new price information. This can help traders to react faster to recent price changes.
Double Exponential Moving Average (DEMA): This is a composite of a single exponential moving average, a double exponential moving average, and an exponential moving average of a triple exponential moving average. It aims to eliminate lag, which is a key drawback of using moving averages.
Jurik Moving Average (JMA): This is a versatile and responsive moving average that can be adjusted for market speed. It is designed to stay balanced and responsive, regardless of how long or short it is.
Kaufman's Adaptive Moving Average (KAMA): This moving average is designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low.
Smoothed Moving Average (SMMA): This type of moving average applies equal weighting to all observations and smooths out the data.
Triangular Moving Average (TMA): This is a double smoothed simple moving average, calculated by averaging the simple moving averages of a dataset.
True Strength Force (TSF): This is a moving average of the linear regression line, a statistical tool used to predict future values from past values.
Volume Moving Average (VMA): This is a simple moving average of a volume, which can help to identify trends in volume.
Volume Adjusted Moving Average (VAMA): This moving average adjusts for volume and can be more responsive to volume changes.
Zero Lag Exponential Moving Average (ZLEMA): This type of moving average aims to eliminate the lag in traditional EMAs, making it more responsive to recent price changes.
Selector: The selector function allows users to easily select and apply any of the moving averages included in the library inside their strategy.
This library provides a broad selection of moving averages to choose from, allowing you to experiment with different types and find the one that best suits your trading strategy.
By providing both simple and series versions for each moving average, this library offers great flexibility, enabling users to pass both constant and changing length parameters as needed.
Statistics
Normal Distribution CurveThis Normal Distribution Curve is designed to overlay a simple normal distribution curve on top of any TradingView indicator. This curve represents a probability distribution for a given dataset and can be used to gain insights into the likelihood of various data levels occurring within a specified range, providing traders and investors with a clear visualization of the distribution of values within a specific dataset. With the only inputs being the variable source and plot colour, I think this is by far the simplest and most intuitive iteration of any statistical analysis based indicator I've seen here!
Traders can quickly assess how data clusters around the mean in a bell curve and easily see the percentile frequency of the data; or perhaps with both and upper and lower peaks identify likely periods of upcoming volatility or mean reversion. Facilitating the identification of outliers was my main purpose when creating this tool, I believed fixed values for upper/lower bounds within most indicators are too static and do not dynamically fit the vastly different movements of all assets and timeframes - and being able to easily understand the spread of information simplifies the process of identifying key regions to take action.
The curve's tails, representing the extreme percentiles, can help identify outliers and potential areas of price reversal or trend acceleration. For example using the RSI which typically has static levels of 70 and 30, which will be breached considerably more on a less liquid or more volatile asset and therefore reduce the actionable effectiveness of the indicator, likewise for an asset with little to no directional volatility failing to ever reach this overbought/oversold areas. It makes considerably more sense to look for the top/bottom 5% or 10% levels of outlying data which are automatically calculated with this indicator, and may be a noticeable distance from the 70 and 30 values, as regions to be observing for your investing.
This normal distribution curve employs percentile linear interpolation to calculate the distribution. This interpolation technique considers the nearest data points and calculates the price values between them. This process ensures a smooth curve that accurately represents the probability distribution, even for percentiles not directly present in the original dataset; and applicable to any asset regardless of timeframe. The lookback period is set to a value of 5000 which should ensure ample data is taken into calculation and consideration without surpassing any TradingView constraints and limitations, for datasets smaller than this the indicator will adjust the length to just include all data. The labels providing the percentile and average levels can also be removed in the style tab if preferred.
Additionally, as an unplanned benefit is its applicability to the underlying price data as well as any derived indicators. Turning it into something comparable to a volume profile indicator but based on the time an assets price was within a specific range as opposed to the volume. This can therefore be used as a tool for identifying potential support and resistance zones, as well as areas that mark market inefficiencies as price rapidly accelerated through. This may then give a cleaner outlook as it eliminates the potential drawbacks of volume based profiles that maybe don't collate all exchange data or are misrepresented due to large unforeseen increases/decreases underlying capital inflows/outflows.
Thanks to @ALifeToMake, @Bjorgum, vgladkov on stackoverflow (and possibly some chatGPT!) for all the assistance in bringing this indicator to life. I really hope every user can find some use from this and help bring a unique and data driven perspective to their decision making. And make sure to please share any original implementaions of this tool too! If you've managed to apply this to the average price change once you've entered your position to better manage your trade management, or maybe overlaying on an implied volatility indicator to identify potential options arbitrage opportunities; let me know! And of course if anyone has any issues, questions, queries or requests please feel free to reach out! Thanks and enjoy.
High/Low of week: Stats & Day of Week tendencies// Purpose:
-To show High of Week (HoW) day and Low of week (LoW) day frequencies/percentages for an asset.
-To further analyze Day of Week (DoW) tendencies based on averaged data from all various custom weeks. Giving a more reliable measure of DoW tendencies ('Meta Averages').
-To backtest day-of-week tendencies: across all asset history or across custom user input periods (i.e. consolidation vs trending periods).
-Education: to see how how data from a 'hard-defined-week' may be misleading when seeking statistical evidence of DoW tendencies.
// Notes & Tips:
-Only designed for use on DAILY timeframe.
-Verification table is to make sure HoW / LoW DAY (referencing previous finished week) is printing correctly and therefore the stats table is populating correctly.
-Generally, leaving Timezone input set to "America/New_York" is best, regardless of your asset or your chart timezone. But if misaligned by 1 day =>> tweak this timezone input to correct
-If you want to use manual backtesting period (e.g. for testing consolidation periods vs trending periods): toggle these settings on, then click the indicator display line three dots >> 'Reset Points' to quickly set start & end dates.
// On custom week start days:
-For assets like BTC which trade 7 days a week, this is quite simple. Pick custom start day, use verification table to check all is well. See the start week day & time in said verification table.
-For traditional assets like S&P which trade only 5 days a week and suffer from occasional Holidays, this is a bit more complicated. If the custom start day input is a bank holiday, its custom 'week' will be discounted from the data set. E.g.1: if you choose 'use custom start day' and set it to Monday, then bank holiday Monday weeks will be discounted from the data set. E.g.2: If you choose 'use custom start day' and set it to Thursday, then the Holiday Thursday custom week (e.g Thanksgiving Thursday >> following Weds) would be discounted from the data set.
// On 'Meta Averages':
-The idea is to try and mitigate out the 'continuation bias' that comes from having a fixed week start/end time: i.e. sometimes a market is trending through the week start/end time, so the start/end day stats are over-weighted if one is trying to tease out typical weekly profile tendencies or typical DoW tendencies. You'll notice this if you compare the stats with various custom start days ('bookend' start/end days are always more heavily weighted). I wanted to try to mitigate out this 'bias' by cycling through all the possible new week start/end days and taking an average of the results. i.e. on BTC/USD the 'meta average' for Tuesday would be the average of the Tuesday HoW frequencies from the set of all 7 possible custom weeks(Mon-Sun, Tues-Mon, Weds-Tues, etc etc).
// User Inputs:
~Week Start:
-use custom week start day (default toggled OFF); Choose custom week start day
-show Meta Averages (default toggled ON)
~Verification Table:
-show table, show new week lines, number of new week lines to show
-table formatting options (position, color, size)
-timezone (only for tweaking if printed DoW is misaligned by 1 day)
~Statistics Table:
-show table, table formatting options (position, color, size)
~Manual Backtesting:
-Use start date (default toggled OFF), choose start date, choose vline color
-Use end date (defautl toggled OFF), choose end date, choose vline color
// Demo charts:
NQ1! (Nasdaq), Full History, Traditional week (Mon>>Friday) stats. And Meta Averages. Annotations in purple:
NQ1! (Nasdaq), Full History, Custom week (custom start day = Wednesday). And Meta Averages. Annotations in purple:
Equity Sessions [vnhilton]Note: Numbers in the chart above, particularly volume, are incorrect as I didn't have extra market data at the time of publication. Default settings are set for US markets.
(OVERVIEW)
This indicator was made specifically for equity markets which have pre-market and after-hours trading, though can be used for any other markets without these sessions, there are many other session indicators better suited for those markets. What makes this indicator different to the hundreds of session indicators out there will be highlighted in bold in the Features section below.
(FEATURES)
- After-Hours session can start earlier if the day ends short and starts after-hours trading earlier due to holidays for example
- Sessions constrained to regular trading hours can also adjust for short days as well
- Show volume for each session and also as a percentage/multiplier of day volume, average day volume with customisable period
- Show range for each session and also as a percentage/multiplier of the daily ATR with customisable period
- Lookback period for the boxes
- Customisable text size, placement, colour, name
- Customisable session lengths and constraints (regular trading hours or all including extending trading hours)
- Customisable border widths, styles and colours, and session background colour
- Toggles to show/hide sessions, volume, day volume, average day volume, session range and day ATR
Day of Month - Volatility Report█ OVERVIEW
The indicator analyses the volatility and reports the statistics by the days of the month.
█ CONCEPTS
The markets move every day. But how does a market move during a month?
Here are some ideas to explore:
Does the volatility kick in with the start of a new month?
Do the markets slow down at the end of the month?
Which period of the month is the most volatile?
How does this relate to your best and worst trades?
When should you take a break?
DAX
EURGBP
Binance Coin
█ FEATURES
Comparison modes
Compare how each day moves relative to the monthly volatility or the average daily volatility.
Configurable outputs
Output the report statistics as mean or median.
Range filter
Select the period to report from.
█ HOW TO USE
Plot the indicator and visit the 1D, 24H, or 1440 minutes timeframe.
█ NOTES
Gaps
The indicator includes the volatility from gaps.
Trading session
The indicator analyses each day from the daily chart, defined by the exchange trading session (see Symbol Info).
Extended trading session
The indicator can include the extended hours when activated on the chart, using the 24H or 1440 minutes timeframe.
Overnight session
The indicator supports overnight sessions (open and close on different calendar days). For example, EURUSD will report Monday’s volatility from Sunday open at 17:00 to Monday close at 17:00.
This is a PREMIUM indicator. In complement, you might find useful my free Time of Day - Volatility Report .
LibrarySupertrendLibrary "LibrarySupertrend"
selective_ma(condition, source, length)
Parameters:
condition (bool)
source (float)
length (int)
trendUp(source)
Parameters:
source (float)
smoothrng(source, sampling_period, range_mult)
Parameters:
source (float)
sampling_period (simple int)
range_mult (float)
rngfilt(source, smoothrng)
Parameters:
source (float)
smoothrng (float)
fusion(overallLength, rsiLength, mfiLength, macdLength, cciLength, tsiLength, rviLength, atrLength, adxLength)
Parameters:
overallLength (simple int)
rsiLength (simple int)
mfiLength (simple int)
macdLength (simple int)
cciLength (simple int)
tsiLength (simple int)
rviLength (simple int)
atrLength (simple int)
adxLength (simple int)
zonestrength(amplitude, wavelength)
Parameters:
amplitude (int)
wavelength (simple int)
atr_anysource(source, atr_length)
Parameters:
source (float)
atr_length (simple int)
supertrend_anysource(source, factor, atr_length)
Parameters:
source (float)
factor (float)
atr_length (simple int)
Relative Daily Change% by SUMIT
"Relative Daily Change%" Indicator (RDC)
The "Relative Daily Change%" indicator compares a stock's average daily price change percentage over the last 200 days with a chosen index.
It plots a colored curve. If the stock's change% is higher than the index, the curve is green, indicating it's doing better. Red means the stock is under-performing.
This indicator is designed to compare the performance of a stock with specific index (as selected) for last 200 candles.
I use this during a breakout to see whether the stock is performing well with comparison to it`s index. As I marked in the chart there was a range zone (red box), we got a breakout with good volume and it is also sustaining above 50 and 200 EMA, the RDC color is also in green so as per my indicator it is performing well. This is how I do fine-tuning of my analysis for a breakout strategy.
You can select Index from the list available in input
**Line Color Green = Avg Change% per day of the stock is more than the Selected Index
**Line Color White = Avg Change% per day of the stock is less than the Selected Index
If you want details of stocks for all index you can ask for it.
Disclaimer : **This is for educational purpose only. It is not any kind of trade recommendation/tips.
[R]2 - ReversionThe Idea:
I had the idea for this script when I read an article about how assets tend to revert to their long-term average or mean. The concept behind "R2" is based on the assumption that extreme deviations from the average tend to be corrected. For example, if an asset is trading well above its historical average, there is a possibility that the price will return towards the average. Conversely, if an asset is trading well below its average, there is a tendency for it to move back towards the average.
This concept serves as the foundation for this script. I have tried to keep the representation as simple as possible, and please remember that "Reversion" (as it's called in financial terms) is not a guaranteed rule but a statistical phenomenon.
The Indicator:
This indicator calculates the average and the distance of closing prices from this average every X periods. The calculated value fluctuates between 0. If the calculated value moves from above towards the zero line, it may indicate further declining prices. If the value moves from below towards the zero line, it may indicate rising prices. If the value is below the zero line, the area between the zero line and the calculated value is displayed in red. If the value is above the zero line, the area is displayed in green.
You can adjust the number of periods. The 'Multiplier' allows you to set how sensitive the indicator reacts, and the 'Threshold' variable sets the threshold for calculating a new average. It's best to adjust the settings to find the most suitable configuration for your needs.
Average purchase price 0.1 [PATREND]
Average purchase price
This tool calculates the average purchase and sell price and the profit/loss ratio for the selected symbol based on the user's inputs for the purchase and sell prices and the entry and exit amounts.
Using Average purchase price with DCA strategy
This tool can be used to track the performance of your dollar cost averaging (DCA) investment strategy.
This tool allows you to enter information about your purchase and sell transactions, such as the purchase and sell price and the entry and exit amount, and it calculates the average purchase and sell price and the profit/loss ratio based on this information.
When using a DCA strategy, you can enter information about your regular purchase and sell transactions and the tool will calculate the average purchase and sell price for you.
You can use this information to determine if your strategy is working well and make the necessary adjustments.
In addition, this tool can help you determine when you should increase or decrease the regular investment amounts that you make as part of your DCA strategy.
It can also show you the profit/loss ratio for each sell transaction that you made.
_________________________________
We hope you find it useful.
Don't hesitate to try this tool and customize its settings to meet your trading needs.
We look forward to seeing your opinions and comments.
______________________________________________________________________________________________________
Average purchase price
هذه الأداة تحسب متوسط سعر الشراء والبيع ونسبة الربح / الخسارة للرمز المحدد بناءً على إدخالات المستخدم لأسعار الشراء والبيع ومبالغ الدخول والخروج.
استخدام Average purchase price مع استراتيجية DCA
يمكن استخدام هذه الأداة لتتبع أداء استراتيجية الاستثمار المتوسط التكلفة الدولارية (DCA) الخاصة بك.
تتيح لك هذه الأداة إدخال معلومات عن عمليات الشراء والبيع الخاصة بك، مثل سعر الشراء والبيع وكمية الدخول والخروج، ويقوم بحساب متوسط سعر الشراء والبيع ونسبة الربح / الخسارة بناءً على هذه المعلومات.
عند استخدام استراتيجية DCA، يمكنك إدخال معلومات عن عمليات الشراء والبيع المنتظمة التي تقوم بها وستقوم الأداة بحساب متوسط سعر الشراء والبيع لك. يمكنك استخدام هذه المعلومات لتحديد ما إذا كانت استراتيجيتك تعمل بشكل جيد وإجراء التعديلات اللازمة.
بالإضافة إلى ذلك
يمكن لهذه الأداة مساعدتك في تحديد متى يجب عليك زيادة أو تقليل مبالغ الاستثمار المنتظمة التي تقوم بها كجزء من استراتيجية DCA. كما يمكنها أن تظهر لك نسبة الربح / الخسارة في كل عملية بيع قمت بها.
تصرف كخبير ترجمه مختص باسواق المال وترجم هذا النص للغه الانكليزيه بشكل دقيق
_________________________________
نأمل أن تجدوه مفيدًا لكم .
لا تترددوا في تجربة هذه الأداة وتخصيص إعداداتها لتلبية احتياجاتكم التداولية.
نتطلع إلى رؤية آرائكم وتعليقاتكم .
TradeMaster SignalsTrading effectively requires a range of techniques, experience, and expertise. From technical analysis to market fundamentals, traders must navigate multiple factors, including market sentiment and economic conditions. However, traders often find themselves overwhelmed by market noise, making it challenging to filter out distractions and make informed decisions. To address this, we present a powerful indicator package designed to assist traders on their journey to success.
The TradeMaster indicator package encompasses a variety of trading strategies, including the SMC (Supply, Demand, and Price Action) approach, along with many other techniques. By leveraging concepts such as price action trading, support and resistance analysis, supply and demand dynamics, these indicators can empower traders to analyze entry and exit positions with precision. Unlike other forms of technical analysis that produce values or plots based on historical price data, Price Action brings you the facts straight from the source - the current price movements.
The indicator package consists of three powerful indicators that can be used individually or together to maximize trading effectiveness.
⭐ About the Signals Indicator
This indicator offers a unique opportunity for traders to design their own personalized trading strategy. It has a built-in backtesting system, which allows you to thoroughly analyze the performance of your strategy before implementing it in live trading. With the ability to customize and test your strategy using historical data, the Signals indicator empowers you to make data-driven decisions and refine your trading approach.
👉 How does it work?
The Signals indicator provides users with the ability to select trigger conditions and further narrow them down using confirmations.
Conditions are quantitative factors that influence the generation of signals on the chart and in the backtest table. You can enable multiple conditions to create a comprehensive set of criteria for signal generation.
Confirmations, on the other hand, are qualitative factors that selectively filter out conditions based on their alignment with the chosen confirmations. This helps refine the signals and provide more targeted trading opportunities. Multiple confirmations can be enabled to further enhance the precision of the signals.
A well-balanced strategy in the Signals indicator involves carefully selecting a combination of conditions and confirmations to generate accurate trading signals. Finding the right balance between them is crucial for consistent and profitable trading.
To offer even more flexibility, the Signals indicator includes two powerful main functions:
Target Placement System: This feature allows you to set up to 6 targets with a stop loss level and partial exit percentages. You can choose between automatic target creation or manual customization, giving you control over your profit targets.
Exit Strategy: With this feature, you can define your preferred trailing stop strategy, allowing you to implement a systematic approach to exiting trades. By setting appropriate trailing stop levels, you can limit potential losses, while the system secures profits by automatically closing positions partially when certain price targets are reached. This may help you to maintain discipline in your trading and optimize your risk-reward ratio.
With over 30 unique conditions, 10 confirmations, and the deep Target Placement and Exit Strategy systems, the Signals indicator offers a vast array of possibilities. In fact, there are potentially millions of different strategy outputs available for each ticker. Despite its complexity, the script remains lightweight and fast, ensuring smooth performance.
The Signals Backtest table provides a comprehensive overview of your strategy's performance. You can track your current position with all the necessary details, allowing you to monitor your trades effectively and make informed decisions based on the backtest results.
⚠️ WARNING!
Backtest results do not guarantee future performance. Strategies tested on synthetic data may not accurately represent real-world results. Testing should be conducted on charts that reflect actual closing prices.
The indicator displays buy/sell signals intended to support traders' analysis. There are numerous possibilities and combinations available to create your own unique strategies, whether trading with or against the trend or capturing oversold bounces. These are just a few of the many options! Our indicator can easily be tailored to fit your trading strategy.
The settings that influence the signal-generating algorithm play a crucial role in effectively utilizing the signals. We provide users with the flexibility to modify the settings to align with their trading style, while also offering simple adjustment methods using various techniques.
Each method for modifying the signal settings has been designed to meet specific user needs. It is important to understand that one method is not necessarily more accurate than another.
It is essential to understand that signal indications generally serve as trend confirmations, rather than direct entry and exit points. Focusing on the easy use of signal settings and utilizing other functionalities in our toolkit will likely be a better decision than attempting to find the "holy grail" of optimized signal settings and solely relying on following the signals.
⭐ Conclusion
We hold the view that the true path to success is the synergy between the trader and the tool, contrary to the common belief that the tool itself is the sole determinant of profitability. The actual scenario is more nuanced than such an oversimplification. Our aim is to offer useful features that meet the needs of the 21st century and that we actually use.
🛑 Risk Notice:
Everything provided by trademasterindicator – from scripts, tools, and articles to educational materials – is intended solely for educational and informational purposes. Past performance does not assure future returns.
Liquidation Ranges + Volume/OI Dots [Kioseff Trading]Hello!
Introducing a multi-faceted indicator "Liquidation Ranges + Volume Dots" - this indicator replicates the volume dot tools found on various charting platforms and populates a liquidation range on crypto assets!
Features
Volume/OI dots populated according to user settings
Size of volume/OI dots corresponds to degree of abnormality
Naked level volume dots
Fixed range capabilities for volume/OI dots
Visible time range capabilities for volume/OI dots
Lower timeframe data used to discover iceberg orders (estimated using 1-minute data)
S/R lines drawn at high volume/OI areas
Liquidation ranges for crypto assets (10x - 100x)
Liquidation ranges are calculated using a popular crypto exchange's method
# of violations of liquidation ranges are recorded and presented in table
Pertinent high volume/OI price areas are recorded and presented in table
Personalized coloring for volume/OI dots
Net shorts / net long for the price range recorded
Lines shows reflecting net short & net long increases/decreases
Configurable volume/OI heatmap (displayed between liquidation ranges)
And some more (:
Liquidation Range
The liquidation range component of the indicator uses a popular crypto exchange's calculation (for liquidation ranges) to populate the chart for where 10x - 100x leverage orders are stopped out.
The image above depicts features corresponding to net shorts and net longs.
The image above shows features corresponding to liquidation zones for the underlying coin.
The image above shows the option to display volume/oi delta at the time the corresponding grid was traded at.
The image above shows an instance of using the "fixed range" feature for the script.
*The average price of the range is calculated to project liquidation zones.
*Heatmap is calculated using OI (or volume) delta.
Huge thank you to Pine Wizard @DonovanWall for his range filter code!
Price ranges are automatically detected using his calculation (:
Volume / OI Dots
Similar to other charting platforms, the volume/OI dots component of the indicator distinguishes "abnormal" changes in volume/OI; the detected price area is subsequently identified on the chart.
The detection method uses percent rank and calculates on the last bar of the chart. The "agelessness" of detection is contingent on user settings.
The image above shows volume dots in action; the size of each volume dot corresponds to the amount of volume at the price area.
Smaller dots = lower volume
Larger dots = higher volume
The image above exemplifies the highest aggression setting for volume/OI dot detection.
The table oriented top-right shows the highest volume areas (discovered on the 1-minute chart) for the calculated period.
The open interest change and corresponding price level are also shown. Results are listed in descending order but can also be listed in order of occurrence (most relevant).
Additionally, you can use the visible time range feature to detect volume dots.
The feature shows and explains how the visible range feature works. You select how many levels you want to detect and the script will detect the selected number of levels.
For instance, if I select to show 20 levels, the script will find the 20 highest volume/OI change price areas and distinguish them.
The image above shows a narrower price range.
The image above shows the same price range; however, the script is detecting the highest OI change price areas instead of volume.
* You can also set a fixed range with this feature
* Naked levels can be used
Additionally, you can select for the script to show only the highest volume/ OI change price area for each bar. When active, the script will successively identify the highest volume / OI change price area for the most recent bars.
Naked Levels
The image above shows and explains how naked levels can be detected when using the script.
And that's pretty much it!
Of course, there're a few more features you can check out when you use the script that haven't been explained here (:
Thank you again to @DonovanWall
Thank you to @Trendoscope for his binary insertion sort library (:
Thank you to @PineCoders for their time library
Thank you for checking this out!
CC Trend strategy 2- Downtrend ShortTrend Strategy #2
Indicators:
1. EMA(s)
2. Fibonacci retracement with a mutable lookback period
Strategy:
1. Short Only
2. No preset Stop Loss/Take Profit
3. 0.01% commission
4. When in a profit and a closure above the 200ema, the position takes a profit.
5. The position is stopped When a closure over the (0.764) Fibonacci ratio occurs.
* NO IMMEDIATE RE-ENTRIES EVER!*
How to use it and what makes it unique:
This strategy will enter often and stop quickly. The goal with this strategy is to take losses often but catch the big move to the downside when it occurs through the Silvercross/Fibonacci combination. This is a unique strategy because it uses a programmed Fibonacci ratio that can be used within the strategy and on any program. You can manipulate the stats by changing the lookback period of the Fibonacci retracement and looking at different assets/timeframes.
This description tells the indicators combined to create a new strategy, with commissions and take profit/stop loss conditions included, and the process of strategy execution with a description of how to use it. If you have any questions feel free to PM me and boost if you found it helpful. Thank you, pineUSERS!
CHEATCODE1
High of Day Low of Day hourly timings: Statistics. Time of day %High of Day (HoD) & Low of Day (LoD) hourly timings: Statistics. Time of day % likelihood for high and low.
//Purpose:
To collect stats on the hourly occurrences of HoD and LoD in an asset, to see which times of day price is more likely to form its highest and lowest prices.
//How it works:
Each day, HoD and LoD are calculated and placed in hourly 'buckets' from 0-23. Frequencies and Percentages are then calculated and printed/tabulated based on the full asset history available.
//User Inputs:
-Timezone (default is New York); important to make sure this matches your chart's timezone
-Day start time: (default is Tradingview's standard). Toggle Custom input box to input your own custom day start time.
-Show/hide day-start vertical lines; show/hide previous day's 'HoD hour' label (default toggled on). To be used as visual aid for setting up & verifying timezone settings are correct and table is populating correctly).
-Use historical start date (default toggled off): Use this along with bar-replay to backtest specific periods in price (i.e. consolidated vs trending, dull vs volatile).
-Standard formatting options (text color/size, table position, etc).
-Option to show ONLY on hourly chart (default toggled off): since this indicator is of most use by far on the hourly chart (most history, max precision).
// Notes & Tips:
-Make sure Timezone settings match (input setting & chart timezone).
-Play around with custom input day start time. Choose a 'dead' time (overnight) so as to ensure stats are their most meaningful (if you set a day start time when price is likely to be volatile or trending, you may get a biased / misleadingly high readout for the start-of-day/ end-of-day hour, due to price's tendency for continuation through that time.
-If you find a time of day with significantly higher % and it falls either side of your day start time. Try adjusting day start time to 'isolate' this reading and thereby filter out potential 'continuation bias' from the stats.
-Custom input start hour may not match to your chart at first, but this is not a concern: simply increment/decrement your input until you get the desired start time line on the chart; assuming your timezone settings for chart and indicator are matching, all will then work properly as designed.
-Use the the lines and labels along with bar-replay to verify HoD/LoD hours are printing correctly and table is populating correctly.
-Hour 'buckets' represent the start of said hour. i.e. hour 14 would be populated if HoD or LoD formed between 14:00 and 15:00.
-Combined % is simply the average of HoD % and LoD %. So it is the % likelihood of 'extreme of day' occurring in that hour.
-Best results from using this on Hourly charts (sub-hourly => less history; above hourly => less precision).
-Note that lower tier Tradingview subscriptions will get less data history. Premium acounts get 20k bars history => circa 900 days history on hourly chart for ES1!
-Works nicely on Btc/Usd too: any 24hr assets this will give meaningful data (whereas some commodities, such as Lean Hogs which only trade 5hrs in a day, will yield less meaningful data).
Example usage on S&P (ES1! 1hr chart): manual day start time of 11pm; New York timezone; Visual aid lines and labels toggled on. HoD LoD hour timings with 920 days history:
AlexD Intraday market footprintThe indicator shows probability of a moving average non reversal at certain moment of day.
IMF_Predict line shows the probability of a reversal for the specified period.
moving average - period/2 shifted sma of typical price ( (close+high+low)/3 ).
Parameters:
Number of days - previous days to calculate the probability
SMA filter period - chart smoothing period
IMF smooth period - additional indicator smoothing after calculation
IMF predict period - period for calculating the probability of a reversal in the next N bars
Skip N hours in days(optimisation) - I recommend a half of the normal session time. Low values - long calculation time, High values - skipping days.
Cumulative TrendThe "Cumulative Trend" indicator is designed to provide insights into the cumulative price trend while considering volume and volatility. It aims to identify potential shifts in the trend based on the relationship between price changes and these factors. Let's break down the steps involved: In the code, the term "previous" refers to the average of the previous data points over a defined length. Instead of considering the exact previous data point, the code calculates the average of a specific number of preceding data points. It enables the consideration of multiple preceding values, resulting in a smoother representation of trends and a more robust analysis of the data
Calculation of Volume and Volatility Adjusted Price Change:
The indicator starts by calculating the price change as a percentage relative to the previous opening price.
It determines the standard deviation of the close prices, providing a measure of price volatility.
The coefficient of variation is calculated by comparing the standard deviation to the previous close price.
Intraday volatility is calculated as the difference between the high and low prices divided by the close price.
Various ratios are derived by comparing the current volume to the previous volume and relating the intraday volatility to the coefficient of variation.
Cumulative Sum:
The Volume and Volatility Adjusted Price Change values are cumulatively summed to form the cumulative sum.
This cumulative sum represents the overall trend of the price changes, incorporating the impact of volume and volatility.
Average Cumulative Sum:
The average cumulative sum is calculated by applying a simple moving average to the cumulative sum over a specified window size.
This moving average helps smooth out the cumulative trend and highlights the general direction of the price changes.
Average Cumulative Sum Change:
The change in the average cumulative sum is determined by subtracting the previous average cumulative sum value from the current value.
This calculation provides insights into the rate of change in the cumulative trend.
Color Determination:
Thresholds are introduced to define levels at which the trend is considered to change.
The average cumulative sum change is compared against these thresholds.
If the average cumulative sum change exceeds the upper threshold, the color is set to green, indicating a potential upward trend.
If the average cumulative sum change falls below the lower threshold, the color is set to red, indicating a potential downward trend.
If the average cumulative sum change is within the threshold range, the color is set to a yellowish tone, indicating a neutral or transitional phase.
Plotting:
The average cumulative sum is plotted as a line on the chart.
The color of the line is determined based on the calculated color value, reflecting the perceived trend direction.
In summary, the Cumulative Trend indicator integrates volume, volatility, and price changes to provide a cumulative perspective on the trend. It tracks the cumulative price changes, calculates the average trend, and visually represents potential trend shifts through color changes. Traders and analysts can utilize this indicator to identify and monitor changes in the underlying trend, aiding in decision-making and market analysis.
ATR_InfoWhat Is the Average True Range (ATR)?
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
Each instrument per unit of time passes its average value of the true range, but there are moments when the volatility explodes or abruptly decays, these phenomena introduce large distortions into the average value of the true range.
The ATR_WPB function calculates the average value of the true range for the specified number of bars, while excluding paranormally large and paranormally small bars from the calculation of the average.
For example, if the instrument has passed a small ATR value, then it has many chances to continue moving, but if the instrument has passed its ATR value, then the chances of continuing to move are extremely low.
Library "ATR_Info"
ATR_Info: Calculates ATR without paranormal bars
ATR_WPB(source, period, psmall, pbig)
ATR_WPB: Calculates ATR without paranormal bars
Parameters:
source (float) : ATR_WPB: (series float) The sequence of data on the basis of which the ATP calculation will be made
period (int) : ATR_WPB: (int) Sequence size for ATR calculation
psmall (float) : ATR_WPB: (float) Coefficient for paranormally small bar
pbig (float) : ATR_WPB: (float) Coefficient for paranormally big bar
Returns: ATR_WPB: (float) ATR without paranormal bars
Binary Option Ultimate Backtester-V.1[tanayroy]The Binary Option strategy backtester gives the user extensive power to test any kind of strategy with advance trade management rules.
The strategy tester accepts external scripts as strategy sources. You can add your strategy and test it for historical stats.
Few assumption regarding strategy tester:
We are opening position at next candle after signal come
We are taking the position at opening price
Our call will be profitable if we get a green candle and put will be profitable if we get a red candle
We can open only one trade at a time. So if we are in trade, subsequent signals will be ignored.
How to make your strategy code compatible for strategy backtesting?
In your strategy code file add following lines:
Signal = is_call ? 1 : is_put ? -1 : 0
plot(Signal, title="🔌Connector🔌", display = display.none)
Is_call and is_put is your buy and sell signal. Plot the signal without displaying it in the chart. The new TradingView feature display = display.none, will not display the plot.
All Input options
Group: STRATEGY
Add Your Binary Strategy: External strategy to back test.
Trade Call/Put: Select CALL, to trade Call, PUT, to trade Put. Default is BOTH, Trading Call and Put both.
Number of Candles to Hold: How many candles to hold per trade. Default 1. If you want to hold the option for 30 minutes and you are testing your strategy in 15m intervals, use 2 candle holding periods.
GROUP: MARTINGALE
Martingale Level: Select up to 15 Martingale. Select 1 for no Martingale.
Use Martingale At Strategy Level: Instead of using Martingale per trade basis, using Martingale per signal basis. Like if we make a loss in the first signal, instead of starting martingale immediately we’ll wait for the next signal to put the martingale amount. For example if you start with $1 and you lose, at the next signal you will invest $2 to recover your losses.
Strategy Martingale Level: Select up to 15 Martingale at strategy signal level. Only workable if Use Martingale At Strategy Level is selected.
Type of Trade: Martingale trade type. Only workable if we are using Martingale Level more than 1.
It can be:
“SAME”: If you are trading CALL and incur a loss, you are taking CALL in subsequent Martingale levels.
“OPSITE”: if you are trading CALL and incur a loss, you are taking PUT in subsequent Martingale levels.
“FOLLOW CANDLE COLOR”: You are following candle color in Martingale levels, i.e if the loss candle is RED, you are taking PUT in subsequent candles.
“OPPOSITE CANDLE COLOR”: You are taking opposite candle color trade, i.e if the loss candle is RED, you are taking CALL in subsequent candle.
GROUP: TRADE MANAGEMENT
Initial Investment Per Option: Initial investment amount per trade
Payout: Per trade payout in percentage
Use Specific Session: Select to test trade on specific session.
Trading Session: Select trading session. Only workable if Use Specific Session is selected.
Use Date Range: Select to use test trades between dates.
Start Time: Select Start Time. Only workable if Use Date Range is selected.
End Time: Select end Time. Only workable if Use Date Range is selected.
Early Quit: Select to quit trade for the day after consecutive win or loss
Quit Trading after Consecutive Win: Number of consecutive wins. Only workable if early Early Quit is selected.
Quit Trading after Consecutive Loss: Number of consecutive losses. Only workable if early Early Quit is selected.
Buy/Sell Flip: Use buy signal for sell and sell signal for buy.
GROUP:STATS
Show Recent Stats: Show win trades in last 3,5,10,15,25 and 30 trades.
Show Daily Stats: Day wise win trades and total trades.
Show Monthly Stats: Month wise win trades and total trades.
Result and stat output:
Back tester without any strategy.
Strategy added with default option.
Stats with 7 Martingales. You can test up to 15.
Optional Stats:
Example Strategy code used :
//@version=5
indicator("Binary Option Strategy",overlay = true)
length = input.int(7, minval=1)
src = input(close, title="Source")
mult = input.float(3.0, minval=0.001, maxval=50, title="StdDev")
basis = ta.sma(src, length)
dev = mult * ta.stdev(src, length)
upper = basis + dev
lower = basis - dev
fab_candle_upcross=(high< upper and low>basis)
fab_candle_downcross= (high< basis and low>lower)
up_cross=ta.barssince(ta.crossover(close,basis))
down_cross=ta.barssince(ta.crossunder(close,basis))
is_first_up=false
is_first_down=false
if fab_candle_upcross
for a=1 to up_cross
if fab_candle_upcross
is_first_up:=false
break
else
is_first_up:=true
if fab_candle_downcross
for a=1 to down_cross
if fab_candle_downcross
is_first_down:=false
break
else
is_first_down:=true
//strategy for buying call
is_call=(is_first_up or is_first_down ) and close>open
//strategy for selling call
is_put=(is_first_up or is_first_down ) and close<open
Signal = is_call ? 1 : is_put ? -1 : 0
plot(Signal, title="🔌Connector🔌", display = display.none)
BladeSCALPER by MetaSignalsProBladeSCALPER
The sharpest tool to scalp M and W patterns
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✔️ Get a clear signal of the next probable reversal move
✔️ Get instantly the zone where the price will probably get attracted to
✔️ Adjust TP1/TP2/TP3 accordingly to the PowerZONES
✔️ Check the winning rate of the M & W patterns on a time period
✔️ Optimize the probability of success of the M & W patterns
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📌 For who?
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Initialy, scalping is based on small moves, supposedly more predictable than big ones and repeating this operation many times.
For that, scalping means usally daytrading and not everybody can/want to be a daytrader: managing one's emotions is just critical;
But you can also use this indicator on a bigger time frame and trade when you want the M & Ws!
So basicaly BladeSCALPER is for anybody who wants to trade succesfully M&W patterns whatever Timeframe, whatever asset!
📌 For which asset?
-------------------------
BladeSCALPER is universal and works fine on all assets and all time-frames;
📌Why we made these innovations?
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"Double Tops" and "Double Bottoms", commonely called "M" and "W" as the letter explicitely shows these patterns, are some of the most predictive patterns you can find.
To exploit them, we needed to have an all in one tool:
◾ a very sharp scalping and innovative tool with embed statistics
◾ identify Risk/Reward ratio for TakeProfits
◾ and advanced Supports and Resistances information i.e the PowerZONES
📌 How to trade with BladeSCALPER ?
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🔹 ScalpUP / ScalpDOWN Signals
The signals are given when the patterns of M and W are identified, in real time and do not repaint.
☝️ Quite often the Market will test the bottoms and the tops before validating such a figure;
👉 Only enter the trade when the candle closes clearly inside the coloured zone and not immediately on the signal.
🔹 PowerZONES
We innovated on the basic Supports and Resistances concept by adding new features with:
◾ zones that correspond better to real life trading than lines
◾ zones that change color depending of their position vs price : they turn red is the price is below them and blue if they are above.
◾ strength / attractivity of these zones = how many times the Support/Resistance have been touched in the past that will magnetize the price
◾ and distance between these zones to give a clear picture
Importance of the PowerZONES
In the current version, the TPs do not adjust to the PowerZONES, precisely to be able to keep a global statistical view;
☝️ But when you plan to trade on a signal, the real relevance is to adjust them according to the PowerZONES, of course;
👉 When buying, place your TPs just below the consecutive PowerZONES that the price could test
👉 When selling, place them just above the consecutive PowerZONES
🔹 TP1/TP2/TP3
TakeProfits are set theoretically and based on 3 risk/reward ratios: 1 / 1.5 / 2 ;
But of course this is just a setting to get an overall view of the effectiveness of the pattern on the current asset;
if you change these settings, you'll see that the Stats change accordingly.
☝️ Again, when you plan to trade on a signal, the real relevance is to adjust them according to the PowerZONES, of course;
🔹 StatsPANEL
With this innovative feature you can now see immediately
◾ the probability of win, based on the past patterns
◾ the exacts number of trades that have reached the TP1/TP2/TP3
◾ and more importantly the gains made by these trades in pips
We introduce also 2 important possibilities to improve the precision and relience of BladeSCALPER
◾ the PatternFACTOR can be changed; it defines a key percentage of the M & W patterns
◾ the MoveringAverageFILTER can be activated to
◽ suppress M patterns when the price is below the selected MovingAverage
◽ suppress W patterns when the price is over the selected MovingAverage
👉 Modifying these variables will change immediately the statistics just like the position of the TP1/TP2/TP3 and HistoryMax variables.
📌 Importance of setting up a Multi TimeFrame and doing a trend analysis
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Even if you are on a scalping mode, it is crucial you set up a Multi Time Frame workspace and that you conduct a trend analysis before entering the market.
If you don't, you won't maximize your chances;
No indicator is 100% reliable, because the market cannot be modelized; anyone who tells you otherwise is lying to your face;
However, a statistical approach to the market is possible, because agents are not incoherent.
This is the meaning of stats we apply on double tops and double bottoms;
But to reinforce this point, you need to know what's happening on the next higher time unit to get a global view.
To do this, it's important to do a trend analysis or have a trend analysis tool.
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🎛️ Configuration
----------------------
◾ Buy/Sell Signals: choose if you want to see only W or only M pattern signals
◾ PowerZones: uncheck if you don't want to see them (not recommanded)
◾ RewardBoxText: uncheck if you don't want to see the words "Entry, TP1, TP2, TP3"
◾ TakeProfit1/TakeProfit2/TakeProfit3: by default correspond to the multiple of the risk zone in grey under/above "Entry" i.e it is the classic concept of Risk/Reward ratio
◾ PowerZoneTouch: sets the number of time the zone has been touched
◾ PowerZoneDensity: increase this number if you want the number of zones to increase and reversely
◾ RewardBoxLength: adjust the standard number to the length of the anticipated move in duration
◾ StopLossExtraPoints: for a W pattern (ScalpUP) will bring lower the lower border of the RewardBOX; in a M pattern (ScalpDOWN) will bring higher the higher border of the RewardBOX; it will automatically move the distance of the TP1/TP2/TP3
◾ HistoryMax: the number of units taken into account to set the PowerZONES and the past M & W patterns
◾ PatternFactor: defines a key percentage of the M & W patterns
◾ MovingAverageFilter:
◽ untick (by default) : the filter is OFF
◽ ticked : the filter is ON
◾ MovingAveragePeriod: choose the speed of the average
◾ MovingAverageType: choose among all the types of averages available
◾ Applied to: define on which available moment of the Price the average is applied (close, open, highest...)
🛠️ Calculation & Precisions
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🔹 TP1/TP2/TP3
the 3 risk/reward ratios: 1 / 1.5 / 2 are multiples of the height of the grey zone = distance between your StopLoss and the entry line;
🔹 %WIN
Note that the % of success (%WIN) must be entered correctly;
Your risk/reward ratio is key and more important than the % success of the signal; you can have a % success of 30% (%WIN) which creates more points earned than a % success of 60% depending on your risk/reward ratio = the position of your TPs;
🔹 Calculation of points/pips
These are full points and we don't calculate partial outputs.
So if you have a tp1 at 20 and a tp2 at 100, if you get to tp2 you get 100 and not 20+100.
Stoplosses are of course calculated in negative.
🔹 PowerZONES
The originality of our concept is to test how many times a zone has been touched
The more the market has touched this zone the more probable it becomes a strategic zone where the liquidity will accumulate and thus will be chased!
Stablecoins market capA simple indicator that displays either the aggregated market cap of the top five stablecoins, or it displays all coins at once (look in the settings).
Because of limitations with the sourced data the indicator only works on the daily timeframe or higher.
Risk to Reward - FIXED SL BacktesterDon't know how to code? No problem! TradingView is an excellent platform for you. ✅ ✅
If you have an indicator that you want to backtest using a risk-to-reward ratio or fixed take profit/stop loss levels, then the Risk to Reward - FIXED SL Backtester script is the perfect solution for you.
introducing Risk to Reward - FIXED SL Backtester Script which will allow you to test any indicator / Signal with RR or Fixed SL system
How does it work ?!
Once you connect the script to your indicator, it will analyze your entry points and perform calculations based on them. It will then open trades for you according to the specified inputs in the script settings.
HOW TO CONNECT IT to your indicator?
simply open your indicator code and add the below line of code to it
plot(Signal ? 100 : 0,"Signal",display = display.data_window)
Replace Signal with the long condition from your own indicator. You can also modify the value 100 to any number you prefer. After that, open the settings.
Once the script is connected to your indicator, you can choose from two options:
Risk To Reward Ratio System
Fixed TP/ SL System
🔸if you select the Risk to Reward System ⤵️
The Risk-to-Reward System requires the calculation of a stop loss. That's why I have included three different types of stop-loss calculations for you to choose from:
ATR Based SL
Pivot Low SL
VWAP Based SL
Your stop loss and take profit levels will be automatically calculated based on the selected stop loss method and your risk-to-reward ratio.
You can also adjust their values to match your desired risk level. The trades will be displayed on the chart.
with the ability to change their values to match your risk.
once this is done, trades will be displayed on the chart
🔸if you select the Fixed system ⤵️
You have 2 inputs, which are FIXED TP & Fixed SL
input the values you want, and trades will be on your chart...
I have also added a Breakeven feature for you.
with this Breakeven feature the trade will not just move SL to Entry ?! NO NO, it will place it above entry by a % you input yourself, so you always win! 🚀
Here is an example
Enjoy, and have fun, if you have any questions do not hesitate to ask
Pip Counter with AlertsThis script can be used to count number of pips on a candle , use settings to change precision(decimals) , look back candles and alerts for number of pips change and percentage change on a candle. This works for only forex. Mention any suggestions or improvements in the comments. Hope it is useful for you all .
CE - Market Performance TableThe 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is a sophisticated market tool designed to provide valuable insights into the current market trends and the approximate current position in the Macroeconomic Regime.
Furthermore the 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 provides the Correlation Implied Trend for the Asset on the Chart. Lastly it provides information about current "RISK ON" or "RISK OFF" periods.
Methodology:
𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 tracks the 15 underlying Stock ETF's to identify their performance and puts the combined performances together to visualize 42MACRO's GRID Equity Model.
For this it uses the below ETF's:
Dividends (SPHD)
Low Beta (SPLV)
Quality (QUAL)
Defensives (DEF)
Growth (IWF)
High Beta (SPHB)
Cyclicals (IYT, IWN)
Value (IWD)
Small Caps (IWM)
Mid Caps (IWR)
Mega Cap Growth (MGK)
Size (OEF)
Momentum (MTUM)
Large Caps (IWB)
Overall Settings:
The main time values you want to change are:
Correlation Length
- Defines the time horizon for the Correlation Table
ROC Period
- Defines the time horizon for the Performance Table
Normalization lookback
- Defines the time horizon for the Trend calculation of the ETF's
- For longer term Trends over weeks or months a length of 50 is usually pretty accurate
Visuals:
There is a variety of options to change the visual settings of what is being plotted and the two table positions and additional considerations.
Everything that is relevant in the underlying logic that can help comprehension can be visualized with these options.
Market Correlation:
The Market Correlation Table takes the Correlation of the above ETF's to the Asset on the Chart, it furthermore uses the Normalized KAMA Oscillator by IkkeOmar to analyse the current trend of every single ETF.
It then Implies a Correlation based on the Trend and the Correlation to give a probabilistically adjusted expectation for the future Chart Asset Movement. This is strengthened by taking the average of all Implied Trends.
With this the Correlation Table provides valuable insights about probabilistically likely Movement of the Asset, for Traders and Investors alike, over the defined time duration.
Market Performance:
𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is the actual valuable part of this Indicator.
It provides valuable information about the current market environment (whether it's risk on or risk off), the rough GRID models from 42MACRO and the actual market performance.
This allows you to obtain a deeper understanding of how the market works and makes it simple to identify the actual market direction.
Utility:
The 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is divided in 4 Sections which are the GRID regimes:
Economic Growth:
Goldilocks
Reflation
Economic Contraction:
Inflation
Deflation
Top 5 Equity Style Factors:
Are the values green for a specific Column? If so then the market reflects the corresponding GRID behavior.
Bottom 5 Equity Style Factors:
Are the values red for a specific Column? If so then the market reflects the corresponding GRID behavior.
So if we have Goldilocks as current regime we would see green values in the Top 5 Goldilocks Cells and red values in the Bottom 5 Goldilocks Cells.
You will find that Reflation will look similar, as it is also a sign of Economic Growth.
Same is the case for the two Contraction regimes.
Premium VWAP Trendfollow Strategy [wbburgin]This is a strongly-revised version of my VWAP Trendfollow Strategy, which follows a substantial reworking to address various structural inefficiencies with the script, such as the narrowing of the standard deviation band upon anchor reset. I will continue updating the original script with planned adjustments, this is a different proof-of-concept that builds off of the original script thesis with a different calculation method and execution.
This strategy is not built for any specific asset or timeframe, and has been backtested on crypto and equities from 1 min-1 day. The previous experimental strategy was heavily-correlated with the actual movement of the asset, which added unpalatable risk to the strategy and increased drawdown. This revised form has a more stable backtesting curve, but I want to heavily emphasize that I cannot guarantee that the strategy will be profitable for your circumstances. Backtesting only goes so far and every exchange has a different fee schedule, which can substantially eat into your profits. At the bottom I will explain the parameters behind the strategy results.
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The VWAP Trendfollow Strategy begins with a simple premise: to enter long when the price breaks above the upper standard deviation of a VWAP, and to close the position when the price breaks below the lower standard deviation of the VWAP. This is more effective than initiating the same strategy for a VWMA because the VWAP resets its anchor depending on your chosen anchor period, and the act of resetting its anchor also resets its standard deviation value. As a consequence, in sustained uptrends, the standard deviation is pulled upward to meet the price when the anchor resets, instead of requiring the price to fall all the way back down, as in the lower standard deviation band of the VWMA. This essentially acts as the VWAP itself raising the stop loss at each anchor period, which works well for the overall trend-following strategy.
However, this narrowing can still have consequences for a simple breakout strategy; as the price gradually oscillates towards above or below its standard deviation band, it may cross over the other and produce false signals. This oscillation is worrisome especially when fees are taken into account.
Thus, the premium VWAP Trendfollow strategy has a variable width which detects abnormal narrowing of the band, and adjusts it until it is reasonable to close the variability period. Additionally, a filter is added to the open/close signals to soften the frequency of signals without impacting performance significantly.
This script contains an ATR stop loss and an ATR take profit (which is also a difference between it and the original experimental script), with customizable inputs. The strategy results shown below are with initial capital of $1000, qty entry of 10%, and commissions of 0.06%. It works best on 24/7 instruments, like crypto, but I have found it also works with FAANG stocks or other high volatility / high volume assets. The issue with stocks, however, is that the price can jump/plummet because of abnormal events after-hours, which the strategy cannot pick up on until pre-trading begins the next morning. For that reason I suggest it be used on crypto and, because of its low % profitable (but high average winning trade in relation to its average losing trade), be used on an exchange that has minimal fees or volume-based discounts. In the unfortunate case that you cannot find a minimal fee or volume-discounted fee exchange (such as fellow Americans following the liquidity-retreat on Binance.US), I encourage you to test out the higher anchor periods for the higher timeframes, which will reduce the number of trades and increase the average % per trade.
Additionally, this is a long-term strategy used best for accumulation. It is currently long-only; that may change based off of user input.
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Disclaimer
Copyright by wbburgin.
The information contained in my Scripts/Indicators/Algorithms does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
