TTP Kent StratKent Strat trades breakouts using Bollinger Bands together with SuperTrend.
Features:
- Risk/reward ratio parameter
- Longs, shorts and combined positions.
- Breakout settings
- Trailing SL, trailing TP
- Use of latest candles to place the SL using a lookback parameter (how many candles to look back for a low/high price)
- Select your SL between the ATR trendline and the latest candle: the closest or furthest away value
- Show the trendline
- Backtest mode for accurate backtests
- Signal mode for live price accurate signals
- Date range backtesting
Filters:
- EMA 200 filter and timeframe selector. This filter can be used to trade with the trend: open longs on an uptrend and shorts on a downtrend.
- ADX filter using threshold. This filter can be used to filter entries where the trend is not very strong.
- ADX pointing up. ADX values pointing up and above certain threshold can improve entries.
- Relative volume filter based on the volume being X% above the MA of the Volume. Trading with volume can help filtering out bad trades.
Example setup:
1) pick BINANCE:ETHUSDT chart, 15 min chart
2) trade longs + shorts
3) pick ratio 3
4) trailing SL checked
5) trailing TP unchecked
7) stop loss "furthest"
8) candle loopback 30
9) BB period 21, dev 1, ATR filter on, atr period 5
10) EMA filter on, 15 min
11) ADX off
12) Volume filter on set to 60%
波動率
Double Bollinger-Bands w/ Alerts for BacktestingFor Mr. Nick and anyone else who stumbles across this.
STD-Filtered, Gaussian-Kernel-Weighted Moving Average BT [Loxx]STD-Filtered, Gaussian-Kernel-Weighted Moving Average BT is the backtest for the following indicator
Included:
This backtest uses a special implementation of ATR and ATR smoothing called "True Range Double" which is a range calculation that accounts for volatility skew.
You can set the backtest to 1-2 take profits with stop-loss
Signals can't exit on the same candle as the entry, this is coded in a way for 1-candle delay post entry
This should be coupled with the INDICATOR version linked above for the alerts and signals. Strategies won't paint the signal "L" or "S" until the entry actually happens, but indicators allow this, which is repainting on current candle, but this is an FYI if you want to get serious with Pinescript algorithmic botting
You can restrict the backtest by dates
It is advised that you understand what Heikin-Ashi candles do to strategies, the default settings for this backtest is NON Heikin-Ashi candles but you have the ability to change that in the source selection
This is a mathematically heavy, heavy-lifting strategy. Make sure you do your own research so you understand what is happening here.
STD-Filtered, Gaussian-Kernel-Weighted Moving Average is a moving average that weights price by using a Gaussian kernel function to calculate data points. This indicator also allows for filtering both source input price and output signal using a standard deviation filter.
Purpose
This purpose of this indicator is to take the concept of Kernel estimation and apply it in a way where instead of predicting past values, the weighted function predicts the current bar value at each bar to create a moving average that is suitable for trading. Normally this method is used to create an array of past estimators to model past data but this method is not useful for trading as the past values will repaint. This moving average does NOT repaint, however you much allow signals to close on the current bar before taking the signal. You can compare this to Nadaraya-Watson Estimator wherein they use Nadaraya-Watson estimator method with normalized kernel weighted function to model price.
What are Kernel Functions?
A kernel function is used as a weighing function to develop non-parametric regression model is discussed. In the beginning of the article, a brief discussion about properties of kernel functions and steps to build kernels around data points are presented.
Kernel Function
In non-parametric statistics, a kernel is a weighting function which satisfies the following properties.
A kernel function must be symmetrical. Mathematically this property can be expressed as K (-u) = K (+u). The symmetric property of kernel function enables its maximum value (max(K(u)) to lie in the middle of the curve.
The area under the curve of the function must be equal to one. Mathematically, this property is expressed as: integral −∞ + ∞ ∫ K(u)d(u) = 1
Value of kernel function can not be negative i.e. K(u) ≥ 0 for all −∞ < u < ∞.
Kernel Estimation
In this article, Gaussian kernel function is used to calculate kernels for the data points. The equation for Gaussian kernel is:
K(u) = (1 / sqrt(2pi)) * e^(-0.5 *(j / bw )^2)
Where xi is the observed data point. j is the value where kernel function is computed and bw is called the bandwidth. Bandwidth in kernel regression is called the smoothing parameter because it controls variance and bias in the output.
Capital Coders - Volume/Volatility StrategyThis strategy utilizes a combination of Volume and volatility to identify highly volatile candles, in periods of high and low volume trading. This allows the strategy to adjust to the market conditions. The Average True Range will highlight High Volatility breakouts, while the 24Hr Volume will determine weather the Trend will continue, or reverse.
Risk Management is an important part of any trading strategy. With this Strategy, you can leave your Risk calculator at home. Simply enter the maximum Risk per trade (% of your account) and let the Strategy determine your lot/position size.
Dynamically exit the market based on a single or multiple take profit levels. With simple configuration, this makes it easier than ever to dynamically exit the market.
Strategy includes the following features
Built in Risk Management
Variable Take Profit Levels
Built in Strategy Alerts
Signal Customization
scalping with market facilitationThis strategy is for scalping low timeframes for 10 pips. I have yet to see a strategy with this unique combo of indicators.
First we have volume indicator market facilitation, where we are looking for volume and mfi to be up, then we want the adx 5 to be above level 30 and above its ema period 3, then if these conditions are good we take shorts when ema 8 is below ema 100 and longs when ema8 is above ema 100 with parabolic sar in its propet place, also to verify trend we have obv over or under its ema of 55 and macd line over its signal line.
I have heikenashi bars on with the regular priceline showing so j see actual price levels, when i get a buy signal i set a buystop above the high of that bar and have a stoploss of 7.5 pips and a take profit of 10 pips, reverse for sells, i have to use metatrader to trade so i use this as my signals to trade.
Note this is not advice trade at your own risk no guarantees in anything in life, but i wanted to share this for it is helping me with my trades to be more strict and semi mechanical. I use it for forex time frames 1 3 5 15 mjn
J2S Backtest: Steven Primo`s Big Trend StrategyIs it possible to benefit from big trend moves? In this study I present you a strategy that aims to capture big trend moves.
Created by trader Steven Primo, The Big Trend strategy is advocates and shared through his YouTube channel without restrictions.
Note:
This is not an investment recommendation. The purpose of this study is only to share knowledge with the community on TradingView.
What is the purpose of the strategy?
The strategy focuses on capturing the movement of trends, providing an entry signal for both LONG and SHORT positions.
To which time-frame of a chart is it applicable to?
According to the author, it is applicable to any chart in different markets.
What about risk management?
The author does not establish a risk management model for strategy. This is left to the definition of each trader.
How are the trends identified in this strategy?
A 20-periods Bollinger Bands with 0.382 deviation should be plotted on the chart. Prices above the upper band indicate an uptrend, on the other hand, prices below the lower band indicate an downtrend. Finally, prices between the two bands indicate sideways trend.
How to identify a signal for LONG entry?
The signal is given after five consecutive closes above the upper Bollinger band. After that, you must enter the trade after the first trade occurs above the high of the signal bar.
How to identify a signal for SHORT entry?
The signal is given after five consecutive closes below the lower Bollinger band. After that, you must enter the trade after the first trade occurs below the low of the signal bar.
Tips and tricks
In my backtest, I tried to prove the strategy from a position trading perspective, so I proposed use fixed stop-loss and take-profits. The stop-loss is defined as being low of the first bar that generated the movement until the signal bar. The value range from the stop-loss to the signal bar is used in determining the profit target. Given any trade, position closing will be triggered when the bar trading limit is reached.
Backtest features
Backtest parameters are fully customizable, for instance: number of bars inside a trend indicating trend maturity for entry, bar limit for trading entry (after a buy or sell signals). Also, the user chooses to validate only LONG or SHORT entries, or both. It is also possible to determine the specific time period for running the backtests.
Final message
In my tests, I noticed excellent results for other crypto pairs, for example: ETH/USDT, BNB/USDT, FIL/USDT, GALA/USDT and ILV/USDT. Of course, no one strategy works perfectly for every asset, crypto, and bond out there. That's why we should explore each trading model and carry out our backtests. Please, feel free to provide me with any improvement suggestions for the backtest script. Bear in mind, feel free to use the ideas in my script in your studies.
Strength Volatility Killer - The Quant ScienceStrength Volatility Killer - The Quant Science™ is based on a special version of RSI (Relative Strength Index), created with the simple average and standard deviation.
DESCRIPTION
The algorithm analyses the market and opens positions following three different volatility entry conditions. Each entry has a specific and personal exit condition. The user can setting trailing stop loss from user interface.
USER INTERFACE SETTING
Configures the algorithm from the user interface.
AUTO TRADING COMPLIANT
With the user interface, the trader can easily set up this algorithm for automatic trading.
BACKTESTING INCLUDED
The trader can adjust the backtesting period of the strategy before putting it live. Analyze large periods such as years or months or focus on short-term periods.
NO LIMIT TIMEFRAME
This algorithm can be used on all timeframes.
GENERAL FEATURES
Multi-strategy: the algorithm can apply long strategy or short strategy.
Built-in alerts: the algorithm contains alerts that can be customized from the user interface.
Integrated indicator: indicator is included.
Backtesting included: quickly automatic backtesting of the strategy.
Auto-trading compliant: functions for auto trading are included.
ABOUT BACKTESTING
Backtesting refers to the period 13 June 2022 - today, ticker: AVAX/USDT, timeframe 5 minutes.
Initial capital: $1000.00
Commission per trade: 0.03%
Longbithello Traders !
Inspired by the indicator Range Filter Buy and Sell 5min by guikroth , namely calculation and works price movement range filter .
And Inspired by the indicator by the indicator VuManChu Cipher B + Divergences by vumanchu namely calculation and working out divergences and convergences , i was inspired to create a strategy .
This is indicator - strategy - ( Longbit ) - aggregate and my modification indicators : Range Filter Buy and Sell 5min by guikroth , VuManChu Cipher B + Divergences by vumanchu , and diferent exponential moving average .
The strategy - ( Longbit ) works on the basis of the price movement range filter , first a smooth average price range is calculated for the basis of the filter and multiplied by a specified amount by indicator Range Filter Buy and Sell 5min by guikroth , and some calculations working out divergences and convergences by indicator VuManChu Cipher B + Divergences by vumanchu . And diferent exponential moving averages for zones Bull / Bear trend for trend trading , and using take and diferent stop loss : algo , user or percentage .
Thus, using my strategy, we get the best entry point to open trades after confirming divergences
example in the picture
or
And when these trend signals are much stronger
example in the picture
Actual Version recommended used for BTCUSDC / BTCUSDT 4 hours time frame and used default settings and stop loss - algo , take profit 6 %
example in the picture
But it can also be used on all time frame with these settings: MaxMin3 Data Sampling period 6 Range Multiplier 2 and Take profit 2.3 % , stop loss algo
DISCLAIMER: This informational planning script / strategy is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. I am not responsible for any losses you may incur.
Привет, Трейдеры ! Вдохновленный индикатором Range Filter Buy and Sell 5min by guikroth , а именно работой и вычеслением диапазона фильтра движения цены . А также вдохновленный индикатором VuManChu Cipher B + Divergences by vumanchu, а именно дивергенций и конвергенций , я был вдохновлен создать эту стратегию
Эта тратегия - (Longbit) - это совокупность индикаторов : Range Filter Buy and Sell 5min by guikroth , VuManChu Cipher B + Divergences by vumanchu, и разние виды экспоненциальных средних .
Стратегия - (Longbit) работает на основе фильтра диапазона изменения цен, сначала рассчитывается гладкий средней диапазон цен для базы фильтра и умножается на оговоренную сумму по индикатору Range Filter Buy and Sell 5min by guikroth модифицированой версии , а также некоторые расчеты вычислений дивергенций и конвергенции по показателю индикатора VuManChu Cipher B+ Divergences by vumanchu. И различные экспоненциальные среднее и их зон для определения тенденции либо трендов для торговли по тренду , и с использованием стоп лоса : алго, пользовательский или процентный .
Таким образом, используя мою стратегию, мы получим лучшую точку для открытия сделок после подтверждения дивергенций или конвергенций
А когда эти сигналы по тренду они намного сильнее на рисунке , примеры на картинках выше
Рекомендуемую для 4-часового тм BTCUSDC/BTCUSDT , и работать с помощью algo stop loss , и take в 6 % с настройками по умолчанию , но можно также на использовать на всех тм с такой вот настройкой :
Данные МакМин3
Sampling period 6
Range Multiplier 2
Примеры на картинках выше
Предупреждаю : Эта стратегия информационного планирования предназначен исключительно для индивидуального пользования и образовательных целей. Это не финансовая или инвестиционная консультация. Инвестиции всегда осуществляются на собственном риске и основываются на вашем личном суждении. Я не отвечаю за потери, которые вы можете понести.
Mean reversal QFL v3My aim is to make the bots trade as you would trading QFL manually and “by the book” or at least to my experience and understanding from the material out there of how you should plan a QFL trade.
Im absolutely not a pro trader, I have made my share of costly mistakes trying to be clever or Beeing impatient resulting in painful losses. QFL is we’re I’ve had consistently good results tough.
Is this where I have to say I’m not a financial advisor and all that? Well I’m not. As always Do your own research and backtest, backtest, backtest.
First: I believe no bot strategy are set and forget, while they can run unattended 80-90% of the time you're always going to find yourself in a situation where you will have to manually handle a bad deal. It would also make sense to be somewhat involved in the really good trades making the most out of them. That’s why understanding the strategy the bot Is using is really important, hence why I prefer QFL. It's an easy concept to understand, and proved to be a safe way of making steady profit in pretty much all market conditions if done right.
Some changes in how aggressive you are might be needed if you are the impatient kind of trader who needs to see a lot of deals happening. But it is an added risk. In those cases Luc would advise to start “nibbling” but that would be hard to implement in a bot but I will see if that’s something I can implement.
Same goes for going the more conservative route when market conditions calls for it.
QFL stands for Quickfingersluc, and sometimes it is referred to as the Base Strategy or Mean Reversals. Its main idea is about identifying the moment of panic selling and buying below the base level and utilizing Safety orders.
Base level or Support Level refers to the lowest price level that was reached before the moment the price started increasing again. At that level, you can notice that buyers of some cryptocurrencies make a strong reaction.
As a bit of a learning material i want to make a few points on important factors in trading using the QFL strategy:
• Identify strong bases
• Read the history of the chart
• No emotions
Trading QFL using a bot has it’s limitations:
· Some of the bases are questionable but im constantly trying to improve this
· The strategy don’t take into consideration chart history(success rate)*
· You need to follow a predefined (by you) buying ladder, hence not considering a particular coin's average price movement, which may vary quite a lot. This why I for now has limited the strategy to SIMPLE bots. So that unique alerts can be created for each pair.
· A set Take profit %, possibly making you miss out on higher profits(This is easy to change during a trade though), and no chance of selling in layers(This is coming soon).
1. Some of the bases are questionable
The strategy will start trades of bases that you wouldn’t consider being a strong base(or a base at all) when looking at the chart.
For those not as familiar with QFL. What is a base, and what qualifies as a strong base?
• A base is also called the Support Level, which is the lowest price level that was reached before the price started turning and increasing again.
• A strong base is recognized by a steep fall in price after breaking the base(Panic), followed by a big reaction pump.
• The reaction pump is the most important factor to say that it is a strong base.
• And also the last base, the one you are trading of is the one that counts
Tip: Look for V shapes on the chart, easy to spot when zoomed out.
2. The integrated signals don’t take into consideration chart history(success rate)*
How can you assess the success rate by looking at the chart?
After finding the bases based on the criterias from the 1st point. Looking at the, how many times did it respect the base after breaking it? 7/10, 8/10, 9/10 times? Great! Chances of the next trade also respecting the base is big, and I would consider raising the TP on that deal. Any lower than that I would keep a really close eye on the deal, or even consider closing the deal. And again remember the last base is the one that counts. If all the others are nice strong bases but that last one you are about to take a trade off is no good the base is invalidated so be cautious.
3. You need to follow a predefined (by you) buying ladder
Crypto is volatile, and there is a huge variation in price movements on all the coins.
Trading manually, looking at the chart gives you a good idea on how much a coin on avg. drops below base, and how big the following reaction is. This gives you an indication on how deep you need to set your layers, and where you can take profit.
Using the strategy you have the backtester to see how much max deviation has been in the past so that you can figure out what the optimal max deviation is.
4. A set Take profit %, possibly making you miss out on higher profits(This is easy to change during a trade though), and no chance of selling in layers.
Not going to say to much about this other than what I often do is:
When a bot has started a trade I usually take a look at the chart. If I like what I see, nice chart history, success rate and trading of a strong previous base etc, with the current base break resulting in a panic drop I will consider increasing the TP so that it will make more profit. This can be a bit risky but also very rewarding. Imagine filling all safeties and then selling just below base! Massive profits!! (Gotta be honest though, almost never stretch it that far with a bot though, but it is a possibility) .
If you have studied the chart and concluded that this particular trade has a 90% chance of success, there isn’t really any reason not to place TP just below base. This is where I would like to have the option of layering my sell orders as well so its something im working on implementing.
Trailing is an option in 3commas, but it’s slow to place orders making you miss a selling opportunity when the coin makes a sudden spike up.
ABOUT THIS STRATEGY
In this strategy we can also reverse the strategy and go short. But i must warn you that that is alot riskier.
QFL is meant to be used on higher TF's like 1hr, 2hr and 4hr. But this strategy also work well on lower Timeframes.
The script also simulates DCA strategy with parameters used in 3commas DCA bots for futures trading.
Experiment with parameters to find your trading setup.
Beware how large your total leveraged position is and how far can market go before you get liquidated!
Do that with the help of futures liquidation calculators you can find online!
Included:
An internal average price and profit calculating, instead of TV`s native one, which is subject to severe slippage.
A graphic interface, so levels are clearly visible and back-test analyzing made easier.
Long & Short direction of the strategy.
Table display a summary of past trades
Vertical colored lines appear when the new maximum deviation from the original price has
been reached
All the trading happens with total account capital, and all order sizes inputs are expressed in percent.
How to use:
- Add the script to the current chart
- Open the strategy settings
-Tweak the settings to to your liking.
-Make a SIMPLE bot in 3commas and use the same settings as you did in tradingview if you only want the strategy to send signals to open a deal and let 3commas handle the rest.
If you check safety orders, Take profit deal stop and Stop loss. The strategy will send all the orders to 3 commas. If that’s what you want set TP in 3commas to 50% set number of safety orders to 0 and keep stop loss unchecked.
- Insert bot details using the deal start condition message found in your 3commas bot.
- When happy, right click on the "..." next to the strategy name, then "Add alert'".
- Under "Condition", on the second line, chose "Any alert () function call". Add the webhook from 3commas( 3commas.io ), give it a name, use {{strategy.order.alert_message}} as a placeholder message and "create".
In the future this signal might make it to the 3commas marketplace. You can then subscribe to that signal where I have cherrypicked coins based on thorough backtesting and optimization.
How to obtain access to the script: send me a private message in Tradingview
3c Ultimate reversal strategy With scanner and backtester v2This might just be the ultimate strategy to identify reversals.
This strategy includes a scanner, a backtester and ability to connect it with you 3 commas bot(See adviced settings below)
Strategy:
-Signals reversal that happened in the last bar. This signal DO NOT repaint.
-Identifies potential reversal that might happen in the current bar but can also not happen depending upon the timeframe closing price.
-The strategy combines the Moving Average Trend Changer, SuperTrend (ATR price detection) and ADX.
-It reduces the number of false signals in sideways market conditons and give more reliable trade signals.
-The signal does not repaint and can be used in any market condition. It determines the trend with high precision.
Take profit:
-Set 2 separate TP conditions.
-You can take profit using percentage, ATR, or RR(Risk Reward), aswell as using Trailing Take Profit.
- Use sell signal from the strategy(I often find way better results using that)
Stoploss:
-You can use either ATR, Percentage or sell signal from the strategy
(For now to let the strategy itself decide when to TP or SL, just set these parameters really high.)
Scanner:
-Identifies coins that are currently in the sell zone
-Identifies coins that are currently in the buy zone
-Screener explores up to 20 pairs in current graph's time frame.
-Optimize the strategy to your liking and use the built in backtester to see if it is a viable strategy.
3commas settings:
-For now you can only use simple bots.
-Create LONG and SHORT bots for the coins you like to trade and set up alerts(You can send long and short signal from the same alert)
-Set TP to 50% the strategy will handle buys and exits based on your inputs.
-Set safety orders to 0. I might add DCA to the strategy if testing proves that to be a good solution.
-When you have made the bots input the bot ID and token adress in the settings of the strategy.
-When creating the alert use this webhook :https://3commas.io/trade_signal/trading_view
-In the message field you use {{strategy.order.alert_message}} as the placeholder.
In the future this signal might make it to the 3commas marketplace. You can then subscribe to that signal where I have cherrypicked coins based on thorough backtesting and optimization.
Mean Reverse Grid Algorithm - The Quant ScienceMean Reverse Grid Algorithm - The Quant Science™ is a dynamic grid algorithm that follows the trend and run a mean reverting strategy on average percentage yield variation.
DESCRIPTION
Trades on different price levels of the grid, following the trend. The grid consists of 10 levels, 5 higher and 5 lower. The grids together create a channel, this channel represents the total percentage change where the algorithm works. The channel also represents the average change yields of the asset, identified during analysis with the "Yield Trend Indicator".
The algorithm can be set long or short.
1. Long algorithm: opens long positions with 20% of the capital every time the price crossunder a lower grid, for a maximum total of 5 simultaneous trades. Trades are closed each time the price crossover a higher grid.
2. Short algorithm: opens short positions with 20% of the capital every time the price crossover a higher grid, for a maximum total of 5 simultaneous trades. Trades are closed each time the price crossunder a lower grid.
USER INTERFACE SETTING
The user configures the percentage value of each grid from the user interface.
AUTO TRADING COMPLIANT
With the user interface, the trader can easily set up this algorithm for automatic trading. Automating it is very simple, activate the alert functions and enter the links generated by your broker.
BACKTESTING INCLUDED
With the user interface, the trader can adjust the backtesting period of the strategy before putting it live. You can analyze large periods such as years or months or focus on short-term periods.
NO LIMIT TIMEFRAME
This algorithm can be used on all timeframes and is ideal for lower timeframes.
GENERAL FEATURES
Multi-strategy: the algorithm can apply either the long strategy or the short strategy.
Built-in alerts: the algorithm contains alerts that can be customized from the user interface.
Integrated grid: the grid indicator is included.
Backtesting included: automatic backtesting of the strategy is generated based on the values set.
Auto-trading compliant: functions for auto trading are included.
ABOUT BACKTESTING
Backtesting refers to the period 1 August 2022 - today, ticker: ETH/USDT, timeframe 1H.
Initial capital: $1000.00
Commission per trade: 0.03%
AlphaTrend StrategyThe strategy version of AlphaTrend indicator for backtesting and optimisation purposes for TradingView users:
Screener / Explorer version and an English explantion video will be published soon...
AlphaTrend's parameters are designed for daily and 4H charts, feel free to optimize on all time frames.
Hope you all use Alphatrend in your profitable trades.
Kıvanç
Swing Trend StrategyThis script is a trend following system which uses a long term Moving Average to spot the trend in combination with the Average True Range to filter out Fakeouts, limiting the overall drawdown.
Default Settings and Calculation:
- The trend is detected using the Exponential Moving Average on 200 periods.
- The Average True Range is calculated on 10 periods.
- The Market is considered in an Uptrend when the price closes above the EMA + ATR.
- The Market is considered in a Downtrend when the price closes below the EMA - ATR.
- The strategy will open a LONG position when the market is in an Uptrend.
- The strategy will close its LONG positions when the price closes below the EMA.
- The strategy will open a SHORT position when the market is in a Downtrend.
- The strategy will close its SHORT positions when the price closes above the EMA.
This script is best suited for the 4h timeframe, and shows good results on BTC and ETH especially.
The options allow to modify the type of moving average to use, the period of the moving average, the ATR multiplier to add as well as the possibility to open short trades or not.
Trade HourThis script is just finds the best hour to buy and sell hour in a day by checking chart movements in past
For example if the red line is on the 0.63 on BTC/USDT chart it mean the start of 12AM hour on a day is the best hour to buy (all based on
It's just for 1 hour time-frame but you can test it on other charts.
IMPORTANT: You can change time Zone in strategy settings.to get the real hours as your location timezone
IMPORTANT: Its for now just for BTC/USDT but you can optimize and test for other charts...
IMPORTANT: A green and red background color calculated for show the user the best places of buy and sell (green : positive signal, red: negative signals)
settings :
timezone : We choice a time frame for our indicator as our geo location
source : A source to calculate rate of change for it
Time Period : Time period of ROC indicator
About Calculations:
1- We first get a plot that just showing the present hour as a zigzag plot
2- So we use an indicator ( Rate of change ) to calculate chart movements as positive and negative numbers. I tested ROC is the best indicator but you can test close-open or real indicator or etc as indicator.
3 - for observe effects of all previous data we should indicator_cum that just a full sum of indicator values.
4- now we need to split this effects to hours and find out which hour is the best place to buy and which is the best for sell. Ok we should just calculate multiple of hour*indicator and get complete sum of it so:
5- we will divide this number to indicator_cum : (indicator_mul_hour_cum) / indicator_cum
6- Now we have the best hour to buy! and for best sell we should just reverse the ROC indicator and recalculate the best hour for it!
7- A green and red background color calculated for show the user the best places of buy and sell that dynamically changing with observing green and red plots(green : positive signal, red: negative signals) when green plot on 15 so each day on hour 15 the background of strategy indicator will change to 15 and if its go upper after some days and reached to 16 the background green color will move to 16 dynamically.
Gap Stats v2This helpful analysis tool displays a table of days a stock has gapped, how much it gapped by and what percentage of the gap was covered.
It is meant to be used on the daily timeframe and can help you distinguish if gaps are going to be filled or not. I like to use it mainly on medium + cap stocks
that have gapped overnight or pre-market and see what the tendency is with how it behaves after a major gap.
Strategy - Trend Chaser - PSeTrend Chaser for Philippine Stock Exchange - LONG ONLY
-Elvin Kennedy Latayan 2022
Bollinger Bands and RSI Short Selling (by Coinrule)The Bollinger Bands are among the most famous and widely used indicators. A Bollinger Band is a technical analysis tool defined by a set of trendlines plotted two standard deviations (positively and negatively) away from a simple moving average ( SMA ) of a security's price, but which can be adjusted to user preferences. They can suggest when an asset is oversold or overbought in the short term, thus provide the best time for buying and selling it.
The relative strength index ( RSI ) is a momentum indicator used in technical analysis . RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security. The RSI can do more than point to overbought and oversold securities. It can also indicate securities that may be primed for a trend reversal or corrective pullback in price. It can signal when to buy and sell. Traditionally, an RSI reading of 70 or above indicates an overbought situation. A reading of 30 or below indicates an oversold condition.
The short order is placed on assets that present strong momentum when it's more likely that it is about to decrease further. The rule strategy places and closes the order when the following conditions are met:
ENTRY
The closing price is greater than the upper standard deviation of the Bollinger Bands
The RSI is less than 70
EXIT
The trade is closed in profit when the RSI is less than 70
Upper standard deviation of the Bollinger Band is greater than the the closing price.
This strategy comes with a stop loss and a take profit, and as you can see by the results, it is well suited for a bear market.
This trade works very well with ETH (1h timeframe), AVA (4h timeframe), and SOL (3h timeframe) and is backtested from the 1 December 2021 to capture how this strategy would perform in a bear market.
To make the results more realistic, the strategy assumes each order to trade 30% of the available capital. A trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange.
LudovicaLudovica is a trend following strategy that works on intraday timeframes (15 minutes).
The stop loss is decided based on the last price movement, take profits are projected through a Fibonacci extension. Two different extensions are calculated, based on a filter that affects the last price movement it is chosen which one to use for take profits.
Money management is fixed fractional: regardless of the distance between entry point and stop loss, the risk on capital for each trade is decided by the user in the strategy inputs. Take profits from 1 to 4 plan to exit with 15% of the initial size, TPs from 5 to 8 plan to exit with 10% of initial size.
There is a trailing stop system to reduce the drawdown of the strategy (note that stop loss moves as the trade develops).
Take profits are limit orders, stops (loss or trailing) occur at candlestick close (set alerts on the strategy).
Optimized strategies selectable from input panel:
-ETHUSDTPERP 15 min
-CRVUSDTPERP 15 min v1
-CRVUSDTPERP 15 min v2
-SNXUSDTPERP 15 min
Other pairs in development and soon available.
This strategy is in beta stage.
Strategy Myth-Busting #1 - UT Bot+STC+Hull [MYN]This is part of a new series we are calling "Strategy Myth-Busting" where we take open public manual trading strategies and automate them. The goal is to not only validate the authenticity of the claims but to provide an automated version for traders who wish to trade autonomously.
Our first one is an automated version of the " The ULTIMATE Scalping Trading Strategy for 2022 " strategy from " My Trading Journey " who claims to have achieved not only profits but a 98.3% win rate. As you can see from the backtest results below, I was unable to substantiate anything close to that that claim on the same symbol (NVDA), timeframe (5m) with identical instrument settings that " My Trading Journey " was demonstrating with. Strategy Busted.
If you know of or have a strategy you want to see myth-busted or just have an idea for one, please feel free to message me.
This strategy uses a combination of 3 open-source public indicators:
UT Bot Alerts by QuantNomad
STC Indicator - A Better MACD By shayankm
Basic Hull Ma Pack tinkered by InSilico
Trading Rules:
5 min candles
Long
New Buy Signal from UT Bot Alerts Strategy
STC is green and below 25 and rising
Hull Suite is green
Short
New Sell Signal from UT Bot Alerts Strategy
STC is red and above 75 and falling
Hull Suite is red
[MT Trader] Backtest template w/ Supertrend Strategy---EN: In this strategy template you will find some functions already pre-programmed to be used in your strategies to speed up the programming process, among them we can highlight the default stop loss and take profit functions, which will help to set easily and quickly, defining the price range in which we want to prevent large losses or protect our profits from unexpected market movements.
🔴 Stop Loss: Among the functions of the stop loss are the 4 most known, first we have the fixed percentage range (%) and price ($), when the price reaches this fixed price will limit the losses of the operation avoiding larger losses, then we have the average true range (ATR), a moving average of true range and X period that can give us good reference points to place our stop loss, finally the last point higher or lower is the most used by traders to place their stop loss.
In addition, the price range between the entry and stop loss can be converted into a trailing stop loss.
🟢 Take Profit: We have 3 options for take profit, just like stop loss, the fixed range of percentage(%) and price($), are available, in addition to this we have the 1:# ratio option, which multiplies by X number the range between the entry and stop loss to use it as take profit, perfect for strategies that use ATR or last high/low point for their strategy.
📈 Heikin Ashi Entrys: The heikin ashi entries are trades that are calculated based on heikin ashi candles but their price is executed in Japanese candles, thus avoiding the false results that occur in heikin candlestick charts, making that in certain cases better results are obtained in the strategies that are executed with this option compared to Japanese candlesticks.
📊 Dashboard: A more visual and organized way to see the results and data needed for our strategy.
Feel free to use this template to program your own strategies, if you find bugs or want to request a new feature let me know in the comments or through my telegram @hvert_mt
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---ES: En esta plantilla de estrategia podrás encontrar algunas funciones ya pre-programadas para ser usadas en tus estrategias para acelerar procesos de programación, entre ellas podemos destacar las funciones por defecto de stop loss y take profit, que ayudaran a establecer de manera fácil y rápida, definiendo los rango de precio en los que queremos prevenirnos de perdidas grandes o proteger nuestras ganancias de movimientos inesperados del mercado.
🔴 Stop Loss: Entre las funciones del stop loss están las 4 más conocidas, en primer lugar tenemos el rango de porcentaje fijo(%) y el precio($), cuando el precio alcance este precio fijo se limitaran las perdidas de la operación evitando perdidas mas grandes, después tenemos el promedio de rango verdadero(ATR), una media móvil del rango verdadero y X periodo que nos puede dar buenos puntos de referencia para colocar nuestro stop loss, por ultimo el ultimo punto mas alto o mas bajo es de los mas usados por los traders para colocar su stop loss.
Adicional a esto, el rango de precio entre la entrada y el stop loss se puede convertir en un trailing stop loss.
🟢 Take Profit: Tenemos 3 opciones para take profit, al igual que en el stop loss, el rango fijo de porcentaje(%) y precio($) se encuentran disponibles, adicional a esto tenemos la opción de ratio 1:#, que multiplica por X numero el rango entre la entrada y el stop loss para usarlo como take profit, perfecto para estrategias que usen ATR o ultimo punto alto/bajo.
📈 Entradas Heikin Ashi: Las entradas Heikin Ashi son trades que son calculados en base a las velas Aeikin Ashi pero su precio esta ejecutado a velas japonesas, evitando así los falsos resultados que se producen en graficas de velas Heikin, esto haciendo que en ciertos casos se obtengan mejores resultados en las estrategias que son ejecutadas con esta opción en comparación con las velas japonesas.
📊 Panel de Control: Una manera mas visual y organizada de ver los resultados y datos necesarios de nuestra estrategia.
Siéntete libre de usar esta plantilla para programar tus propias estrategias, si encuentras errores o quieres solicitar una nueva función házmelo saber en los comentarios o a través de mi Telegram: @hvert_mt
Squeeze Momentum Strategy [LazyBear] Buy Sell TP SL Alerts-Modified version of Squeeze Momentum Indicator by @LazyBear.
-Converted to version 5,
-Taken inspiration from @KivancOzbilgic for its buy sell calculations,
-Used @Bunghole strategy template with Take Profit, Stop Loss and Enable/Disable Toggles
-Added Custom Date Backtesting Module
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All credit goes to above
Problem with original version:
The original Squeeze Momentum Strategy did not have buy sell signals and there was alot of confusion as to when to enter and exit.
There was no proper strategy that would allow backtesting on which further analysis could be carried out.
There are 3 aspects this strategy:
1 ) Strategy Logic (easily toggleable from the dropdown menu from strategy settings)
- LazyBear (I have made this simple by using Kivanc technique of Momentums Moving Average Crossover, BUY when MA cross above signal line, SELL when crossdown signal line)
- Zero Crossover Line (BUY signal when crossover zero line, and SELL crossdown zero line)
2) Long Short TP and SL
- In strategies there is usually only 1 SL and 1 TP, and it is assumed that if a 2% SL giving a good profit %, then it would be best for both long and short. However this is not the case for many. Many markets/pairs, go down with much more speed then they go up with. Hence once we have a profitable backtesting setting, then we should start optimizing Long and Short SL's seperately. Once that is done, we should start optimizing for Long and Short TP's separately, starting with Longs first in both cases.
3) Enable and Disable Toggles of Long and Short Trades
- Many markets dont allow short trades, or are not suitable for short trades. In this case it would be much more feasible to disable "Short" Trading and see results of Long Only as a built in graphic view of backtestor provides a more easy to understand data feed as compared to the performance summary in which you have to review long and short profitability separately.
4) Custom Data Backtesting
- One of most crucial aspects while optimizing for backtesting is to check a strategies performance on uptrends, downtrend and sideways markets seperately as to understand the weak points of strategy.
- Once you enable custom date backtesting, you will see lines on the chart which can be dragged left right based on where you want to start and end the backtesting from and to.
Note:
- Not a financial advise
- Open to feedback, questions, improvements, errors etc.
- More info on how the squeeze momentum works visit LazyBear indicator link:
Happy Trading!
Cheers
M Tahreem Alam @mtahreemalam
Smoothed Heikin Ashi Trend on Chart - TraderHalai BACKTESTSmoothed Heikin Ashi Trend on chart - Backtest
This is a backtest of the Smoothed Heikin Ashi Trend indicator, which computes the reverse candle close price required to flip a Heikin Ashi trend from red to green and vice versa. The original indicator can be found in the scripts section of my profile.
This particular back test uses this indicator with a Trend following paradigm with a percentage-based stop loss.
Note, that backtesting performance is not always indicative of future performance, but it does provide some basis for further development and walk-forward / live testing.
Testing was performed on Bitcoin , as this is a primary target market for me to use this kind of strategy.
Sample Backtesting results as of 10th June 2022:
Backtesting parameters:
Position size: 10% of equity
Long stop: 1% below entry
Short stop: 1% above entry
Repainting: Off
Smoothing: SMA
Period: 10
8 Hour:
Number of Trades: 1046
Gross Return: 249.27 %
CAGR Return: 14.04 %
Max Drawdown: 7.9 %
Win percentage: 28.01 %
Profit Factor (Expectancy): 2.019
Average Loss: 0.33 %
Average Win: 1.69 %
Average Time for Loss: 1 day
Average Time for Win: 5.33 days
1 Day:
Number of Trades: 429
Gross Return: 458.4 %
CAGR Return: 15.76 %
Max Drawdown: 6.37 %
Profit Factor (Expectancy): 2.804
Average Loss: 0.8 %
Average Win: 7.2 %
Average Time for Loss: 3 days
Average Time for Win: 16 days
5 Day:
Number of Trades: 69
Gross Return: 1614.9 %
CAGR Return: 26.7 %
Max Drawdown: 5.7 %
Profit Factor (Expectancy): 10.451
Average Loss: 3.64 %
Average Win: 81.17 %
Average Time for Loss: 15 days
Average Time for Win: 85 days
Analysis:
The strategy is typical amongst trend following strategies with a less regular win rate, but where profits are more significant than losses. Most of the losses are in sideways, low volatility markets. This strategy performs better on higher timeframes, where it shows a positive expectancy of the strategy.
The average win was positively impacted by Bitcoin’s earlier smaller market cap, as the percentage wins earlier were higher.
Overall the strategy shows potential for further development and may be suitable for walk-forward testing and out of sample analysis to be considered for a demo trading account.
Note in an actual trading setup, you may wish to use this with volatility filters, combined with support resistance zones for a better setup.
As always, this post/indicator/strategy is not financial advice, and please do your due diligence before trading this live.
Original indicator links:
On chart version -
Oscillator version -
Update - 27/06/2022
Unfortunately, It appears that the original script had been taken down due to auto-moderation because of concerns with no slippage / commission. I have since adjusted the backtest, and re-uploaded to include the following to address these concerns, and show that I am genuinely trying to give back to the community and not mislead anyone:
1) Include commission of 0.1% - to match Binance's maker fees prior to moving to a fee-less model.
2) Include slippage of 10 ticks (This is a realistic slippage figure from searching online for most crypto exchanges)
3) Adjust account balance to 10,000 - since most of us are not millionaires.
The rest of the backtesting parameters are comparable to previous results:
Backtesting parameters:
Initial capital: 10000 dollars
Position size: 10% of equity
Long stop: 2% below entry
Short stop: 2% above entry
Repainting: Off
Smoothing: SMA
Period: 10
Slippage: 10 ticks
Commission: 0.1%
This script still remains to shows viability / profitablity on higher term timeframes (with slightly higher drawdown), and I have included the backtest report below to document my findings:
8 Hour:
Number of Trades: 1082
Gross Return: 233.02%
CAGR Return: 14.04 %
Max Drawdown: 7.9 %
Win percentage: 25.6%
Profit Factor (Expectancy): 1.627
Average Loss: 0.46 %
Average Win: 2.18 %
Average Time for Loss: 1.33 day
Average Time for Win: 7.33 days
Once again, please do your own research and due dillegence before trading this live. This post is for education and information purposes only, and should not be taken as financial advice.
STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones BT [Loxx]STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones BT is the backtest strategy for "STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones " seen below:
Included:
This backtest uses a special implementation of ATR and ATR smoothing called "True Range Double" which is a range calculation that accounts for volatility skew.
You can set the backtest to 1-2 take profits with stop-loss
Signals can't exit on the same candle as the entry, this is coded in a way for 1-candle delay post entry
This should be coupled with the INDICATOR version linked above for the alerts and signals. Strategies won't paint the signal "L" or "S" until the entry actually happens, but indicators allow this, which is repainting on current candle, but this is an FYI if you want to get serious with Pinescript algorithmic botting
You can restrict the backtest by dates
It is advised that you understand what Heikin-Ashi candles do to strategies, the default settings for this backtest is NON Heikin-Ashi candles but you have the ability to change that in the source selection
This is a mathematically heavy, heavy-lifting strategy with multi-layered adaptivity. Make sure you do your own research so you understand what is happening here. This can be used as its own trading system without any other oscillators, moving average baselines, or volatility/momentum confirmation indicators.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
What is R-squared Adaptive?
One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement.
The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average .
R-squared is used here to derive a T3 factor used to modify price before passing price through a six-pole non-linear Kalman filter.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
