[ADOL_]Trend Break Pro ARVIS BOTENG) Trend Break Pro Version : ARVIS BOT
It automatically draws trend lines and signals signals based on sophisticated standards.
It is an indicator that can be used as a tool for trading. from scalping
It was made as an all-round player that can even use the swing.
It contains all the signals of a breakout of the basic trend line,
By applying a new logic, signals that are faster than the breakout point of the basic trend line and
I made it to produce a better value while catching the signal of the press.
principle)
Basic trendline features:
It reflects the concepts of HH and LL.
Trendlines are created using the basic construction method, connecting highs and highs and lows and lows.
The 3 candles prior to the breakout of the trend line are the triggers to create a ready-to-prepare base signal.
What is HH? It is an abbreviation of Higher High, which means to raise the high.
What is LL? An abbreviation of Lower Low, which means to lower the low.
Features of the new core logic:
This is a quick way to find a reversal of a trend, taking into account candle patterns, volume, and moving averages.
The candle pattern that is reversed when the trading volume increases and then disappears is processed by subdividing and digitizing the pattern.
generate a signal. At the turning point of the trend, it triggers a long and short reversal signal.
For trading volume, obv was utilized. Overlaid elements allow you to find signals that are slightly faster than the trend line.
What is obv? OBV = An abbreviation of On Balance Volume. It is a secondary indicator showing the cumulative total of trading volume.
The background is a notation of the section where the trend is reversing.
option)
The degree of freedom to set options according to scalping, single stroke, swing, and bot implementation is given.
A volatility warning notification has been included. The function of TD setup is included.
You can set the range of candles as an option. The backtesting value is printed.
The backtesting value is calculated as the average of the opening and closing prices.
Volatility warnings are displayed in yellow.
TD Setup: Numbers floating on the candle, in ascending and descending order (=sequence) from 1 to 9.
The reason for writing up to 9 is based on statistics, and the more fundamental principle is the theory of the Fibonacci sequence.
The Fibonacci sequence is the number in the golden ratio that makes up nature, 1.1.2.3.5.8.13.21.34.55.89...
The sum of the nth and n+1th numbers becomes n+2th.
A note on odds of winning. Even if the win rate is 33%, if the profit-loss stop-loss ratio is 3 to 1, the profit exceeds 2, so cumulative profits are generated.
In other words, it is decided by considering both the win rate and profit/loss ratio, not just the win rate. The logic of a short stop loss and a long profit trend is reflected.
time frame)
Applicable to any timeframe.
Scalping: 1 minute, 3 minute, 5 minute / Danta: 15 minute, 1 hour / Swing: 4 hour, daily
Recommended time frame: 3, 15 minutes
alarm)
Various alert functions are available. based on the alert signal
When trading, various signals help to set specific conditions.
- Basic long and short alerts
- Volatility warning alert
- Filtered long and short alerts
- Long and short alerts occur Next peak alerts
- Uptrend line, downtrend line breakout alert
- Alerts about bot start and end when bot is running
- Filtered uptrend line, downtrend line breakout alert
- Alerts for rising and falling 1 setup and rising and falling 9 setups respectively
trading method)
1. For the trend line breakout signal, when the signal overlaps more than 3 times, the breakout probability increases.
It reflects the concept below.
One touch of support/resistance: The first touch sees longs at support and shorts at resistance.
2 touches of support/resistance line: Confirm the position once more in the stage of building support/resistance line (compacting). We see longs at support and shorts at resistance.
3 touches of the ground/resistance line: This is a section with a high possibility of both directions.
Support/resistance line 4 (over) touch: Used as a breakout reference line. When breaking through, short at support and long at resistance.
When the support/resistance line breaks through the closing price, support becomes resistance and resistance becomes support.
2. Trade support/resistance lines using the trend line breakout and signal as an important reference bar.
example)
3-1. Entry criteria/stop loss criteria (when trading hands and bots)
- entry criteria; Follow the signal.
- Stop loss criteria;
Use fixed stop loss: Set 1% fixed stop loss section from signal generation. (% is set individually)
Use Candle Stop Loss: Set a stop loss when the low or high point of the signal generating bar collapses.
Use flow stop loss: Set the stop loss considering the flow of the wave.
3-2. Entry criteria/stop loss criteria (in case of signal-based bot trading)
- Approach with a low magnification (more than 10 times is not recommended) to prevent the risk of liquidation of the largest drop,
There is an opposite signal after entering without setting stop loss separately.
4. Note
You are solely responsible for any trading decisions you make.
5. How to use
It is set to be available only to invited users. When invited,
Tap Add Indicator to Favorites at the bottom of the indicator.
If you click the indicator at the top of the chart screen and look at the left tab, there is a Favorites tab.
Add an indicator by clicking the indicator name in the Favorites tab.
KOR) Trend Break Pro버전 : ARVIS BOT ; 아비스 봇 입니다.
추세선을 자동으로 작도해주며, 정교화된 기준으로 시그널을
발생시켜 매매에 도구로써 활용가능한 지표입니다. 스캘핑부터
스윙까지 활용가능한 올라운드 플레이어로 만들어졌습니다.
기본 추세선 돌파의 시그널들을 모두 포함하고 있으며,
새로운 로직을 적용하여 기본 추세선 돌파시점보다 빠른 신호와
눌림의 신호까지 잡아내면서, 더 나은 값을 산출하도록 만들었습니다.
원리)
기본 추세선 기능 :
HH와 LL의 개념을 반영합니다.
추세선은 고점과 고점, 저점과 저점을 잇는 기본 작도 방법으로 만들어집니다.
추세선 돌파의 3개 이전 캔들부터 신호발생으로 준비를 기본 시그널을 만듭니다.
HH란 ? Higher High의 약자로 고점을 높인다는 의미입니다.
LL란? Lower Low의 약자로 저점을 낮춘다는 의미입니다.
새로운 핵심적인 로직의 기능 :
추세의 반전을 빠르게 찾기 위한 방법으로 캔들패턴, 거래량, 이평선을 고려하여 만들어졌습니다.
거래량이 증가하다가 소멸하는 시점에서 반전되는 캔들패턴을 세분화하고 수치화하여 가공된
신호를 발생시킵니다. 추세의 전환자리에서 롱과 숏의 전환 신호를 발생시키도록 합니다.
거래량에는 obv가 활용되었습니다. 중첩된 요소들을 통해 추세선보다 조금 더 빠른 신호를 찾을 수 있습니다.
obv란 ? OBV = On Blance Volume의 약자로 거래량의 누적합계를 나타내는 보조지표 입니다.
배경은 추세가 전환되는 구간의 표기입니다.
옵션)
스캘핑과 단타, 스윙, 봇의 구현에 맞게 옵션을 설정할 수 있는 자유도를 부여하였습니다.
변동성 경고 알림이 포함되었습니다. TD셋업의 기능이 포함되었습니다.
캔들의 범위를 옵션으로 설정할 수 있습니다. 백테스팅 값이 출력됩니다.
백테스팅 값은 시가와 종가의 평균값으로 산출됩니다.
변동성경고는 yellow 컬러로 표기됩니다.
TD셋업 : 캔들위에 플로팅 되는 숫자로 1~9까지의 오름차순, 내림차순 (=시퀀스)으로 구성됩니다.
9까지 쓰는 이유는 통계기반, 좀 더 근본적인 원리는 피보나치 수열의 이론이 반영되어 있습니다.
피보나치 수열이란 자연을 이루는 황금비율의 숫자로 1.1.2.3.5.8.13.21.34.55.89...
n번째와 n+1번째 숫자의 합이 n+2번째가 됩니다.
원리 예시)
승률에 관한 참고사항. 승률이 33퍼센트의 승률이어도 3대 1의 익절 손절 비율이면 이익이 2를 넘어가니까 누적수익이 발생합니다.
즉, 승률과 손익비를 모두 고려해서 결정하는 것이지, 승률만 봐서는 안됩니다.
손절은 짧게, 수익은 추세대로 길게의 로직이 반영되어 있습니다.
타임프레임)
모든 시간프레임에 적용 가능합니다.
스캘핑 : 1분봉, 3분봉, 5분봉 / 단타 : 15분봉, 1시간봉 / 스윙 : 4시간봉, 일봉
추천타임프레임 : 3, 15분봉
5분봉)
15분봉)
4시간봉)
알람)
다양한 얼러트 기능을 사용할 수 있습니다. 얼러트 신호를 기반으로
매매시 다양한 신호는 구체적 조건 설정에 도움이 됩니다.
- 기본 롱,숏 얼러트
- 변동성 경고 얼러트
- 필터링된 롱,숏 얼러트
- 롱,숏 얼러트 발생 다음봉 얼러트
- 상승추세선, 하락추세선 돌파 얼러트
- 봇구동시 봇의 시작과 종료에 관한 얼러트
- 필터링된 상승추세선, 하락추세선 돌파 얼러트
- 상승,하락1셋업과 상승,하락9셋업 각각에 대한 얼러트
매매방법)
1. 추세선 돌파신호는 신호가 3번이상 중첩될시 돌파확률이 상승합니다.
아래 개념을 반영합니다.
지지/저항선 1터치 : 첫번째 터치에는 지지선에서 롱을, 저항선에서 숏을 봅니다.
지지/저항선 2터치 : 지지/저항선 구축(다지기)의 단계로 한번 더 자리를 확인합니다. 지지선에서 롱을, 저항선에서 숏을 봅니다.
지/저항선의 3터치 : 양방향의 가능성이 높은 구간입니다.
지지/저항선4(이상)터치 : 돌파기준선으로 사용합니다. 돌파할 때, 지지선에서 숏을, 저항선에서 롱을 칩니다.
지지/저항선이 종가로 뚫리면 지지는 저항이 되고, 저항은 지지가 됩니다.
2. 추세선돌파와 시그널이 발생한 봉을 중요한 기준봉으로 활용하여 지지/저항선 매매를 합니다.
예시)
3-1. 진입기준/손절기준(손,봇 매매시)
- 진입기준; 시그널을 따릅니다.
- 손절기준;
고정손절가 이용 : 시그널 발생으로부터 1% 고정 손절가 구간을 설정합니다.(%는 개별로 설정)
캔들손절가 이용 : 시그널 발생봉의 저점이나 고점이 무너지면 손절을 설정합니다.
흐름손절가 이용 : 파동의 흐름을 고려하여 손절을 설정합니다.
3-2. 진입기준/손절기준(신호기반 봇 매매시)
- 저배율(10배이상은 권장하지 않습니다)로 접근해 최대낙폭의 청산위험을 예방하며,
손절가를 따로 설정하지 않고 진입후 반대신호가 뜰때마다 스위칭을 합니다.
4. 참고
귀하가 내리는 모든 거래 결정은 전적으로 귀하의 책임입니다.
5. 사용방법
초대된 사용자만 사용할 수 있도록 설정이 되어있습니다. 초대를 받을 경우,
지표 하단의 즐겨찾기에 인디케이터 넣기를 누릅니다.
차트화면 상단에 지표를 눌러서 왼쪽탭에 보면 즐겨찾기 탭이 있습니다.
즐겨찾기 탭에서 지표이름을 눌러서 지표를 추가합니다.
在脚本中搜索"stop loss"
Breakout Trend Trading Strategy - V1Strategy in nutshell:
This strategy is made to be used in daily time-frames. Works better on trending instruments where volume is available. Hence, this is more suitable for trending shares rather than currencies, commodities and indexes where volume data is either not present or not reliable.
Breakout signifies the continuation of trend. Hence, trade in the direction of breakouts. Breakouts are calculated based on high volume and price movement in a day. This will be combined with few other conditions to generate buy and sell signals along with stop and compound targets. Supertrend is used for trend bias. Our buy and sell targets do not directly depend on the bias. But, entry criteria in opposite trend is made much difficult than that of trend direction. Further explanation of method and input parameters are explained below.
Backtesting parameters :
Capital and position sizing : Capital and position sizing parameters are set to test investing 2000 wholly on certain stock without compounding.
Initial Capital : 2000
Order Size : 100% of equity
Pyramiding : 1
ExitOnSignal : If unchecked exit is triggered solely on trailing stop
Trade Direction : Long, Short or All. Short condition is riskier than long conditions and often results in losses as per my observation. On most of the stocks trending up, strategy will not generate any short signals. This is achieved by comparing yearly high lows to previous two years to decide whether to allow short or long entries.
allowImmediateCompound : Applicable only if compounding/pyramiding is enabled in trade. If checked allows to place compounding orders immediately. If unchecked, it waits for stopline to cross order price before placing next compound.
Display Mode :
Targets : Whenever breakout happens, show marker for upTarget and downTarget
TargetChannel : Show up target and downtarget as a channel
Target With Stop : Along with targets, show also stop levels for breakouts
Up Channel : Channel created from UpTarget and respective stops
Down Channel : Channel created from DownTarget and respective stops
ShowTrailingStop : Shows trailing stop and compound lines when there is a trading position.
ShowTargetLevels : Shows Buy Sell target levels along with stop and compound lines. Trades are done as market orders. Hence, target levels are displayed after strategy makes the trade. Since only one order allowed per side without compounding, target, stop and compound levels are shown sometimes even without trade being made. These can be considered as entry levels if there is no existing position.
ShowPreviousLevels : Shows previous buy/sell target levels. When enabled, layout can look messy.
StopMultiplyer: To Set trailing stop loss.
BacktestYears: Number of years to include in backtest
So far my test cases are:
Positive : AAPL, AMZN, TSLA, RUN, VRT, ASX:APT
Negative Test Cases: WPL, WHC, NHC, WOW, COL, NAB (All ASX stocks)
Special test case: WDI
Negative test cases still show losses in backtesting. I have attempted including many conditions to eliminate or reduce the loss. But, further efforts has resulted in reduction in profits in positive cases as well. Still experimenting. Will update whenever I find improvements. Comments and suggestions welcome :)
Tweezer Top & Tweezer Bottom Pattern (Expo)Tweezer Top/Bottom Pattern (Expo) indicator identifies real-time Tweezer Top and Tweezer Bottom in any market and in any timeframe. This is an enhanced version of the pattern which enables higher accuracy, and noise filtering. In addition to that, Tweezer Levels can be displayed which can be used in many different ways, for instance, as a stop loss level, or to confirm the signal.
The ideal Tweezer pattern includes that the wicks should be 100% the same length. However, that rarely happens. So in this enhanced version, the user can set the maximum change that is allowed between the wicks.
To enhance the signal accuracy a Bullish engulfing and Bearish engulfing filter can be toggled on. So a signal will only be displayed if an engulfing candle is in play.
HOW TO USE
Tweezer Top/Bottom should be used together with KEY market levels or with KEY supply/demand zones.
Enter Long: if a Tweezer Bottom signal occurs at a KEY Level or on a KEY supply/demand zone.
Target: The first target should be the nearest resistance level or supply zone. The second target should be the next resistance level or supply zone, and so on.
Enter Short: if a Tweezer Top signal occurs at a KEY Level or on a KEY supply/demand zone.
Target: The first target should be the nearest support level or demand zone. The second target should be the next support level or demand zone, and so on.
Stop Loss: The Tweezer levels can be used as a stop loss.
Remember: Use the indicator together with KEY levels or KEY Supply/Demand Zones.
INDICATOR IN ACTION
4 Hour chart
I hope you find this indicator useful , and please comment or contact me if you like the script or have any questions/suggestions for future improvements. Thanks!
I will continually work on this indicator, so please share your experience and feedback as it will enable me to make even better improvements. Thanks to everyone that has already contacted me regarding my scripts. Your feedback is valuable for future developments!
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Disclaimer
Copyright by Zeiierman.
The information contained in my scripts/indicators/ideas 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, 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.
My scripts/indicators/ideas are only for educational purposes!
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ACCESS THE INDICATOR
• Contact me on TradingView or use the links below
Grid Like StrategyIt is possible to use progressive position sizing in order to recover from past losses, a well-known position sizing system being the "martingale", which consists of doubling your position size after a loss, this allows you to recover any previous losses in a losing streak + winning an extra. This system has seen a lot of attention from the trading community (mostly from beginners), and many strategies have been designed around the martingale, one of them being "grid trading strategies".
While such strategies often shows promising results on paper, they are often subjects to many frictions during live trading that makes them totally unusable and dangerous to the trader. The motivations behind posting such a strategy isn't to glorify such systems, but rather to present the problems behind them, many users come to me with their ideas and glorious ways to make money, sometimes they present strategies using the martingale, and it is important to present the flaws of this methodology rather than blindly saying "you shouldn't use it".
Strategy Settings
Point determines the "grid" size and should be adjusted accordingly to the scale of the symbol you are applying the strategy to. Higher value would require larger price movements in order to trigger a trade, as such higher values will generate fewer trades.
The order size determines the number of contracts/shares to purchase.
The martingale multiplier determines the factor by which the position size is multiplied after a loss, using values higher to 2 will "squarify" your balance, while a value of 1 would use a constant position sizing.
Finally, the anti-martingale parameter determines whether the strategy uses a reverse martingale or not, if set to true then the position size is multiplied after any wins.
The Grid
Grid strategies are commons and do not present huge problems until we use certain position sizing methods such as the martingale. A martingale is extremely sensitive to any kind of friction (frictional costs, slippage...etc), the grid strategy aims to provide a stable and simple environment where a martingale might possibly behave well.
The goal of a simple grid strategy is to go long once the price crossover a certain level, a take profit is set at the level above the current one and stop loss is placed at the level below the current one, in a winning scenario the price reach the take profit, the position is closed and a new one is opened with the same setup. In a losing scenario, the price reaches the stop loss level, the position is closed and a short one is opened, the take profit is set at the level below the current one, and a stop loss is set at the level above the current one. Note that all levels are equally spaced.
It follows from this strategy that wins and losses should be constant over time, as such our balance would evolve in a linear fashion. This is a great setup for a martingale, as we are theoretically assured to recover all the looses in a losing streak.
Martingale - Exponential Decays - Risk/Reward
By using a martingale we double our position size (exposure) each time we lose a trade, if we look at our balance when using a martingale we see significant drawdowns, with our balance peaking down significantly. The martingale sequence is subject to exponential growth, as such using a martingale makes our balance exposed to exponential decays, that's really bad, we could basically lose all the initially invested capital in a short amount of time, it follows from this that the theoretical success of a martingale is determined by what is the maximum losing streak you can endure
Now consider how a martingale affects our risk-reward ratio, assuming unity position sizing our martingale sequence can be described by 2^(x-1) , using this formula we would get the amount of shares/contracts we need to purchase at the x trade of a losing streak, we would need to purchase 256 contracts in order to recover from a losing streak of size 9, this is enormous when you take into account that your wins are way smaller, the risk-reward ratio is totally unfair.
Of course, some users might think that a losing streak of size 9 is pretty unlikely, if the probability of winning and losing are both equal to 0.5, then the probability of 9 consecutive losses is equal to 0.5^9 , there are approximately 0.2% of chance of having such large losing streak, note however that under a ranging market such case scenario could happen, but we will see later that the length of a losing streak is not the only problem.
Other Problems
Having a capital large enough to tank 9any number of consecutive losses is not the only thing one should focus on, as we have to take into account market prices and trading dynamics, that's where the ugly part start.
Our first problem is frictional costs, one example being the spread, but this is a common problem for any strategy, however here a martingale is extra sensitive to it, if the strategy does not account for it then we will still double our positions costs but we might not recover all the losses of a losing streak, instead we would be recovering only a proportion of it, under such scenario you would be certain to lose over time.
Another problem are gaps, market price might open under a stop-loss without triggering it, and this is a big no-no.
Equity of the strategy on AMD, in a desired scenario the equity at the second arrow should have been at a higher position than the equity at the first arrow.
In order for the strategy to be more effective, we would need to trade a market that does not close, such as the cryptocurrency market. Finally, we might be affected by slippage, altho only extreme values might drastically affect our balance.
The Anti Martingale
The strategy lets you use an anti-martingale, which double the position size after a win instead of a loss, the goal here is not to recover from a losing strike but instead to profit from a potential winning streak.
Here we are exposing your balance to exponential gross but you might also lose a trade at the end a winning streak, you will generally want to reinitialize your position size after a few wins instead of waiting for the end of a streak.
Alternative
You can use other-kind of progressions for position sizing, such as a linear one, increasing your position size by a constant number each time you lose. More gentle progressions will recover a proportion of your losses in a losing streak.
You can also simulate the effect of a martingale without doubling your position size by doubling your target profit, if for example you have a 10$ profit-target/stop-loss and lose a trade, you can use a 20$ profit target to recover from the lost trade + gain a profit of 10$. While this approach does not introduce exponential decay in your balance, you are betting on the market reaching your take profits, considering the fact that you are doubling their size you are expecting market volatility to increase drastically over time, as such this approach would not be extremely effective for high losing streak.
Conclusion
You will see a lot of auto-trading strategies that are based on a grid approach, they might even use a martingale. While the backtests will look appealing, you should think twice before using such kind of strategy, remember that frictional costs will be a huge challenge for the strategy, and that it assumes that the trader has an important initial capital. We have also seen that the risk/reward ratio is theoretically the worst you can have on a strategy, having a low reward and a high risk. This does not mean that progressive position sizing is bad, but it should not be pushed to the extreme.
It is nice to note that the martingale is originally a betting system designed for casino games, which unlike trading are not subject to frictional costs, but even casino players don't use it, so why would you?
Thx for reading
Two Take Profit StrategyThis script is for research purposes only. I am not a financial advisor.
Entry Condition
This strategy is based on two take profit targets and scaling out strategy. The entry rule is very simple. Whenever the EMA crossover WMA, the long trade is taken and vice versa.
Take Profit and Stop Loss
The first take profit is set at 20 pips above the long entry and the second take profit is set at 40 pips above the long entry. Meanwhile, the stop loss is set at 20 pips below the long entry.
Money Management
When the first take profit is achieved, half of the position is closed. The rest of the position is open to achieve either second take profit or stop loss.
There are three outcomes when using this strategy. Let's say you enter the trade with 200 lot size and you are risking 2% of your equity.
1. The first outcome is when the price hits stop loss, you lose the entire 2%.
2. The second outcome is when the price hits the first take profit and you close half of your position. Meaning that you have gained 1%. Then you let the trade running and eventually it hits stop loss. The total loss is 0% because the remaining lot size which is 200/2=100 times by 20pips is 1%. You have gained the earlier 1% and then loss 1%. At this point, you are at break even.
3. The third outcome is similar to the second out but instead of hiring stop loss, the trade is running to your favor and hits the second take profit.
Therefore, you gained 1% from the first take profit and you gained another 2% for the second take profit. Your total gained is 3%
Summary
The reason behind this strategy is to minimize risk. with normal strategy, you only have two outcomes which are either win or loss. With this strategy, you have three outcomes which are win, loss or break even.
Gandulfas Trading SuiteAn indicator designed to quickly and easily enter you trades, with all the information you need in just one chart!
It uses a combination of :
Price action to have a Weekly BIAS
Volatility channels to track dynamic points of support/resistance
A momentum indicator on the background to point out when the pair is in "overbought/oversold" status - adds confluence to our entries.
Volatility pivots based on the Average Daily Range, to quickly manage your entries and stop losses - more confluence!
How to use this system?
First, we use the price action lines to define a weekly bias. If the price moves above this anchor, then we are looking for buying oportunities on retracements. If the price moves below this anchor, then we look for seling oportunities on retracements.
Now that we have a Weekly BIAS, we know that for this week we are currently looking for buying oportunities. Then, we can use the Volatility Channel to look for buying oportunities. Optimal entries are found within the channels and clouds!.
We should always look for a confluence of factors before entering any trade , and for that we decided to also add to the system a momentum indicator . This also can act as an early warning, telling us a that a potential setup is coming our way.
Does it repaint? No. The system is designed this way to reduce confirmation bias and keep your emotions at bay. For example, if you see the momentum indicator , maybe it is close to your threshold but it did not crossed over or crossed under it. Maybe it's 35, when your threshold is 25. It is low indeed , but it has not crossed it. Fact is this will get you emotional, and perhaps make you trigger a trade before you should.
So for this reason we decided to add the momentum indicator just showing the triangles when indeed the threshold has been crossed, so you don't get emotional trying to enter a trade too early. For this reason, you should always wait to the candle to close, to see if indeed we have crossed that threshold.
And then we have our volatility pivots , that help us to define better entries, targets and stop losses. They are based on the Average Daily Range, and it's use is pretty much straigthforward. The notion of this system is to take entries in the weekly direction. So, what we are really trying to achieve here is to get a chunk of that weekly expansion . The fastest the better, as having a positions means having exposure to the market. To achieve this objective the daily range helps us a lot.
If we are looking for a bullish day, that means we are looking for an good expansion of the Open-High range. So the daily range helps us to see how many pips this range could be on any given day. Then we apply a correction factor , because we do not want to estimate the whole range, we are good to go if we could anticipate just a 60% of it, let's say.
And it also helps us to define our stop loss places, because the range also tell us when it is most probable that our trading idea was wrong. Because if we are looking for a bullish day, we expect a good expansion on the open-high, not on the open-low!. So it also makes sense to take a look on the level that makes our idea most probably wrong!.
Here you can see how these pivots helps us to add extra confluence to our trading idea.
Our best trades are then performed....
Wednesday on the chart is the perfect example of the best type of trade you could perform. You have price entering the volatility channel, with the momentum indicator in our oversold zone, and price just a bit above our ADR pivots!.
Where should you put your targets?
You can use the volatility channel, and or also the pivots as we just described!
Where should you put your stop loss?
Below the volatility channel, taking into account where the pivots are. Because remember, if price trades below those, it most likely means that your idea was wrong, and you should not keep the trade open.
If you want to test or use this trading system on a regular basis, please get in touch with us through the private chat!
T3 ATR [DCAUT]█ T3 ATR
📊 ORIGINALITY & INNOVATION
The T3 ATR indicator represents an important enhancement to the traditional Average True Range (ATR) indicator by incorporating the T3 (Tilson Triple Exponential Moving Average) smoothing algorithm. While standard ATR uses fixed RMA (Running Moving Average) smoothing, T3 ATR introduces a configurable volume factor parameter that allows traders to adjust the smoothing characteristics from highly responsive to heavily smoothed output.
This innovation addresses a fundamental limitation of traditional ATR: the inability to adapt smoothing behavior without changing the calculation period. With T3 ATR, traders can maintain a consistent ATR period while adjusting the responsiveness through the volume factor, making the indicator adaptable to different trading styles, market conditions, and timeframes through a single unified implementation.
The T3 algorithm's triple exponential smoothing with volume factor control provides improved signal quality by reducing noise while maintaining better responsiveness compared to traditional smoothing methods. This makes T3 ATR particularly valuable for traders who need to adapt their volatility measurement approach to varying market conditions without switching between multiple indicator configurations.
📐 MATHEMATICAL FOUNDATION
The T3 ATR calculation process involves two distinct stages:
Stage 1: True Range Calculation
The True Range (TR) is calculated using the standard formula:
TR = max(high - low, |high - close |, |low - close |)
This captures the greatest of the current bar's range, the gap from the previous close to the current high, or the gap from the previous close to the current low, providing a comprehensive measure of price movement that accounts for gaps and limit moves.
Stage 2: T3 Smoothing Application
The True Range values are then smoothed using the T3 algorithm, which applies six exponential moving averages in succession:
First Layer: e1 = EMA(TR, period), e2 = EMA(e1, period)
Second Layer: e3 = EMA(e2, period), e4 = EMA(e3, period)
Third Layer: e5 = EMA(e4, period), e6 = EMA(e5, period)
Final Calculation: T3 = c1×e6 + c2×e5 + c3×e4 + c4×e3
The coefficients (c1, c2, c3, c4) are derived from the volume factor (VF) parameter:
a = VF / 2
c1 = -a³
c2 = 3a² + 3a³
c3 = -6a² - 3a - 3a³
c4 = 1 + 3a + a³ + 3a²
The volume factor parameter (0.0 to 1.0) controls the weighting of these coefficients, directly affecting the balance between responsiveness and smoothness:
Lower VF values (approaching 0.0): Coefficients favor recent data, resulting in faster response to volatility changes with minimal lag but potentially more noise
Higher VF values (approaching 1.0): Coefficients distribute weight more evenly across the smoothing layers, producing smoother output with reduced noise but slightly increased lag
📊 COMPREHENSIVE SIGNAL ANALYSIS
Volatility Level Interpretation:
High Absolute Values: Indicate strong price movements and elevated market activity, suggesting larger position risks and wider stop-loss requirements, often associated with trending markets or significant news events
Low Absolute Values: Indicate subdued price movements and quiet market conditions, suggesting smaller position risks and tighter stop-loss opportunities, often associated with consolidation phases or low-volume periods
Rapid Increases: Sharp spikes in T3 ATR often signal the beginning of significant price moves or market regime changes, providing early warning of increased trading risk
Sustained High Levels: Extended periods of elevated T3 ATR indicate sustained trending conditions with persistent volatility, suitable for trend-following strategies
Sustained Low Levels: Extended periods of low T3 ATR indicate range-bound conditions with suppressed volatility, suitable for mean-reversion strategies
Volume Factor Impact on Signals:
Low VF Settings (0.0-0.3): Produce responsive signals that quickly capture volatility changes, suitable for short-term trading but may generate more frequent color changes during minor fluctuations
Medium VF Settings (0.4-0.7): Provide balanced signal quality with moderate responsiveness, filtering out minor noise while capturing significant volatility changes, suitable for swing trading
High VF Settings (0.8-1.0): Generate smooth, stable signals that filter out most noise and focus on major volatility trends, suitable for position trading and long-term analysis
🎯 STRATEGIC APPLICATIONS
Position Sizing Strategy:
Determine your risk per trade (e.g., 1% of account capital - adjust based on your risk tolerance and experience)
Decide your stop-loss distance multiplier (e.g., 2.0x T3 ATR - this varies by market and strategy, test different values)
Calculate stop-loss distance: Stop Distance = Multiplier × Current T3 ATR
Calculate position size: Position Size = (Account × Risk %) / Stop Distance
Example: $10,000 account, 1% risk, T3 ATR = 50 points, 2x multiplier → Position Size = ($10,000 × 0.01) / (2 × 50) = $100 / 100 points = 1 unit per point
Important: The ATR multiplier (1.5x - 3.0x) should be determined through backtesting for your specific instrument and strategy - using inappropriate multipliers may result in stops that are too tight (frequent stop-outs) or too wide (excessive losses)
Adjust the volume factor to match your trading style: lower VF for responsive stop distances in short-term trading, higher VF for stable stop distances in position trading
Dynamic Stop-Loss Placement:
Determine your risk tolerance multiplier (typically 1.5x to 3.0x T3 ATR)
For long positions: Set stop-loss at entry price minus (multiplier × current T3 ATR value)
For short positions: Set stop-loss at entry price plus (multiplier × current T3 ATR value)
Trail stop-losses by recalculating based on current T3 ATR as the trade progresses
Adjust the volume factor based on desired stop-loss stability: higher VF for less frequent adjustments, lower VF for more adaptive stops
Market Regime Identification:
Calculate a reference volatility level using a longer-period moving average of T3 ATR (e.g., 50-period SMA)
High Volatility Regime: Current T3 ATR significantly above reference (e.g., 120%+) - favor trend-following strategies, breakout trades, and wider targets
Normal Volatility Regime: Current T3 ATR near reference (e.g., 80-120%) - employ standard trading strategies appropriate for prevailing market structure
Low Volatility Regime: Current T3 ATR significantly below reference (e.g., <80%) - favor mean-reversion strategies, range trading, and prepare for potential volatility expansion
Monitor T3 ATR trend direction and compare current values to recent history to identify regime transitions early
Risk Management Implementation:
Establish your maximum portfolio heat (total risk across all positions, typically 2-6% of capital)
For each position: Calculate position size using the formula Position Size = (Account × Individual Risk %) / (ATR Multiplier × Current T3 ATR)
When T3 ATR increases: Position sizes automatically decrease (same risk %, larger stop distance = smaller position)
When T3 ATR decreases: Position sizes automatically increase (same risk %, smaller stop distance = larger position)
This approach maintains constant dollar risk per trade regardless of market volatility changes
Use consistent volume factor settings across all positions to ensure uniform risk measurement
📋 DETAILED PARAMETER CONFIGURATION
ATR Length Parameter:
Default Setting: 14 periods
This is the standard ATR calculation period established by Welles Wilder, providing balanced volatility measurement that captures both short-term fluctuations and medium-term trends across most markets and timeframes
Selection Principles:
Shorter periods increase sensitivity to recent volatility changes and respond faster to market shifts, but may produce less stable readings
Longer periods emphasize sustained volatility trends and filter out short-term noise, but respond more slowly to genuine regime changes
The optimal period depends on your holding time, trading frequency, and the typical volatility cycle of your instrument
Consider the timeframe you trade: Intraday traders typically use shorter periods, swing traders use intermediate periods, position traders use longer periods
Practical Approach:
Start with the default 14 periods and observe how well it captures volatility patterns relevant to your trading decisions
If ATR seems too reactive to minor price movements: Increase the period until volatility readings better reflect meaningful market changes
If ATR lags behind obvious volatility shifts that affect your trades: Decrease the period for faster response
Match the period roughly to your typical holding time - if you hold positions for N bars, consider ATR periods in a similar range
Test different periods using historical data for your specific instrument and strategy before committing to live trading
T3 Volume Factor Parameter:
Default Setting: 0.7
This setting provides a reasonable balance between responsiveness and smoothness for most market conditions and trading styles
Understanding the Volume Factor:
Lower values (closer to 0.0) reduce smoothing, allowing T3 ATR to respond more quickly to volatility changes but with less noise filtering
Higher values (closer to 1.0) increase smoothing, producing more stable readings that focus on sustained volatility trends but respond more slowly
The trade-off is between immediacy and stability - there is no universally optimal setting
Selection Principles:
Match to your decision speed: If you need to react quickly to volatility changes for entries/exits, use lower VF; if you're making longer-term risk assessments, use higher VF
Match to market character: Noisier, choppier markets may benefit from higher VF for clearer signals; cleaner trending markets may work well with lower VF for faster response
Match to your preference: Some traders prefer responsive indicators even with occasional false signals, others prefer stable indicators even with some delay
Practical Adjustment Guidelines:
Start with default 0.7 and observe how T3 ATR behavior aligns with your trading needs over multiple sessions
If readings seem too unstable or noisy for your decisions: Try increasing VF toward 0.9-1.0 for heavier smoothing
If the indicator lags too much behind volatility changes you care about: Try decreasing VF toward 0.3-0.5 for faster response
Make meaningful adjustments (0.2-0.3 changes) rather than small increments - subtle differences are often imperceptible in practice
Test adjustments in simulation or paper trading before applying to live positions
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
The T3 smoothing algorithm provides improved responsiveness compared to traditional RMA smoothing used in standard ATR. The triple exponential design with volume factor control allows the indicator to respond more quickly to genuine volatility changes while maintaining the ability to filter noise through appropriate VF settings. This results in earlier detection of volatility regime changes compared to standard ATR, particularly valuable for risk management and position sizing adjustments.
Signal Stability:
Unlike simple smoothing methods that may produce erratic signals during transitional periods, T3 ATR's multi-layer exponential smoothing provides more stable signal progression. The volume factor parameter allows traders to tune signal stability to their preference, with higher VF settings producing remarkably smooth volatility profiles that help avoid overreaction to temporary market fluctuations.
Comparison with Standard ATR:
Adaptability: T3 ATR allows adjustment of smoothing characteristics through the volume factor without changing the ATR period, whereas standard ATR requires changing the period length to alter responsiveness, potentially affecting the fundamental volatility measurement
Lag Reduction: At lower volume factor settings, T3 ATR responds more quickly to volatility changes than standard ATR with equivalent periods, providing earlier signals for risk management adjustments
Noise Filtering: At higher volume factor settings, T3 ATR provides superior noise filtering compared to standard ATR, producing cleaner signals for long-term analysis without sacrificing volatility measurement accuracy
Flexibility: A single T3 ATR configuration can serve multiple trading styles by adjusting only the volume factor, while standard ATR typically requires multiple instances with different periods for different trading applications
Suitable Use Cases:
T3 ATR is well-suited for the following scenarios:
Dynamic Risk Management: When position sizing and stop-loss placement need to adapt quickly to changing volatility conditions
Multi-Style Trading: When a single volatility indicator must serve different trading approaches (day trading, swing trading, position trading)
Volatile Markets: When standard ATR produces too many false volatility signals during choppy conditions
Systematic Trading: When algorithmic systems require a single, configurable volatility input that can be optimized for different instruments
Market Regime Analysis: When clear identification of volatility expansion and contraction phases is critical for strategy selection
Known Limitations:
Like all technical indicators, T3 ATR has limitations that users should understand:
Historical Nature: T3 ATR is calculated from historical price data and cannot predict future volatility with certainty
Smoothing Trade-offs: The volume factor setting involves a trade-off between responsiveness and smoothness - no single setting is optimal for all market conditions
Extreme Events: During unprecedented market events or gaps, T3 ATR may not immediately reflect the full scope of volatility until sufficient data is processed
Relative Measurement: T3 ATR values are most meaningful in relative context (compared to recent history) rather than as absolute thresholds
Market Context Required: T3 ATR measures volatility magnitude but does not indicate price direction or trend quality - it should be used in conjunction with directional analysis
Performance Expectations:
T3 ATR is designed to help traders measure and adapt to changing market volatility conditions. When properly configured and applied:
It can help reduce position risk during volatile periods through appropriate position sizing
It can help identify optimal times for more aggressive position sizing during stable periods
It can improve stop-loss placement by adapting to current market conditions
It can assist in strategy selection by identifying volatility regimes
However, volatility measurement alone does not guarantee profitable trading. T3 ATR should be integrated into a comprehensive trading approach that includes directional analysis, proper risk management, and sound trading psychology.
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. T3 ATR provides adaptive volatility measurement but has limitations and should not be used as the sole basis for trading decisions. The indicator measures historical volatility patterns, and past volatility characteristics do not guarantee future volatility behavior. Market conditions can change rapidly, and extreme events may produce volatility readings that fall outside historical norms.
Traders should combine T3 ATR with directional analysis tools, support/resistance analysis, and other technical indicators to form a complete trading strategy. Proper backtesting and forward testing with appropriate risk management is essential before applying T3 ATR-based strategies to live trading. The volume factor parameter should be optimized for specific instruments and trading styles through careful testing rather than assuming default settings are optimal for all applications.
Signal Tester EN [Abusuhil]Signal Tester - Complete Description
Overview
Signal Tester is a comprehensive trading tool designed to backtest and analyze external trading signals with advanced risk management capabilities. The indicator provides seven different calculation methods for stop-loss and take-profit levels, along with detailed performance statistics and real-time tracking of active trades.
Important Disclaimer: This indicator is a tool for analysis and education purposes only. Past performance does not guarantee future results. Trading involves substantial risk of loss and is not suitable for all investors. Always conduct your own research and consider seeking advice from a qualified financial advisor before making trading decisions.
Key Features
7 Calculation Methods for customizable risk management
External Signal Integration via any oscillator or indicator
Real-time Trade Tracking with visual entry/exit points
Comprehensive Statistics Table showing win rate, profit/loss, and active trades
Date Filtering for focused backtesting periods
Custom Alerts for new buy signals
Multi-Target System with up to 5 take-profit levels
How to Use
Step 1: Connect External Signal
The indicator requires an external signal source to generate buy signals.
Add your preferred indicator to the chart (RSI, MACD, Stochastic, custom indicator, etc.)
In Signal Tester settings, locate "External Indicator" input
Click the input and select your indicator's plot line
Buy signals are generated when the external source crosses above zero
Example: If using RSI, connect the RSI line. A buy signal triggers when RSI crosses above the zero reference (if plotted as oscillator).
Step 2: Choose Your Calculation Method
Select one of seven methods under "Calculation Method":
1. Percentage %
The simplest method using fixed percentage values.
Settings:
Stop Loss %: Distance from entry to stop-loss (default: 2%)
Target 1-5 %: Distance from entry to each take-profit level
Example: Entry at $100
Stop Loss (2%): $98
Target 1 (2%): $102
Target 2 (4%): $104
Best For: Beginners, markets with consistent volatility
2. ATR Multiplier
Uses Average True Range for dynamic levels based on market volatility.
Settings:
ATR Period: Calculation period (default: 14)
Stop Multiplier: ATR multiplier for stop-loss (default: 1.5)
Target Multipliers: ATR multipliers for each take-profit
Example: Entry at $100, ATR = $2
Stop Loss (1.5x ATR): $100 - $3 = $97
Target 1 (2x ATR): $100 + $4 = $104
Best For: Volatile markets, adapting to changing conditions
3. Risk:Reward Ratio
Calculates targets based on risk-to-reward ratios.
Settings:
Stop Loss %: Initial risk percentage
Target Ratios: R:R ratio for each target (1:1.5, 1:2, 1:3, etc.)
Example: Entry at $100, Stop at $98 (2% risk = $2)
Target 1 (1:1.5): $100 + ($2 × 1.5) = $103
Target 2 (1:2): $100 + ($2 × 2) = $104
Target 3 (1:3): $100 + ($2 × 3) = $106
Best For: Traders focused on risk management and position sizing
4. Swing High/Low
Places stop-loss at recent swing low with targets as multiples of the risk.
Settings:
Swing Lookback Candles: Number of bars to find swing low (default: 5)
Stop Safety Distance %: Buffer below swing low
Target Multipliers: Risk multiples for each target
Example: Entry at $105, Swing Low at $100
Stop Loss: $100 - 0.1% = $99.90 (risk = $5.10)
Target 1 (1.5x): $105 + ($5.10 × 1.5) = $112.65
Best For: Swing traders, respecting market structure
5. Partial Take Profit
Sells portions of the position at each target level, moving stop to entry after first target.
Settings:
Stop Loss %: Initial stop distance
Target 1-5 %: Price levels for partial exits
Sell % at TP1-4: Percentage of position to close at each level
Example: 100% position, 50% sell at each target
TP1 hit: Sell 50%, remaining 50%, stop moves to entry
TP2 hit: Sell 25% (50% of remaining), remaining 25%
TP3 hit: Sell 12.5%, remaining 12.5%
Best For: Conservative traders, locking in profits gradually
6. Trailing Stop
Similar to Partial Take Profit but trails the stop-loss to each achieved target.
Settings:
Stop Loss %: Initial stop distance
Target 1-5 %: Price levels for trailing stops
Sell % at TP1-4: Percentage to close at each level
Example:
TP1 ($102) hit: Sell 50%, stop trails to $102
TP2 ($104) hit: Sell 25%, stop trails to $104
Price retraces to $104: Exit with locked profits
Best For: Trend followers, maximizing profit in strong moves
7. Smart Exit
Advanced method that moves stop to entry after first target, then exits based on technical conditions.
Settings:
Stop Loss %: Initial stop distance
First Target %: When hit, stop moves to breakeven
Exit Method: Choose from 8 exit strategies
Exit Methods:
Close < EMA 21: Exits when price closes below 21-period EMA
Close < MA 20: Exits when price closes below 20-period Moving Average
Supertrend Flip: Exits when Supertrend indicator flips bearish
ATR Trailing Stop: Dynamic trailing stop based on ATR
MACD Crossover: Exits on MACD bearish crossover
RSI < 50: Exits when RSI drops below specified level
Parabolic SAR Flip: Exits when SAR flips above price
Bollinger Bands: Exits when price closes below middle or lower band
Best For: Advanced traders, letting winners run with protection
Date Filtering
Control which trades are included in backtesting.
Filter Types:
Specific Date: Only trades after selected date
Number of Weeks: Last X weeks (default: 12)
Number of Months: Last X months (default: 3)
How to Enable:
Check "Enable Date Filter"
Select filter type
Set the date or number of weeks/months
Use Case: Test strategy performance in recent market conditions or specific periods
Understanding the Statistics Table
The table displays the last 10 trades plus comprehensive statistics:
Trade Columns:
#: Trade number
Entry: Entry price
Stop: Current stop-loss level
TP1-TP5: Checkmarks (✅) when targets are hit
Profit %: Realized profit for the trade
Max %: Maximum unrealized profit reached (⬆️ indicates active trade)
Status:
🔄 Active trade
✅ Closed winner
❌ SL - Stopped out
Summary Row:
Total: Number of trades executed
Period: Duration of trading period (Years, Months, Days)
Statistics Row:
W: Number of winning trades
L: Number of losing trades
A: Number of active (open) trades
Win Rate %: (Wins / Total Trades) × 100
Performance Row:
Profit: Total profit from all winning trades
Loss: Total loss from all losing trades
Net: Net profit/loss (Profit - Loss)
Visual Elements
When a buy signal triggers, the indicator draws:
Blue Line: Entry price
Red Line: Stop-loss level
Green Lines: Take-profit levels (up to 5)
Green Label: Trade number below the entry bar
Green Triangle: Buy signal marker
Alerts
The indicator includes customizable alerts for new buy signals.
Setting Up Alerts:
Click the "⏰" icon in TradingView
Select "Signal Tester "
Choose condition: "Buy"
Configure notification preferences (popup, email, webhook)
Click "Create"
Alert Message Format:
🚀 New Buy Signal!
Price:
Trade #:
Best Practices
Backtest First: Test each calculation method on historical data before live trading
Match Timeframe: Use the indicator on the timeframe you plan to trade
Combine with Analysis: Use alongside support/resistance, trend analysis, and other tools
Risk Management: Never risk more than 1-2% of capital per trade
Review Statistics: Regularly check win rate and profit/loss metrics
Adjust Settings: Optimize parameters based on the asset's volatility and your risk tolerance
Limitations
Requires external signal source (does not generate signals independently)
Backtesting assumes perfect entry/exit execution (real trading includes slippage)
Past performance does not guarantee future results
Should be used as one component of a complete trading strategy
Version Information
Version: 1.0
Pine Script Version: v5
Type: Overlay Indicator
Author: Abusuhil
Support and Updates
This indicator is provided as-is for educational and analytical purposes. Users are responsible for their own trading decisions and should thoroughly test any strategy before implementing it with real capital.
Risk Warning: Trading financial instruments carries a high level of risk and may not be suitable for all investors. The high degree of leverage can work against you as well as for you. Before deciding to trade, you should carefully consider your investment objectives, level of experience, and risk appetite. Only trade with money you can afford to lose.
Advanced Trading System - [WOLONG X DBG]Advanced Multi-Timeframe Trading System
Overview
This technical analysis indicator combines multiple established methodologies to provide traders with market insights across various timeframes. The system integrates SuperTrend analysis, moving average clouds, MACD-based candle coloring, RSI analysis, and multi-timeframe trend detection to suggest potential entry and exit opportunities for both swing and day trading approaches.
Methodology
The indicator employs a multi-layered analytical approach based on established technical analysis principles:
Core Signal Generation
SuperTrend Engine: Utilizes adaptive SuperTrend calculations with customizable sensitivity (1-20) combined with SMA confirmation filters to identify potential trend changes and continuations
Braid Filter System: Implements moving average filtering using multiple MA types (McGinley Dynamic, EMA, DEMA, TEMA, Hull, Jurik, FRAMA) with percentage-based strength filtering to help reduce false signals
Multi-Timeframe Analysis: Analyzes trend conditions across 10 different timeframes (1-minute to Daily) using EMA-based trend detection for broader market context
Advanced Features
MACD Candle Coloring: Applies dynamic 4-level candle coloring system based on MACD histogram momentum and signal line relationships for visual trend strength assessment
RSI Analysis: Identifies potential reversal areas using RSI oversold/overbought conditions with SuperTrend confirmation
Take Profit Analysis: Features dual-mode TP detection using statistical slope analysis and Parabolic SAR integration for exit timing analysis
Key Components
Signal Types
Primary Signals: Green ▲ for potential long entries, Red ▼ for potential short entries with trend and SMA alignment
Reversal Signals: Small circular indicators for RSI-based counter-trend possibilities
Take Profit Markers: X-cross symbols indicating statistical TP analysis zones
Pullback Signals: Purple arrows for potential trend continuation entries using Parabolic SAR
Visual Elements
8-Layer MA Cloud: Customizable moving average cloud system with 3 color themes for trend visualization
Real-Time Dashboard: Multi-timeframe trend analysis table showing bullish/bearish status across all timeframes
Dynamic Candle Colors: 4-intensity MACD-based coloring system (ranging from light to strong trend colors)
Entry/SL/TP Labels: Automatic calculation and display of suggested entry points, stop losses, and multiple take profit levels
Usage Instructions
Basic Configuration
Sensitivity Setting: Start with default value 6
Increase (7-15) for more frequent signals in volatile markets
Decrease (3-5) for higher quality signals in trending markets
MA Filter Type: McGinley Dynamic recommended for smoother signals
Filter Strength: Set to 80% for balanced filtering, adjust based on market conditions
Signal Interpretation
Long Entry: Green ▲ suggests when price crosses above SuperTrend with bullish SMA alignment
Short Entry: Red ▼ suggests when price crosses below SuperTrend with bearish SMA alignment
Reversal Opportunities: Small circles indicate RSI-based counter-trend analysis
Take Profit Zones: X-crosses mark statistical TP areas based on slope analysis
Dashboard Analysis
Green Cells: Bullish trend detected on that timeframe
Red Cells: Bearish trend detected on that timeframe
Multi-Timeframe Confluence: Look for alignment across multiple timeframes for stronger signal confirmation
Risk Management Features
Automatic Calculations
ATR-Based Stop Loss: Dynamic stop loss calculation using ATR multiplier (default 1.9x)
Multiple Take Profit Levels: Three TP targets with 1:1, 1:2, and 1:3 risk-reward ratios
Position Sizing Guidance: Entry labels display suggested price levels for order placement
Confirmation Requirements
Trend Alignment: Requires SuperTrend and SMA confirmation before signal generation
Filter Validation: Braid filter must show sufficient strength before signals activate
Multi-Timeframe Context: Dashboard provides broader market context for decision making
Optimal Settings
Timeframe Recommendations
Scalping: 1M-5M charts with sensitivity 8-12
Day Trading: 15M-1H charts with sensitivity 6-8
Swing Trading: 4H-Daily charts with sensitivity 4-6
Market Conditions
Trending Markets: Reduce sensitivity, increase filter strength
Ranging Markets: Increase sensitivity, enable reversal signals
High Volatility: Adjust ATR risk factor to 2.0-2.5
Advanced Features
Customization Options
MA Cloud Periods: 8 customizable periods for cloud layers (default: 2,6,11,18,21,24,28,34)
Color Themes: Three professional color schemes plus transparent option
Dashboard Position: 9 positioning options with 4 size settings
Signal Filtering: Individual toggle controls for each signal type
Technical Specifications
Moving Average Types: 21 different MA calculations including advanced types (Jurik, FRAMA, VIDA, CMA)
Pullback Detection: Parabolic SAR with customizable start, increment, and maximum values
Statistical Analysis: Linear regression slope calculation for trend-based TP analysis
Important Limitations
Lagging Nature: Some signals may appear after potential entry points due to confirmation requirements
Ranging Markets: May produce false signals during extended sideways price action
High Volatility: Requires parameter adjustment during news events or unusual market conditions
Computational Load: Multiple timeframe analysis may impact performance on slower devices
No Guarantee: All signals are suggestions based on technical analysis and may be incorrect
Educational Disclaimers
This indicator is designed for educational and analytical purposes only. It represents a technical analysis tool based on mathematical calculations of historical price data and should not be considered as financial advice or trading recommendations.
Risk Warning: Trading involves substantial risk of loss and is not suitable for all investors. Past performance of any trading system or methodology is not necessarily indicative of future results. The high degree of leverage can work against you as well as for you.
Important Notes:
Always conduct your own analysis before making trading decisions
Use appropriate position sizing and risk management strategies
Never risk more than you can afford to lose
Consider your investment objectives, experience level, and risk tolerance
Seek advice from qualified financial professionals when needed
Performance Disclaimer: Backtesting results do not guarantee future performance. Market conditions change constantly, and what worked in the past may not work in the future. Always paper trade new strategies before risking real capital.
Estrategia Cava - IndicadorSimplified Criteria of the Cava Strategy
Below is the logic behind the Cava strategy, broken down into conditions for a buy operation:
Variables and Necessary Data
EMA 55: 55-period Exponential Moving Average.
MACD: Two lines (MACD Line and Signal Line) and the histogram.
RSI: Relative Strength Index.
Stochastic: Two lines (%K and %D).
Closing Price: The closing price of the current period.
Previous Closing Price: The closing price of the previous period.
Entry Logic (Buy Operation)
Trend Condition (EMA 55):
The price must be above the EMA 55.
The EMA 55 must have a positive slope (or at least not a negative one). This can be checked if the current EMA 55 is greater than the previous period's EMA 55.
Momentum Conditions (Oscillators):
MACD: The MACD line must have crossed above the signal line. For a strong signal, this cross should occur near or above the zero line.
RSI: The RSI must have exited the "oversold" zone (generally below 30) and be rising.
Stochastic: The Stochastic must have crossed upwards from the "oversold" zone (generally below 20).
Confirmation Condition (Price):
The current closing price must be higher than the previous closing price. This confirms the strength of the signal.
Position Management (Exit)
Take Profit: An exit can be programmed at a predetermined price target (e.g., the next resistance level) or when the momentum of the move begins to decrease.
Stop Loss: A stop loss should be placed below a significant support level or the entry point to limit losses in case the trade does not evolve as expected. The Cava strategy focuses on dynamic stop-loss management, moving it in the trader's favor as the price moves.
In summary, the strategy is a filtering system. If all conditions are met, the trade is considered high probability. If only some are met, the signal is discarded, and you wait for the next one. It's crucial to understand that discipline and risk management are just as important as the indicators themselves.
ICT Institutional Order Flow (Riz)This indicator implements Inner Circle Trader (ICT) institutional order flow concepts to identify high-probability entry points where smart money is actively participating in the market. It combines volume analysis, market structure, and price action patterns to detect institutional accumulation and distribution zones.
Core Concepts & Methodology
1. Institutional Order Blocks Detection
Order blocks represent the last opposing candle before a strong directional move, indicating institutional accumulation (bullish) or distribution (bearish) zones.
How it works:
⦁ Identifies the final bearish candle before bullish expansion (accumulation)
⦁ Identifies the final bullish candle before bearish expansion (distribution)
⦁ Validates with volume spike (2x average) to confirm institutional participation
⦁ Requires minimum 0.5% price displacement to filter weak moves
⦁ Tracks these zones as future support/resistance levels
2. Fair Value Gap (FVG) Analysis
FVGs are price inefficiencies created by aggressive institutional orders that leave gaps in price action.
Detection method:
⦁ Bullish FVG: When current low > high from 2 bars ago
⦁ Bearish FVG: When current high < low from 2 bars ago
⦁ Minimum gap size filter (0.1% default) eliminates noise
⦁ Monitors gap fills with volume for entry signals
⦁ Gaps act as magnets drawing price back for "rebalancing"
3. Liquidity Hunt Detection
Institutions often trigger retail stop losses before reversing direction, creating liquidity for their positions.
Algorithm:
⦁ Calculates rolling 20-period highs/lows as liquidity pools
⦁ Detects wicks beyond these levels (0.1% sensitivity)
⦁ Identifies rejection back inside range (liquidity grab)
⦁ Volume spike confirmation ensures institutional involvement
⦁ These reversals often mark significant turning points
4. Volume Profile Integration
Analyzes volume distribution across price levels to identify institutional interest zones.
Components:
⦁ Point of Control (POC): Price level with highest volume (institutional consensus)
⦁ Value Area: 70% of volume range (institutional comfort zone)
⦁ Uses 50-bar lookback to build volume histogram
⦁ 20 price levels for granular distribution analysis
5. Market Structure Analysis
Determines overall trend bias using pivot points and swing analysis.
Process:
⦁ Identifies swing highs/lows using 3-bar pivots
⦁ Bullish structure: Price above last swing high
⦁ Bearish structure: Price below last swing high
⦁ Filters signals to trade with institutional direction
Signal Generation Logic
BUY signals trigger when ANY condition is met:
1. Order Block Formation: Bearish-to-bullish transition + volume spike + strong move
2. Liquidity Grab Reversal: Sweep below lows + recovery + volume spike
3. FVG Fill: Price fills bullish gap with institutional volume (within 3 bars)
4. Order Block Respect: Price bounces from previous bullish OB + volume
SELL signals trigger when ANY condition is met:
1. Order Block Formation: Bullish-to-bearish transition + volume spike + strong move
2. Liquidity Grab Reversal: Sweep above highs + rejection + volume spike
3. FVG Fill: Price fills bearish gap with institutional volume (within 3 bars)
4. Order Block Respect: Price rejects from previous bearish OB + volume
Additional filters:
⦁ Signals align with market structure (no counter-trend trades)
⦁ No new signals while position is active
⦁ All signals require volume confirmation (institutional fingerprint)
Trading Style Auto-Configuration
The indicator features intelligent preset configurations for different trading styles:
Scalping Mode (1-5 min charts):
⦁ Volume multiplier: 1.5x (more signals)
⦁ Tighter parameters for quick trades
⦁ Risk:Reward 1.5:1, ATR multiplier 1.0
Day Trading Mode (15-30 min charts):
⦁ Volume multiplier: 1.7x (balanced)
⦁ Medium sensitivity settings
⦁ Risk:Reward 2:1, ATR multiplier 1.5
Swing Trading Mode (1H-4H charts):
⦁ Volume multiplier: 2.0x (quality focus)
⦁ Conservative parameters
⦁ Risk:Reward 3:1, ATR multiplier 2.0
Custom Mode:
⦁ Full manual control of all parameters
Visual Components
⦁ Order Blocks: Colored rectangles (green=bullish, red=bearish)
⦁ Fair Value Gaps: Orange boxes showing imbalances
⦁ Liquidity Levels: Dashed blue lines at key highs/lows
⦁ Volume Spikes: Yellow background highlighting
⦁ POC Line: Orange line showing highest volume price
⦁ Value Area: Blue shaded zone of 70% volume
⦁ Buy/Sell Signals: Triangle markers with text labels
⦁ Stop Loss/Take Profit: Dotted lines (red/green)
Information Panel
Real-time dashboard displaying:
⦁ Current trading mode
⦁ Volume ratio (current vs average)
⦁ Market structure (bullish/bearish)
⦁ Active order blocks count
⦁ Position status
⦁ Configuration details
How to Use
Step 1: Select Trading Style
Choose your style in settings - all parameters auto-adjust
Step 2: Timeframe Selection
⦁ Scalping: 1-5 minute charts
⦁ Day Trading: 15-30 minute charts
⦁ Swing: 1H-4H charts
Step 3: Signal Interpretation
⦁ Wait for BUY/SELL markers
⦁ Check volume ratio >2 for strong signals
⦁ Verify market structure alignment
⦁ Note automatic SL/TP levels
Step 4: Risk Management
⦁ Default 2:1 risk:reward (adjustable)
⦁ Stop loss: 1.5x ATR from entry
⦁ Position sizing based on stop distance
Best Practices
1. Higher probability setups occur when multiple conditions align
2. Volume confirmation is crucial - avoid signals without volume spikes
3. Trade with structure - longs in bullish, shorts in bearish structure
4. Monitor POC - acts as dynamic support/resistance
5. Confluence zones where OBs, FVGs, and liquidity levels overlap are strongest
Important Notes
⦁ Not a standalone system - combine with your analysis
⦁ Works best in trending markets with clear structure
⦁ Adjust settings based on instrument volatility
⦁ Backtest thoroughly on your specific markets
⦁ Past performance doesn't guarantee future results
Alerts Available
⦁ ICT Buy Signal
⦁ ICT Sell Signal
⦁ Volume Spike Detection
⦁ Liquidity Grab Detection
This indicator provides a systematic approach to ICT concepts, helping traders identify where institutions are entering positions through volume analysis and key price action patterns. The auto-configuration feature ensures optimal settings for your trading style without manual adjustment.
Disclaimer
This tool is for educational and research purposes only. It is not financial advice, nor does it guarantee profitability. All trading involves risk, and users should test thoroughly before applying live.
Technical Summary VWAP | RSI | VolatilityTechnical Summary VWAP | RSI | Volatility
The Quantum Trading Matrix is a multi-dimensional market-analysis dashboard designed as an educational and idea-generation tool to help traders read price structure, participation, momentum and volatility in one compact view. It is not an automated execution system; rather, it aggregates lightweight “quantum” signals — VWAP position, momentum oscillator behaviour, multi-EMA trend scoring, volume flow and institutional activity heuristics, market microstructure pivots and volatility measures — and synthesizes them into a single, transparent score and signal recommendation. The primary goal is to make explicit why a given market looks favourable or unfavourable by showing the individual ingredients and how they combine, enabling traders to learn, test and form rules based on observable market mechanics.
Each module of the matrix answers a distinct market question. VWAP and its percentage distance indicate whether the current price is trading above or below the intraday volume-weighted average — a proxy for intraday institutional control and value. The quantum momentum oscillator (fast and slow EMA difference scaled to percent) captures short-to-intermediate momentum shifts, providing a quickly responsive view of directional pressure. Multi-EMA trend scoring (8/21/50) produces a simple, transparent trend score by counting conditions such as price above EMAs and cross-EMAs ordering; this score is used to categorize market trend into descriptive buckets (e.g., STRONG UP, WEAK UP, NEUTRAL, DOWN). Volume analysis compares current volume to a recent moving average and computes a Z-score to detect spikes and unusual participation; additional buy/sell pressure heuristics (buyingPressure, sellingPressure, flowRatio) estimate whether upside or downside participation dominates the bar. Institutional activity is approximated by flagging large orders relative to volume baseline (e.g., volume > 2.5× MA) and estimating a dark pool proxy; this is a heuristic to highlight bars that likely had large players involved.
The dashboard also performs market-structure detection with small pivot windows to identify recent local support/resistance areas and computes price position relative to the daily high/low (dailyMid, pricePosition). Volatility is measured via ATR divided by price and bucketed into LOW/NORMAL/HIGH/EXTREME categories to help you adapt stop sizing and expectational horizons. Finally, all these pieces feed an interpretable scoring function that rewards alignment: VWAP above, strong flow ratio, bullish trend score, bullish momentum, and favorable RSI zone add to the overall score which is presented as a 0–100 metric and a colored emoji indicator for at-a-glance assessment.
The mashup is purposeful: each indicator covers a failure mode of the other. For example, momentum readings can be misleading during volatility spikes; VWAP informs whether institutions are on the bid or offer; volume Z-score detects abnormal participation that can validate a breakout; multi-EMA score mitigates single-EMA whipsaws by requiring a combination of price/EMA conditions. Combining these signals increases information content while keeping each component explainable — a key compliance requirement. The script intentionally emphasizes transparency: when it shows a BUY/SELL/HOLD recommendation, the dashboard shows the underlying sub-components so a trader can see whether VWAP, momentum, volume, trend or structure primarily drove the score.
For practical use, adopt a clear workflow: (1) check the matrix score and read the component tiles (VWAP position, momentum, trend and volume) to understand the drivers; (2) confirm market-structure support/resistance and pricePosition relative to the daily range; (3) require at least two corroborating components (for example, VWAP ABOVE + Momentum BULLISH or Volume spike + Trend STRONG UP) before considering entries; (4) use ATR-based stops or daily pivot distance for stop placement and size positions such that the trade risks a small, pre-defined percent of capital; (5) for intraday scalps shorten holding time and tighten stops, for swing trades increase lookback lengths and require multi-timeframe (higher TF) agreement. Treat the matrix as an idea filter and replay lab: when an alert triggers, replay the bars and observe which components anticipated the move and which lagged.
Parameter tuning matters. Shortening the momentum length makes the oscillator more sensitive (useful for scalping), while lengthening it reduces noise for swing contexts. Volume profile bars and MA length should match the instrument’s liquidity — increase the MA for low-liquidity stocks to reduce false institutional flags. The trend multiplier and signal sensitivity parameters let you calibrate how aggressively the matrix counts micro evidence into the score. Always backtest parameter sets across multiple periods and instruments; run walk-forward tests and keep a simple out-of-sample validation window to reduce overfitting risk.
Limitations and failure modes are explicit: institutional flags and dark-pool estimates are heuristics and cannot substitute for true tape or broker-level order flow; volume split by price range is an approximation and will not perfectly reflect signed volume; pivot detection with small windows may miss larger structural swings; VWAP is typically intraday-centric and less meaningful across multi-day swing contexts; the score is additive and may not capture non-linear relationships between features in extreme market regimes (e.g., flash crashes, circuit breaker events, or overnight gaps). The matrix is also susceptible to false signals during major news releases when price and volume behavior dislocate from typical patterns. Users should explicitly test behavior around earnings, macro data and low-liquidity periods.
To learn with the matrix, perform these experiments: (A) collect all BUY/SELL alerts over a 6-month period and measure median outcome at 5, 20 and 60 bars; (B) require additional gating conditions (e.g., only accept BUY when flowRatio>60 and trendScore≥4) and compare expectancy; (C) vary the institutional threshold (2×, 2.5×, 3× volumeMA) to see how many true positive spikes remain; (D) perform multi-instrument tests to ensure parameters are not tuned to a single ticker. Document every test and prefer robust, slightly lower returns with clearer logic rather than tuned “optimal” results that fail out of sample.
Originality statement: This script’s originality lies in the curated combination of intraday value (VWAP), multi-EMA trend scoring, momentum percent oscillator, volume Z-score plus buy/sell flow heuristics and a compact, interpretable scoring system. The script is not a simple indicator mashup; it is a didactic ensemble specifically designed to make internal rationale visible so traders can learn how each market characteristic contributes to actionable probability. The tool’s novelty is its emphasis on interpretability — showing the exact contributing signals behind a composite score — enabling reproducible testing and educational value.
Finally, for TradingView publication, include a clear description listing the modules, a short non-technical summary of how they interact, the tunable inputs, limitations and a risk disclaimer. Remove any promotional content or external contact links. If you used trademark symbols, either provide registration details or remove them. This transparent documentation satisfies TradingView’s requirement that mashups justify their composition and teach users how to use them.
Quantum Trading Matrix — multi-factor intraday dashboard (educational use only).
Purpose: Combines intraday VWAP position, a fast/slow EMA momentum percent oscillator, multi-EMA trend scoring (8/21/50), volume Z-score and buy/sell flow heuristics, pivot-based microstructure detection, and ATR-based volatility buckets to produce a transparent, componentized market score and trade-idea indicator. The mashup is intentional: VWAP identifies intraday value, momentum detects short bursts, EMAs provide structural trend bias, and volume/flow confirm participation. Signals require alignment of at least two components (for example, VWAP ABOVE + Momentum BULLISH + positive flow) for higher confidence.
Inputs: momentum period, volume MA/profile length, EMA configuration (8/21/50), trend multiplier, signal sensitivity, color and display options. Use shorter momentum lengths for scalps and longer for swing analysis. Increase volume MA for thinly traded instruments.
Limitations: Institutional/dark-pool estimates and flow heuristics are approximations, not actual exchange tape. VWAP is intraday-focused. Expect false signals during major news or low-liquidity sessions. Backtest and paper-trade before applying real capital.
Risk Disclaimer: For education and analysis only. Not financial advice. Use proper risk management. The author is not responsible for trading losses.
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Risk & Misuse Disclaimer
This indicator is provided for education, analysis and idea generation only. It is not investment or financial advice and does not guarantee profits. Institutional activity flags, dark-pool estimates and flow heuristics are approximations and should not be treated as exchange tape. Backtest thoroughly and use demo/paper accounts before trading real capital. Always apply appropriate position sizing and stop-loss rules. The author is not responsible for any trading losses resulting from the use or misuse of this tool.
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Risk Disclaimer: This tool is provided for education and analysis only. It is not financial advice and does not guarantee returns. Users assume all risk for trades made based on this script. Back test thoroughly and use proper risk management.
NIFTY_2min_FVG_Buy_StrategySummary
This strategy is designed for scalping Nifty on a 2-minute chart, focusing exclusively on long entries. The script's purpose is to identify and act on specific bullish reversal patterns based on volume analysis and price action.
Concept & Core Logic
The strategy operates on a two-stage confirmation process:
Volume Absorption: The initial condition seeks to identify potential bullish reversals by detecting signs of selling pressure being absorbed by buyers. This suggests that a downward move may be losing momentum.
Fair Value Gap (FVG) Confirmation: After a volume absorption signal, the strategy waits for a Fair Value Gap (FVG) to appear. A long entry signal is generated only after a candle closes above the FVG zone, serving as confirmation of bullish intent.
Risk Management
The strategy employs a fixed take profit and stop loss for each trade, based on the Nifty underlying price:
Take Profit: The exit signal is triggered when a trade reaches a 25-point profit.
Stop Loss: The exit signal is triggered when a trade reaches a 30-point loss.
Intended Use
This tool is intended for traders who:
Utilize mechanical, rule-based systems for intraday trading and scalping.
Are interested in studying a structured approach that combines volume analysis with price action inefficiencies like Fair Value Gaps.
Autoback Grid Lab [trade_lexx]Autoback Grid Lab: Your personal laboratory for optimizing grid strategies.
Introduction
First of all, it is important to understand that Autoback Grid Lab is a powerful professional tool for backtesting and optimization, created specifically for traders using both grid strategies and regular take profit with stop loss.
The main purpose of this script is to save you weeks and months of manual testing and parameter selection. Instead of manually testing one combination of settings after another, Autoback Grid Lab automatically tests thousands of unique strategies on historical data, providing you with a comprehensive report on the most profitable and, more importantly, sustainable ones.
If you want to find mathematically sound, most effective settings for your grid strategy on a specific asset and timeframe, then this tool was created for you.
Key Features
My tool has functionality that transforms the process of finding the perfect strategy from a routine into an exciting exploration.
🧪 Mass testing of thousands of combinations
The script is able to systematically generate and run a huge number of unique combinations of parameters through the built-in simulator. You set the ranges, and the indicator does all the work, testing all possible options for the following grid settings:
* Number of safety orders (SO Count)
* Grid step (SO Step)
* Step Multiplier (SO Multiplier) for building nonlinear grids
* Martingale for controlling the volume of subsequent orders
* Take Profit (%)
* Stop Loss (%), with the possibility of calculating both from the entry point and from the dynamic breakeven line
* The volume of the base order (Volume BO) as a percentage of the deposit
🏆 Unique `FinalScore` rating system
Sorting strategies by net profit alone is a direct path to self—deception and choosing strategies that are "tailored" to history and will inevitably fail in real trading. To solve this problem, we have developed FinalScore, a comprehensive assessment of the sustainability and quality of the strategy.
How does it work?
FinalScore analyzes each combination not one by one, but by nine key performance metrics at once, including Net Profit, Drawdown, Profit Factor, WinRate, Sharpe coefficients, Sortino, Squid and Omega. Each of these indicators is normalized, that is, reduced to a single scale. Then, to test the strategy for strength, the system performs 30 iterations, each time assigning random weights to these 9 metrics. A strategy gets a high FinalScore only if it shows consistently high results under different evaluation criteria. This proves her reliability and reduces the likelihood that her success was an accident.
📈 Realistic backtesting engine
The test results are meaningless if they do not take into account the actual trading conditions. Our simulator simulates real trading as accurately as possible, taking into account:
* Leverage: Calculation of the required margin to open and hold positions.
* Commission: A percentage commission is charged each time an order is opened and closed.
* Slippage: The order execution price is adjusted by a set percentage to simulate real market conditions.
* Liquidation model: This is one of the most important functions. The script continuously monitors the equity of the account (capital + unrealized P&L). If equity falls below the level of the supporting margin (calculated from the current value of the position), the simulator forcibly closes the position, as it would happen on a real exchange. This eliminates unrealistic scenarios where the strategy survives after a huge drawdown.
🔌 Integration with external signals
The indicator operates in two modes:
1. `No Signal': Standard mode. The trading cycle starts immediately as soon as the previous one has been closed. Ideal for testing the "pure" mechanics of the grid.
2. `External Signal`: In this mode, a new trading cycle will start only when a signal is received from an external source. You can connect any other indicator (such as the RSI, MACD, or your own strategy) to the script and use it as a trigger to log in. This allows you to combine the power of a grid strategy with your own entry points.
📊 Interactive and informative results panel
Upon completion of the calculations, a detailed table with the TOP N best strategies appears on the screen, sorted according to your chosen criterion. For each strategy in the rating, you will see not only the key metrics (Profit, Drawdown, duration of transactions), but also all the parameters that led to this result. You can immediately take these settings and apply them in your trading.
Application Options: How To Solve Your Problems
Autoback Grid Lab is a flexible tool that can be adapted to solve various tasks, from complete grid optimization to fine—tuning existing strategies. Here are some key scenarios for its use:
1. Complete Optimization Of The Grid Strategy
This is the basic and most powerful mode of use. You can find the most efficient grid configuration for any asset from scratch.
* How to use: Set wide ranges for all key grid parameters ('SO Count`, SO Step, SO Multiplier, Martingale, TP, etc.).
* In the `No Signal` mode: You will find the most stable grid configuration that works as an independent, constantly active strategy, regardless of which-or entrance indicators.
* In the `External Signal` mode: You can connect your favorite indicator for input (for example, RSI, MACD or a complex author's script) and find the optimal grid parameters that best complement your input signals. This allows you to turn a simple signaling strategy into a full-fledged grid system.
2. Selecting the Optimal Take Profit and Stop Loss for Your Strategy
Do you already have an entry strategy, but you are not sure where it is best to put Take Profit and Stop Loss? Autoback Grid Lab can solve this problem as well.
* How to use:
1. Disable optimization of all grid parameters (uncheck SO Count, SO Step, Martingale, etc.). Set the Min value for SO Count to 0.
2. Set the ranges for iteration only for 'Take Profit` and `Stop Loss'.
3. Turn on the External Signal mode and connect your indicator with input signals.
* Result: The script will run your historical entry signals with hundreds of different TP and SL combinations and show you which stop order levels bring maximum profit with minimal risk specifically for your entry points.
3. Building a Secure Network with Risk Management
Many traders are afraid of grid strategies because of the risk of large drawdowns. With the help of the optimizer, you can purposefully find the parameters for such a grid, which includes mandatory risk management through Stop Loss.
* How to use: Enable and set the range for Stop Loss, along with other grid parameters. Don't forget to test both types of SL calculations (`From entry point` and `From breakeven line`) to determine which one works more efficiently.
* Result: You will find balanced strategies in which the grid parameters (number of orders, martingale) and the Stop Loss level are selected in such a way as to maximize profits without going beyond the acceptable risk level for you.
How To Use The Indicator (Step-By-Step Guide)
Working with the Autoback Grid Lab is a sequential process consisting of four main steps: from initial setup to analysis of the finished results. Follow this guide to get the most out of the tool.
Step 1: Initial Setup
1. Add the indicator to the chart of your chosen asset and timeframe.
2. Open the script settings. The first thing you should pay attention to is the ⚙️ Optimization Settings ⚙️ group.
3. Set the `Bars Count'. This parameter determines how much historical data will be used for testing.
* Important: The more bars you specify, the more statistically reliable the backtest results will be. We recommend using the maximum available value (25,000) to test strategies at different market phases.
* Consider: The indicator performs all calculations on the last historical bar. After applying the TradingView settings, it will take some time to load all the specified bars. The results table will appear only after the data is fully loaded. Don't worry if it doesn't appear instantly. And if an error occurs, simply switch the number of combinations to 990 and back to 1000 until the table appears.
Step 2: Optimization Configuration
At this stage, you define the "universe" of parameters that our algorithm will explore.
1. Set the search ranges (🛠 Optimization Parameters 🛠 group).
For each grid parameter that you want to optimize (for example, SO Count or `Take Profit'), you must specify three values:
* Min: The minimum value of the range.
* Max: The maximum value of the range.
* Step: The step with which the values from Min to Max will be traversed.
*Example:* If you set Min=5, Max=10, and Step=1 for SO Count, the script will test strategies with 5, 6, 7, 8, 9, and 10 safety orders.
* Tip for users: To get the first results quickly, start with a larger step (for example, TP from 0.5% to 2.5% in 0.5 increments instead of 0.1). After you identify the most promising areas, you can perform a deeper analysis by expanding the ranges around these values.
2. Set Up Money Management (Group `💰 Money Management Settings 💰`).
Fill in these fields with the values that best match your actual trading conditions. This is critically important for obtaining reliable results.
* Capital: Your initial deposit.
* Leverage: Leverage.
* Commission (%): Your trading commission as a percentage.
* Slippage (%): Expected slippage.
* Liquidation Level (%): The level of the supporting margin (MMR in %). For example, for Binance Futures, this value is usually between 0.4% and 2.5%, depending on the asset and position size. Specify this value for your exchange.
3. Select the Sorting Criterion and the Direction (Group `⚙️ Optimization Settings ⚙️').
* `Sort by': Specify the main criteria by which the best strategies will be selected and sorted. I strongly recommend using finalScore to find the most balanced and sustainable strategies.
* `Direction': Choose which trades to test: Long, Short or Both.
Step 3: Start Testing and Work with "Parts"
The total number of unique combinations generated based on your ranges can reach tens of millions. TradingView has technical limitations on the number of calculations that the script can perform at a time. To get around this, I implemented a "Parts" system.
1. What are `Part` and `Combinations in Part'?
* `Combinations in Part': This is the number of backtests that the script performs in one run (1000 by default).
* `Part`: This is the number of the "portion" of combinations that you want to test.
2. How does it work in practice?
* After you have everything set up, leave Part:1 and wait for the results table to appear. You will see the TOP N best strategies from the first thousand tested.
* Analyze them. Then, to check the next thousand combinations, just change the Part to 2 in the settings and click OK. The script will run a test for the next batch.
* Repeat this process by increasing the Part number (`3`, 4, 5...), until you reach the last available part.
* Where can I see the total number of parts? In the information row below the results table, you will find Total parts. This will help you figure out how many more tests are left to run.
Step 4: Analyze the Results in the Table
The results table is your main decision—making tool. It displays the best strategies found, sorted by the criteria you have chosen.
1. Study the performance metrics:
* Rating: Position in the rating.
* Profit %: Net profit as a percentage of the initial capital.
* Drawdown%: The maximum drawdown of the deposit for the entire test period.
* Max Length: The maximum duration of one transaction in days, hours and minutes.
* Trades: The total number of completed trades.
2. Examine the winning parameters:
* To the right of the performance metrics are columns showing the exact settings that led to this result ('SO Count`, SO Step, TP (%), etc.).
3. How to choose the best strategy?
* Don't chase after the maximum profit! The strategy with the highest profit often has the highest drawdown, which makes it extremely risky.
* Seek a balance. The ideal strategy is a compromise between high profitability, low drawdown (Drawdown) and the maximum length of trades acceptable to you (Max Length).
* finalScore was created to find this balance. Trust him — he often highlights not the most profitable, but the most stable and reliable options.
Detailed Description Of The Settings
This section serves as a complete reference for each parameter available in the script settings. The parameters are grouped in the same way as in the indicator interface for your convenience.
Group: ⚙️ Optimization Settings ⚙️
The main parameters governing the testing process are collected here.
* `Enable Optimizer': The main switch. Activates or deactivates all backtesting functionality.
* `Direction': Determines which way trades will be opened during the simulation.
* Long: Shopping only.
* Short: Sales only.
* Both: Testing in both directions. Important: This mode only works in conjunction with an External Signal, as the script needs an external signal to determine the direction for each specific transaction.
* `Signal Mode`: Controls the conditions for starting a new trading cycle (opening a base order).
* No Signal: A new cycle starts immediately after the previous one is completed. This mode is used to test "pure" grid mechanics without reference to market conditions.
* External Signal: A new cycle begins only when a signal is received from an external indicator connected via the Signal field.
* `Signal': A field for connecting an external signal source (works only in the `External Signal` mode). You can select any other indicator on the chart.
* For Long** trades, the signal is considered received if the value of the external indicator ** is greater than 0.
* For Short** trades, the signal is considered received if the value of the external indicator ** is less than 0.
* `Bars Count': Sets the depth of the history in the bars for the backtest. The maximum value (25000) provides the most reliable results.
* `Sort by`: A key criterion for selecting and ranking the best strategies in the final table.
* FinalScore: Recommended mode. A comprehensive assessment that takes into account 9 metrics to find the most balanced and sustainable strategies.
* Profit: Sort by net profit.
* Drawdown: Sort by minimum drawdown.
* Max Length: Sort by the minimum length of the longest transaction.
* `Combinations Count': Indicates how many of the best strategies (from 1 to 50) will be displayed in the results table.
* `Close last trade`: If this option is enabled, any active trade will be forcibly closed at the closing price of the last historical bar. For grid strategies, it is recommended to always enable this option in order to get the correct calculation of the final profit and eliminate grid strategies that have been stuck for a long time.
Group: 💰 Money Management Settings 💰
The parameters in this group determine the financial conditions of the simulation. Specify values that are as close as possible to your actual values in order to get reliable results.
* `Capital': The initial deposit amount for the simulation.
* `Leverage`: The leverage used to calculate the margin.
* `Slippage` (%): Simulates the difference between the expected and actual order execution price. The specified percentage will be applied to each transaction.
* `Commission` (%): The trading commission of your exchange as a percentage. It is charged at the execution of each order (both at opening and closing).
* `Liquidation Level' (%): Maintenance Margin Ratio. This is a critical parameter for a realistic test. Liquidation in the simulator occurs if the Equity of the account (Capital + Unrealized P&L) falls below the level of the supporting margin.
Group: 🛠 Optimization Parameters 🛠
This is the "heart" of the optimizer, where you set ranges for iterating through the grid parameters.
* `Part`: The portion number of the combinations to be tested. Start with 1, and then increment (`2`, 3, ...) sequentially to check all generated strategies.
* `Combinations in Part': The number of backtests performed at a time (in one "Part"). Increasing the value may speed up the process, but it may cause the script to error due to platform limitations. If an error occurs, it is recommended to switch to the step below and back.
Three fields are available for each of the following parameters (`SO Count`, SO Step, SO Multiplier, etc.):
* `Min`: Minimum value for testing.
* `Max': The maximum value for testing.
* `Step`: The step with which the values in the range from Min to Max will be iterated over.
There is also a checkbox for each parameter. If it is enabled, the parameter will be optimized in the specified range. If disabled, only one value specified in the Min field will be used for all tests.
* 'Stop Loss': In addition to the standard settings Min, Max, Step, it has an additional parameter:
* `Type`: Defines how the stop loss price is calculated.
* From entry point: The SL level is calculated once from the entry price (base order price).
* From breakeven line: The SL level is dynamically recalculated from the average position price after each new safety order is executed.
Group: ⚡️Filters⚡️
Filters allow you to filter out those results from the final table that do not meet your minimum requirements.
For each filter (`Max Profit`, Min Drawdown, `Min Trade Length`), you can:
1. Turn it on or off using the checkbox.
2. Select the comparison condition: Greater (More) or Less (Less).
3. Set a threshold value.
*Example:* If you set Less and 20 for the Min Drawdown filter, only those strategies with a maximum drawdown of less than 20% will be included in the final table.
Group: 🎨 Visual Settings 🎨
Here you can customize the appearance of the results table.
* `Position': Selects the position of the table on the screen (for example, Bottom Left — bottom left).
* `Font Size': The size of the text in the table.
* `Header Background / Data Background`: Background colors for the header and data cells.
* `Header Font Color / Data Font Color`: Text colors for the header and data cells.
Important Notes and Limitations
So that you can use the Autoback Grid Lab as efficiently and consciously as possible, please familiarize yourself with the following key features of its work.
1. It is a Tool for Analysis, not for Signals
It is extremely important to understand that this script does not generate trading signals in real time. Its sole purpose is to conduct in—depth research (**backtesting**) on historical data.
* The results you see in the table are a report on how a particular strategy would have worked in the past.
* The script does not provide alerts and does not draw entry/exit points on the chart for the current market situation.
* Your task is to take the best sets of parameters found during optimization and use them in your real trading, for example, when setting up a trading bot or in a manual trading system.
2. Features Of Calculations (This is not a "Repainting")
You will notice that the results table appears and is updated only once — when all historical bars on the chart are loaded. It does not change in real time with each tick of the price.
This is correct and intentional behavior.:
* To test thousands, and sometimes millions of combinations, the script needs to perform a huge amount of calculations. In the Pine Script™ environment, it is technically possible to do this only once, at the very last bar in history.
* The script does not show false historical signals, which then disappear or change. It provides a static report on the results of the simulation, which remains unchanged for a specific historical period.
3. Past Results do not Guarantee Future Results.
This is the golden rule of trading, and it fully applies to the results of backtesting. Successful strategy performance in the past is not a guarantee that it will be as profitable in the future. Market conditions, volatility and trends are constantly changing.
My tool, especially when sorting by finalScore, is aimed at finding statistically stable and reliable strategies to increase the likelihood of their success in the future. However, it is a tool for managing probabilities, not a crystal ball for predicting the future. Always use proper risk management.
4. Dependence on the Quality and Depth of the Story
The reliability of the results directly depends on the quantity and quality of the historical data on which the test was conducted.
* Always strive to use the maximum number of bars available (`Bars Count: 25,000`) so that your strategy is tested on different market cycles (rise, fall, flat).
* The results obtained on data for one month may differ dramatically from the results obtained on data for two years. The longer the testing period, the higher the confidence in the parameters found.
Conclusion
The Autoback Grid Lab is your personal research laboratory, designed to replace intuitive guesses and endless manual selection of settings with a systematic, data—driven approach. Experiment with different assets, timeframes, and settings ranges to find the unique combinations that best suit your trading style.
Bullish Breakaway Dual Session-Publish-Consolidated FVG
Inspired by the FVG Concept:
This indicator is built on the Fair Value Gap (FVG) concept, with a focus on Consolidated FVG. Unlike traditional FVGs, this version only works within a defined session (e.g., ETH 18:00–17:00 or RTH 09:30–16:00).
Bullish consolidated FVG & Bullish breakaway candle
Begins when a new intraday low is printed. After that, the indicator searches for the 1st bullish breakaway candle, which must have its low above the high of the intraday low candle. Any candles in between are part of the consolidated FVG zone. Once the 1st breakaway forms, the indicator will shades the candle’s range (high to low). Then it will use this candle as an anchor to search for the 2nd, 3rd, etc. breakaways until the session ends.
Session Reset: Occurs at session close.
Repaint Behavior:
If a new intraday (or intra-session) low forms, earlier breakaway patterns are wiped, and the system restarts from the new low.
Counter:
A session-based counter at the top of the chart displays how many bullish consolidated FVGs have formed.
Settings
• Session Setup:
Choose ETH, RTH, or custom session. The indicator is designed for CME futures in New York timezone, but can be adjusted for other markets.
If nothing appears on your chart, check if you loaded it during an inactive session (e.g., weekend/Friday night).
• Max Zones to Show:
Default = 3 (recommended). You can increase, but 3 zones are usually most useful.
• Timeframe:
Best on 1m, 5m, or 15m. (If session range is big, try higher time frame)
Usage
1. Avoid Trading in Wrong Direction
• No bullish breakaway = No long trade.
• Prevents the temptation to countertrade in strong downtrends.
2. Catch the Trend Reversal
• When a bullish breakaway appears after an intraday low, it signals a potential reversal.
• You will need adjust position sizing, watch out liquidity hunt, and place stop loss.
• Best entries of your preferred choices: (this is your own trading edge)
Retest
Breakout
Engulf
MA cross over
Whatever your favorite approach
• Reversal signal is the strongest when price stays within/above the breakaway candle’s
range. Weak if it breaks below.
3. Higher Timeframe Confirmation
• 1m can give false reversals if new lows keep forming.
• 5m often provides cleaner signals and avoids premature reversals.
Failed Trade Example:
This indicator will repaint if a new intraday session low is updated. So it is possible to have a failed trade. Here is an example from the same session in 1m chart. However, if you enter the trade later at another bullish breakaway candle signal. The loss can be mitigated by the profit.
Therefore you should use smaller position size for your 1st trade. You should also considering using 5m chart to avoid 1m bull trap. In this example, if you use 5m chart, you can totally avoid this failed trade.
If you enter the trade, you will see the intraday low is stop loss hunted. You can also see the 1st bullish breakaway candle is super weak. There are a lot of candles below the breakaway candle low, so it is very possible to fail.
In the next chart, you can see the failed traded get stop loss hunted. However you can enter another trade with huge profit to win back the loss from the 1st trade if you follow the rule.
Summary
This indicator offers 3 main advantages:
1. Prevents wrong-direction trades.
2. Confirms trend entry after reversal signals.
3. Filters false positives using higher timeframes.
How to sharp your edge:
1. ⏳Extreme patience⏳: Do not guess the bottom during a downtrend before a confirmed bullish breakaway candle. If you get caught, have the courage to cut loss. This is literally the most important usage of this indicator. Again, this is the most important rule of this indicator and actually the hardest rule to follow.
2. 🛎Better Entry🛎: After a confirmed bullish breakaway, you will always have a good opportunity to enter the trade using established trading technique. Your edge will come from the position size, draw down, stop loss placement, risk/reward ratio.
3. ✂Cut loss fast✂: If you enter a trade according to the rule, but you are still not making profit for a period of time, and the price is below the low of the breakaway candle. It is very likely you may hit stop loss soon (intraday session low). It won't be a bad idea to cut loss before stop loss hit.
4. 🔂Reentry with confidence after stop loss🔂: a stop loss will not invalidate the indicator. If you see a second chance to reenter, you should still follow the trade guide and rule.
5. 🕔Time frame matter🕔: try 1m, 3m, 5m, 10m, 15m time frame. Over time, you should know what time frame work best for you and the market. Higher time frame will reduce the noise of false positive trade, but it comes with a higher stop loss placement and less max profit, however it may come with a lower draw down. Time frame will matter depending on the range of the session. If the session range is small (<0.5%), lower time frame is good. If session range is big (>1%), 5m time frame is better. Remember to wait for candle to close, if you use higher time frame.
Last Mention:
The indicator is only used for bullish side trading.
Risk & Money Calculator / Fixed Losses This indicator is designed for people who want to control their losses as precisely as possible!
It allows you to quickly calculate the potential loss on a position, taking commission into account. It's designed so that you can have a fixed loss with different stop-loss lengths by adjusting the position size, expressed in currency!
Next to the Stop Loss price, you'll see the percentage distance to the stop and the actual loss, including the double commission (for opening and closing).
The indicator is very easy to use. You select the trade direction, enter the entry price, and the Stop Loss price. Optionally, you can set a Take Profit price to visualize the profit percentage! Since commission is charged both when opening and closing a position, you need to specify the size of your one-way commission.
Important!
• DON'T FORGET ABOUT LIQUIDATION, WHICH HAPPENS BEFORE THE CORRESPONDING STOP LOSS PERCENTAGE IS REACHED!
• YOU ARE SOLELY RESPONSIBLE FOR YOUR CALCULATIONS AND LOSSES!
• IF YOU HAVE ANY WISHES OR SUGGESTIONS RELATED TO THE INDICATOR'S OPERATION, I'M READY TO LISTEN AND POSSIBLY MAKE CHANGES TO ITS FUNCTIONALITY!
Drawdown Distribution Analysis (DDA) ACADEMIC FOUNDATION AND RESEARCH BACKGROUND
The Drawdown Distribution Analysis indicator implements quantitative risk management principles, drawing upon decades of academic research in portfolio theory, behavioral finance, and statistical risk modeling. This tool provides risk assessment capabilities for traders and portfolio managers seeking to understand their current position within historical drawdown patterns.
The theoretical foundation of this indicator rests on modern portfolio theory as established by Markowitz (1952), who introduced the fundamental concepts of risk-return optimization that continue to underpin contemporary portfolio management. Sharpe (1966) later expanded this framework by developing risk-adjusted performance measures, most notably the Sharpe ratio, which remains a cornerstone of performance evaluation in financial markets.
The specific focus on drawdown analysis builds upon the work of Chekhlov, Uryasev and Zabarankin (2005), who provided the mathematical framework for incorporating drawdown measures into portfolio optimization. Their research demonstrated that traditional mean-variance optimization often fails to capture the full risk profile of investment strategies, particularly regarding sequential losses. More recent work by Goldberg and Mahmoud (2017) has brought these theoretical concepts into practical application within institutional risk management frameworks.
Value at Risk methodology, as comprehensively outlined by Jorion (2007), provides the statistical foundation for the risk measurement components of this indicator. The coherent risk measures framework developed by Artzner et al. (1999) ensures that the risk metrics employed satisfy the mathematical properties required for sound risk management decisions. Additionally, the focus on downside risk follows the framework established by Sortino and Price (1994), while the drawdown-adjusted performance measures implement concepts introduced by Young (1991).
MATHEMATICAL METHODOLOGY
The core calculation methodology centers on a peak-tracking algorithm that continuously monitors the maximum price level achieved and calculates the percentage decline from this peak. The drawdown at any time t is defined as DD(t) = (P(t) - Peak(t)) / Peak(t) × 100, where P(t) represents the asset price at time t and Peak(t) represents the running maximum price observed up to time t.
Statistical distribution analysis forms the analytical backbone of the indicator. The system calculates key percentiles using the ta.percentile_nearest_rank() function to establish the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of the historical drawdown distribution. This approach provides a complete picture of how the current drawdown compares to historical patterns.
Statistical significance assessment employs standard deviation bands at one, two, and three standard deviations from the mean, following the conventional approach where the upper band equals μ + nσ and the lower band equals μ - nσ. The Z-score calculation, defined as Z = (DD - μ) / σ, enables the identification of statistically extreme events, with thresholds set at |Z| > 2.5 for extreme drawdowns and |Z| > 3.0 for severe drawdowns, corresponding to confidence levels exceeding 99.4% and 99.7% respectively.
ADVANCED RISK METRICS
The indicator incorporates several risk-adjusted performance measures that extend beyond basic drawdown analysis. The Sharpe ratio calculation follows the standard formula Sharpe = (R - Rf) / σ, where R represents the annualized return, Rf represents the risk-free rate, and σ represents the annualized volatility. The system supports dynamic sourcing of the risk-free rate from the US 10-year Treasury yield or allows for manual specification.
The Sortino ratio addresses the limitation of the Sharpe ratio by focusing exclusively on downside risk, calculated as Sortino = (R - Rf) / σd, where σd represents the downside deviation computed using only negative returns. This measure provides a more accurate assessment of risk-adjusted performance for strategies that exhibit asymmetric return distributions.
The Calmar ratio, defined as Annual Return divided by the absolute value of Maximum Drawdown, offers a direct measure of return per unit of drawdown risk. This metric proves particularly valuable for comparing strategies or assets with different risk profiles, as it directly relates performance to the maximum historical loss experienced.
Value at Risk calculations provide quantitative estimates of potential losses at specified confidence levels. The 95% VaR corresponds to the 5th percentile of the drawdown distribution, while the 99% VaR corresponds to the 1st percentile. Conditional VaR, also known as Expected Shortfall, estimates the average loss in the worst 5% of scenarios, providing insight into tail risk that standard VaR measures may not capture.
To enable fair comparison across assets with different volatility characteristics, the indicator calculates volatility-adjusted drawdowns using the formula Adjusted DD = Raw DD / (Volatility / 20%). This normalization allows for meaningful comparison between high-volatility assets like cryptocurrencies and lower-volatility instruments like government bonds.
The Risk Efficiency Score represents a composite measure ranging from 0 to 100 that combines the Sharpe ratio and current percentile rank to provide a single metric for quick asset assessment. Higher scores indicate superior risk-adjusted performance relative to historical patterns.
COLOR SCHEMES AND VISUALIZATION
The indicator implements eight distinct color themes designed to accommodate different analytical preferences and market contexts. The EdgeTools theme employs a corporate blue palette that matches the design system used throughout the edgetools.org platform, ensuring visual consistency across analytical tools.
The Gold theme specifically targets precious metals analysis with warm tones that complement gold chart analysis, while the Quant theme provides a grayscale scheme suitable for analytical environments that prioritize clarity over aesthetic appeal. The Behavioral theme incorporates psychology-based color coding, using green to represent greed-driven market conditions and red to indicate fear-driven environments.
Additional themes include Ocean, Fire, Matrix, and Arctic schemes, each designed for specific market conditions or user preferences. All themes function effectively with both dark and light mode trading platforms, ensuring accessibility across different user interface configurations.
PRACTICAL APPLICATIONS
Asset allocation and portfolio construction represent primary use cases for this analytical framework. When comparing multiple assets such as Bitcoin, gold, and the S&P 500, traders can examine Risk Efficiency Scores to identify instruments offering superior risk-adjusted performance. The 95% VaR provides worst-case scenario comparisons, while volatility-adjusted drawdowns enable fair comparison despite varying volatility profiles.
The practical decision framework suggests that assets with Risk Efficiency Scores above 70 may be suitable for aggressive portfolio allocations, scores between 40 and 70 indicate moderate allocation potential, and scores below 40 suggest defensive positioning or avoidance. These thresholds should be adjusted based on individual risk tolerance and market conditions.
Risk management and position sizing applications utilize the current percentile rank to guide allocation decisions. When the current drawdown ranks above the 75th percentile of historical data, indicating that current conditions are better than 75% of historical periods, position increases may be warranted. Conversely, when percentile rankings fall below the 25th percentile, indicating elevated risk conditions, position reductions become advisable.
Institutional portfolio monitoring applications include hedge fund risk dashboard implementations where multiple strategies can be monitored simultaneously. Sharpe ratio tracking identifies deteriorating risk-adjusted performance across strategies, VaR monitoring ensures portfolios remain within established risk limits, and drawdown duration tracking provides valuable information for investor reporting requirements.
Market timing applications combine the statistical analysis with trend identification techniques. Strong buy signals may emerge when risk levels register as "Low" in conjunction with established uptrends, while extreme risk levels combined with downtrends may indicate exit or hedging opportunities. Z-scores exceeding 3.0 often signal statistically oversold conditions that may precede trend reversals.
STATISTICAL SIGNIFICANCE AND VALIDATION
The indicator provides 95% confidence intervals around current drawdown levels using the standard formula CI = μ ± 1.96σ. This statistical framework enables users to assess whether current conditions fall within normal market variation or represent statistically significant departures from historical patterns.
Risk level classification employs a dynamic assessment system based on percentile ranking within the historical distribution. Low risk designation applies when current drawdowns perform better than 50% of historical data, moderate risk encompasses the 25th to 50th percentile range, high risk covers the 10th to 25th percentile range, and extreme risk applies to the worst 10% of historical drawdowns.
Sample size considerations play a crucial role in statistical reliability. For daily data, the system requires a minimum of 252 trading days (approximately one year) but performs better with 500 or more observations. Weekly data analysis benefits from at least 104 weeks (two years) of history, while monthly data requires a minimum of 60 months (five years) for reliable statistical inference.
IMPLEMENTATION BEST PRACTICES
Parameter optimization should consider the specific characteristics of different asset classes. Equity analysis typically benefits from 500-day lookback periods with 21-day smoothing, while cryptocurrency analysis may employ 365-day lookback periods with 14-day smoothing to account for higher volatility patterns. Fixed income analysis often requires longer lookback periods of 756 days with 34-day smoothing to capture the lower volatility environment.
Multi-timeframe analysis provides hierarchical risk assessment capabilities. Daily timeframe analysis supports tactical risk management decisions, weekly analysis informs strategic positioning choices, and monthly analysis guides long-term allocation decisions. This hierarchical approach ensures that risk assessment occurs at appropriate temporal scales for different investment objectives.
Integration with complementary indicators enhances the analytical framework. Trend indicators such as RSI and moving averages provide directional bias context, volume analysis helps confirm the severity of drawdown conditions, and volatility measures like VIX or ATR assist in market regime identification.
ALERT SYSTEM AND AUTOMATION
The automated alert system monitors five distinct categories of risk events. Risk level changes trigger notifications when drawdowns move between risk categories, enabling proactive risk management responses. Statistical significance alerts activate when Z-scores exceed established threshold levels of 2.5 or 3.0 standard deviations.
New maximum drawdown alerts notify users when historical maximum levels are exceeded, indicating entry into uncharted risk territory. Poor risk efficiency alerts trigger when the composite risk efficiency score falls below 30, suggesting deteriorating risk-adjusted performance. Sharpe ratio decline alerts activate when risk-adjusted performance turns negative, indicating that returns no longer compensate for the risk undertaken.
TRADING STRATEGIES
Conservative risk parity strategies can be implemented by monitoring Risk Efficiency Scores across a diversified asset portfolio. Monthly rebalancing maintains equal risk contribution from each asset, with allocation reductions triggered when risk levels reach "High" status and complete exits executed when "Extreme" risk levels emerge. This approach typically results in lower overall portfolio volatility, improved risk-adjusted returns, and reduced maximum drawdown periods.
Tactical asset rotation strategies compare Risk Efficiency Scores across different asset classes to guide allocation decisions. Assets with scores exceeding 60 receive overweight allocations, while assets scoring below 40 receive underweight positions. Percentile rankings provide timing guidance for allocation adjustments, creating a systematic approach to asset allocation that responds to changing risk-return profiles.
Market timing strategies with statistical edges can be constructed by entering positions when Z-scores fall below -2.5, indicating statistically oversold conditions, and scaling out when Z-scores exceed 2.5, suggesting overbought conditions. The 95% VaR serves as a stop-loss reference point, while trend confirmation indicators provide additional validation for position entry and exit decisions.
LIMITATIONS AND CONSIDERATIONS
Several statistical limitations affect the interpretation and application of these risk measures. Historical bias represents a fundamental challenge, as past drawdown patterns may not accurately predict future risk characteristics, particularly during structural market changes or regime shifts. Sample dependence means that results can be sensitive to the selected lookback period, with shorter periods providing more responsive but potentially less stable estimates.
Market regime changes can significantly alter the statistical parameters underlying the analysis. During periods of structural market evolution, historical distributions may provide poor guidance for future expectations. Additionally, many financial assets exhibit return distributions with fat tails that deviate from normal distribution assumptions, potentially leading to underestimation of extreme event probabilities.
Practical limitations include execution risk, where theoretical signals may not translate directly into actual trading results due to factors such as slippage, timing delays, and market impact. Liquidity constraints mean that risk metrics assume perfect liquidity, which may not hold during stressed market conditions when risk management becomes most critical.
Transaction costs are not incorporated into risk-adjusted return calculations, potentially overstating the attractiveness of strategies that require frequent trading. Behavioral factors represent another limitation, as human psychology may override statistical signals, particularly during periods of extreme market stress when disciplined risk management becomes most challenging.
TECHNICAL IMPLEMENTATION
Performance optimization ensures reliable operation across different market conditions and timeframes. All technical analysis functions are extracted from conditional statements to maintain Pine Script compliance and ensure consistent execution. Memory efficiency is achieved through optimized variable scoping and array usage, while computational speed benefits from vectorized calculations where possible.
Data quality requirements include clean price data without gaps or errors that could distort distribution analysis. Sufficient historical data is essential, with a minimum of 100 bars required and 500 or more preferred for reliable statistical inference. Time alignment across related assets ensures meaningful comparison when conducting multi-asset analysis.
The configuration parameters are organized into logical groups to enhance usability. Core settings include the Distribution Analysis Period (100-2000 bars), Drawdown Smoothing Period (1-50 bars), and Price Source selection. Advanced metrics settings control risk-free rate sourcing, either from live market data or fixed rate specification, along with toggles for various risk-adjusted metric calculations.
Display options provide flexibility in visual presentation, including color theme selection from eight available schemes, automatic dark mode optimization, and control over table display, position lines, percentile bands, and standard deviation overlays. These options ensure that the indicator can be adapted to different analytical workflows and visual preferences.
CONCLUSION
The Drawdown Distribution Analysis indicator provides risk management tools for traders seeking to understand their current position within historical risk patterns. By combining established statistical methodology with practical usability features, the tool enables evidence-based risk assessment and portfolio optimization decisions.
The implementation draws upon established academic research while providing practical features that address real-world trading requirements. Dynamic risk-free rate integration ensures accurate risk-adjusted performance calculations, while multiple color schemes accommodate different analytical preferences and use cases.
Academic compliance is maintained through transparent methodology and acknowledgment of limitations. The tool implements peer-reviewed statistical techniques while clearly communicating the constraints and assumptions underlying the analysis. This approach ensures that users can make informed decisions about the appropriate application of the risk assessment framework within their broader trading and investment processes.
BIBLIOGRAPHY
Artzner, P., Delbaen, F., Eber, J.M. and Heath, D. (1999) 'Coherent Measures of Risk', Mathematical Finance, 9(3), pp. 203-228.
Chekhlov, A., Uryasev, S. and Zabarankin, M. (2005) 'Drawdown Measure in Portfolio Optimization', International Journal of Theoretical and Applied Finance, 8(1), pp. 13-58.
Goldberg, L.R. and Mahmoud, O. (2017) 'Drawdown: From Practice to Theory and Back Again', Journal of Risk Management in Financial Institutions, 10(2), pp. 140-152.
Jorion, P. (2007) Value at Risk: The New Benchmark for Managing Financial Risk. 3rd edn. New York: McGraw-Hill.
Markowitz, H. (1952) 'Portfolio Selection', Journal of Finance, 7(1), pp. 77-91.
Sharpe, W.F. (1966) 'Mutual Fund Performance', Journal of Business, 39(1), pp. 119-138.
Sortino, F.A. and Price, L.N. (1994) 'Performance Measurement in a Downside Risk Framework', Journal of Investing, 3(3), pp. 59-64.
Young, T.W. (1991) 'Calmar Ratio: A Smoother Tool', Futures, 20(1), pp. 40-42.
Buy The Dip - ENGThis script implements a grid trading strategy for long positions in the USDT market. The core idea is to place a series of buy limit orders at progressively lower prices below an initial entry point, aiming to lower the average entry price as the price drops. It then aims to exit the entire position when the price rises a certain percentage above the average entry price.
Here's a detailed breakdown:
1. Strategy Setup (`strategy` function):
`'거미줄 자동매매 250227'`: The name of the strategy.
`overlay = true`: Draws plots and labels directly on the main price chart.
`pyramiding = 15`: Allows up to 15 entries in the same direction (long). This is essential for grid trading, as it needs to open multiple buy orders.
`initial_capital = 600`: Sets the starting capital for backtesting to 600 USDT.
`currency = currency.USDT`: Specifies the account currency as USDT.
`margin_long/short = 0`: Doesn't define specific margin requirements (might imply spot trading logic or rely on exchange defaults if used live).
`calc_on_order_fills = false`: Strategy calculations happen on each bar's close, not just when orders fill.
2. Inputs (`input`):
Core Settings:
`lev`: Leverage (default 10x). Used to calculate position sizes.
`Investment Percentage %`: Percentage of total capital to allocate to the initial grid (default 80%).
`final entry Percentage %`: Percentage of the *remaining* capital (100 - `Investment Percentage %`) to use for the "semifinal" entry (default 50%). The rest goes to the "final" entry.
`Price Adjustment Length`: Lookback period (default 4 bars) to determine the initial `maxPrice`.
`price range`: The total percentage range downwards from `maxPrice` where the grid orders will be placed (default -10%, meaning 10% down).
`tp`: Take profit percentage above the average entry price (default 0.45%).
`semifinal entry price percent`: Percentage drop from `maxPrice` to trigger the "semifinal" larger entry (default -12%).
`final entry price percent`: Percentage drop from `maxPrice` to trigger the "final" larger entry (default -15%).
Rounding & Display:
`roundprice`, `round`: Decimal places for rounding price and quantity calculations.
`texts`, `label_style`: User interface preferences for text size and label appearance on the chart.
Time Filter:
`startTime`, `endTime`: Defines the date range for the backtest.
3. Calculations & Grid Setup:
`maxPrice`: The highest price point for the grid setup. Calculated as the lowest low of the previous `len` bars only if no trades are open. If trades are open, it uses the entry price of the very first order placed in the current sequence (`strategy.opentrades.entry_price(0)`).
`minPrice`: The lowest price point for the grid, calculated based on `maxPrice` and `range1`.
`totalCapital`: The amount of capital (considering leverage and `per1`) allocated for the main grid orders.
`coinRatios`: An array ` `. This defines the *relative* size ratio for each of the 11 grid orders. Later orders (at lower prices) will be progressively larger.
`totalRatio`: The sum of all ratios (66).
`positionSizes`: An array calculated based on `totalCapital` and `coinRatios`. It determines the actual quantity (size) for each of the 11 grid orders.
4. Order Placement Logic (`strategy.entry`):
Initial Grid Orders:
Runs only if within the specified time range and no position is currently open (`strategy.opentrades == 0`).
A loop places 11 limit buy orders (`Buy 1` to `Buy 11`).
Prices are calculated linearly between `maxPrice` and `minPrice`.
Order sizes are taken from the `positionSizes` array.
Semifinal & Final Entries:
Two additional, larger limit buy orders are placed simultaneously with the grid orders:
`semifinal entry`: At `maxPrice * (1 - semifinal / 100)`. Size is based on `per2`% of the capital *not* used by the main grid (`1 - per1`).
`final entry`: At `maxPrice * (1 - final / 100)`. Size is based on the remaining capital (`1 - per2`% of the unused portion).
5. Visualization (`line.new`, `label.new`, `plot`, `plotshape`, `plotchar`):
Grid Lines & Labels:
When a position is open (`strategy.opentrades > 0`), horizontal lines and labels are drawn for each of the 11 grid order prices and the "final" entry price.
Lines extend from the bar where the *first* entry occurred.
Labels show the price and planned size for each level.
Dynamic Coloring: If the price drops below a grid level, the corresponding line turns green, and the label color changes, visually indicating that the level has been reached or filled.
Plotted Lines:
`maxPrice` (initial high point for the grid).
`strategy.position_avg_price` (current average entry price of the open position, shown in red).
Target Profit Price (`strategy.position_avg_price * (1 + tp / 100)`, shown in green).
Markers:
A flag marks the `startTime`.
A rocket icon (`🚀`) appears below the bar where the `final entry` triggers.
A stop icon (`🛑`) appears below the bar where the `semifinal entry` triggers.
6. Exit Logic (`strategy.exit`, `strategy.entry` with `qty=0`):
Main Take Profit (`Full Exit`):
Uses `strategy.entry('Full Exit', strategy.short, qty = 0, limit = target2)`. This places a limit order to close the entire position (`qty=0`) at the calculated take profit level (`target2 = avgPrice * (1 + tp / 100)`). Note: Using `strategy.entry` with `strategy.short` and `qty=0` is a way to close a long position, though `strategy.exit` is often clearer. This exit seems intended to apply whenever any part of the grid position is open.
First Order Trailing Stop (`1st order Full Exit`):
Conditional: Only active if `trail` input is true AND the *last* order filled was "Buy 1" (meaning only the very first grid level was entered).
Uses `strategy.exit` with `trail_points` and `trail_offset` based on ATR values to implement a trailing stop loss/profit mechanism for this specific scenario.
This trailing stop order is cancelled (`strategy.cancel`) if any subsequent grid orders ("Buy 2", etc.) are filled.
Final/Semifinal Take Profit (`final Full Exit`):
Conditional: Only active if more than 11 entries have occurred (meaning either the "semifinal" or "final" entry must have triggered).
Uses `strategy.exit` to place a limit order to close the entire position at the take profit level (`target3 = avgPrice * (1 + tp / 100)`).
7. Information Display (Tables & UI Label):
`statsTable` (Top Right):
A comprehensive table displaying grouped information:
Market Info (Entry Point, Current Price)
Position Info (Avg Price, Target Price, Unrealized PNL $, Unrealized PNL %, Position Size, Position Value)
Strategy Performance (Realized PNL $, Realized PNL %, Initial/Total Balance, MDD, APY, Daily Profit %)
Trade Statistics (Trade Count, Wins/Losses, Win Rate, Cumulative Profit)
`buyAvgTable` (Bottom Left):
* Shows the *theoretical* entry price and average position price if trades were filled sequentially up to each `buy` level (buy1 to buy10). It uses hardcoded percentage drops (`buyper`, `avgper`) based on the initial `maxPrice` and `coinRatios`, not the dynamically changing actual average price.
`uiLabel` (Floating Label on Last Bar):
Updates only on the most recent bar (`barstate.islast`).
Provides real-time context when a position is open: Size, Avg Price, Current Price, Open PNL ($ and %), estimated % drop needed for the *next* theoretical buy (based on `ui_gridStep` input), % rise needed to hit TP, and estimated USDT profit at TP.
Shows "No Position" and basic balance/trade info otherwise.
In Summary:
This is a sophisticated long-only grid trading strategy. It aims to:
1. Define an entry range based on recent lows (`maxPrice`).
2. Place 11 scaled-in limit buy orders within a percentage range below `maxPrice`.
3. Place two additional, larger buy orders at deeper percentage drops (`semifinal`, `final`).
4. Calculate the average entry price as orders fill.
5. Exit the entire position for a small take profit (`tp`) above the average entry price.
6. Offer a conditional ATR trailing stop if only the first order fills.
7. Provide extensive visual feedback through lines, labels, icons, and detailed information tables/UI elements.
Keep in mind that grid strategies can perform well in ranging or slowly trending markets but can incur significant drawdowns if the price trends strongly against the position without sufficient retracements to hit the take profit. The leverage (`lev`) input significantly amplifies both potential profits and losses.
Risk Guardian Pro📊 Risk Guardian Pro - Complete Script Summary
🎯 Overview
Risk Guardian Pro is a comprehensive Pine Script indicator for advanced trading risk management. It provides real-time position sizing, risk calculation, fee tracking, and profit/loss analysis with intelligent profit stop detection.
⚙️ Settings Sections
1. Account Settings
2. Risk Management
3. Stop Loss Settings
4. Trading Fees Settings
5. Display Settings
6. Table Row Toggles
7. Position Settings
🧮 Core Calculations
Risk Analysis
Position Risk
Risk Percentage
Risk:Reward Ratio
Fee Calculations
Trading Fee: Position Size × Leverage × Trading Fee %
Funding Rate: Full Margin × Funding Rate % × 8-Hour Periods
Total Fees: Trading Fee + Accumulated Funding Fees
P&L Calculations
Gross P&L: Position Size × Leverage × Price Movement / Entry Price
Net P&L: Gross P&L - Total Fees
Adjusted Risk/Reward: All targets include fee impact
🎨 Visual Display System
Risk Management Table
Header: "RISK GUARDIAN PRO" with version info
Status Bar: Position direction + Risk heat level with color coding
Main Metrics (11 configurable rows):
Account Balance: Total trading capital
Position Size: Trade allocation with size warnings
Risk %: Percentage at risk with progress bars
Risk:Reward: Current ratio with quality indicators
Stop Loss: Exit price with profit stop intelligence
Take Profit: Target price for profits
Trading Fee: One-time entry cost
Funding Rate: 8-hour accumulation with auto-timer
Total Fees: Complete cost breakdown
Potential Loss: Maximum risk including fees
Potential Win: Profit target minus fees
Chart Elements
Entry Line: Blue horizontal line at entry price
Stop Loss Line: Red line (turns green for profit stops)
Take Profit Line: Green horizontal line
Live P&L Plot: Real-time profit/loss line
Price Labels
Entry Label: "ENTRY" - Blue box
Stop Label: "STOP or "PROFIT STOP"
"PROFITSTOP" - Red/Green
Target Label: "TARGET" - Green box
Live P&L Label: "NET P&L" - Dynamic colour
🧠 Intelligent Features
Profit Stop Detection
Automatically detects when stop loss moves into profit territory:
Long Position: Stop above entry price
Short Position: Stop below entry price
Smart Transformations:
Status → "PROFIT SECURED" 🔒
Stop Loss → "Profit Stop" (green)
Risk % → "Secured Profit %"
Risk:Reward → "Secured:Additional"
Chart elements turn green
Risk Heat Levels
LOW: <2% risk (Green indicators)
MODERATE: 2-3% risk (Yellow indicators)
HIGH: >3% risk (Red indicators)
PROFIT SECURED: Stop in profit (Dark green)
Dynamic Color Coding
Table borders change based on risk level
Position size warnings for oversized trades
Progress bars show risk proximity to limits
All elements adapt to profit stop status
⏰ Time & Fee Management
Automatic Fee Accumulation
Set trade entry time using calendar picker
Funding fees automatically increase every 8 hours
Real-time progression as chart time advances
Visual feedback shows elapsed time and periods
Fee Impact Integration
All P&L calculations include fee deductions
Risk calculations add fee costs to potential loss
Take profit targets account for fee impact
Breakeven point automatically adjusts for fees
🚨 Alert System
Risk Alerts
High Risk: When risk exceeds 3% of capital
High Fees: When fees exceed 10% of position risk
Long Hold: Position held over 10 days
Extended Trade: Trade duration review reminder
Profit Alerts
Profit Secured: Stop loss moved to profit territory
🎛️ Customization Options
Visual Customization
2 Currency options with proper symbols
9 Table positions around chart
4 Size options for table and labels
200-bar range for label positioning
Progress bar toggles for risk visualization
Data Display Control
11 Individual row toggles for table content
Percentage displays in chart labels
Emoji options for visual enhancement
Tooltip information on hover
💡 Key Benefits
Professional Risk Management
✅ Comprehensive fee tracking with real-time accumulation
✅ Intelligent profit stop detection with visual adaptation
✅ Multi-method stop loss (ATR, percentage, manual)
✅ Advanced R:R calculations with fee impact
User Experience
✅ Date/time picker for precise entry timing
✅ Real-time updates as market moves
✅ Visual risk indicators with progress bars
✅ Extensive customization for personal preferences
Trading Integration
✅ Works with any timeframe and instrument
✅ Suitable for long-term use on charts
✅ Accurate fee modeling for realistic P&L
✅ Professional alert system for risk management
🎯 Perfect For
Active traders needing precise risk management
Position traders with long-term holds
Leveraged trading with fee-conscious strategies
Professional risk assessment and portfolio management
Risk Guardian Pro V2.5 represents a complete, professional-grade risk management solution that adapts intelligently to your trading style while providing comprehensive oversight of costs, risks, and profit potential.
Easy Position Size Calculator with Fees# Easy Position Size Calculator with Fees - Manual
## Overview
The Easy Position Size Calculator is a Pine Script indicator designed to help traders calculate the optimal position size for their trades while accounting for trading fees. This tool automatically determines whether you're planning a long or short position and calculates the exact position size needed to risk a specific dollar amount.
## Key Features
- **Automatic Trade Direction Detection**: Determines if you're going long or short based on entry price vs stop loss
- **Fee Integration**: Accounts for trading fees in position size calculations
- **Risk Management**: Calculates position size based on your specified risk amount
- **Risk Factor Adjustment**: Allows you to scale your position size up or down
- **Visual Display**: Shows all calculations in a clear, organized table
## Input Parameters
### Entry Price ($)
- **Purpose**: The price at which you plan to enter the trade
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
### Stop Loss ($)
- **Purpose**: The price at which you will exit the trade if it goes against you
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
### Risk ($)
- **Purpose**: The maximum dollar amount you're willing to lose on this trade
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
### Risk Factor
- **Purpose**: A multiplier to scale your position size up or down
- **Default**: 1.0 (no scaling)
- **Range**: 0.0 to 10.0
- **Step**: 0.1
- **Examples**:
- 1.0 = Normal position size
- 2.0 = Double the position size
- 0.5 = Half the position size
### Fee (%)
- **Purpose**: The percentage fee charged per transaction (buy/sell)
- **Default**: 0.01% (0.01)
- **Range**: 0.0% to 1.0%
- **Step**: 0.001
## How It Works
### Trade Direction Detection
The script automatically determines your trade direction:
- **Long Trade**: Entry price > Stop loss price
- **Short Trade**: Entry price < Stop loss price
### Position Size Calculation
#### For Long Trades:
```
Position Size = -Risk Factor × Risk Amount / (Stop Loss × (1 - Fee) - Entry Price × (1 + Fee))
```
#### For Short Trades:
```
Position Size = -Risk Factor × Risk Amount / (Entry Price × (1 - Fee) - Stop Loss × (1 + Fee))
```
### Fee Adjustment
The script accounts for fees on both entry and exit:
- **Long trades**: You pay fees when buying (entry) and selling (exit)
- **Short trades**: You pay fees when shorting (entry) and covering (exit)
## Output Display
The indicator displays a table with the following information:
### Trade Information
- **Trade Type**: Shows whether it's a LONG, SHORT, or INVALID trade
- **Entry Price**: Your specified entry price
- **Stop Loss**: Your specified stop loss price
- **Fee (%)**: The fee percentage being used
### Risk Parameters
- **Risk Amount**: The dollar amount you're willing to risk
- **Risk Factor**: The multiplier being applied
### Calculated Values
- **Effective Entry**: The actual cost per share including fees
- **Effective Exit**: The actual exit value per share including fees
- **Expected Loss**: The calculated loss if stop loss is hit
- **Deviation from Risk %**: Shows how close the expected loss is to your target risk
- **Position Size**: The number of shares/units to trade
## Usage Examples
### Example 1: Long Trade
- Entry Price: $100.00
- Stop Loss: $95.00
- Risk Amount: $500.00
- Risk Factor: 1.0
- Fee: 0.01%
**Result**: The script will calculate how many shares to buy so that if the stop loss is hit, you lose approximately $500 (accounting for fees). Position Size: 99.61152
### Example 2: Short Trade
- Entry Price: $50.00
- Stop Loss: $55.00
- Risk Amount: $300.00
- Risk Factor: 1.0
- Fee: 0.01%
**Result**: The script will calculate how many shares to short so that if the stop loss is hit, you lose approximately $300 (accounting for fees). Position Size: 59.87426
## Important Notes
### Validation Requirements
For the script to work properly, all of the following must be true:
- Entry price > 0
- Stop loss > 0
- Risk amount > 0
- Entry price ≠ Stop loss (to determine direction)
### Negative Position Sizes
The script may show negative position sizes, which is normal:
- **Negative values for long trades**: Represents shares to buy
- **Negative values for short trades**: Represents shares to short
### Risk Deviation
The "Deviation from Risk %" shows how closely the calculated position size matches your target risk. Small deviations are normal due to:
- Fee calculations
- Rounding
- Market precision
## Color Coding
The table uses color coding for easy identification:
- **Green**: Long trade information
- **Red**: Short trade information
- **Gray**: Invalid trade (when inputs are incorrect)
- **Blue**: Final position size
- **Red background**: Risk-related calculations
## Troubleshooting
### Common Issues
1. **Position Size shows 0**
- Check that all inputs are greater than 0
- Ensure entry price is different from stop loss
2. **Trade Type shows INVALID**
- Verify that entry price and stop loss are both positive
- Make sure entry price ≠ stop loss
3. **Large Risk Deviation**
- This is normal for very small position sizes
- Consider adjusting your risk amount or price levels
## Best Practices
1. **Always validate your inputs** before placing actual trades
2. **Double-check the trade direction** shown in the table
3. **Review the expected loss** to ensure it aligns with your risk management
4. **Consider the effective entry/exit prices** which include fees
5. **Use appropriate risk factors** - avoid extreme values that could lead to overexposure
## Disclaimer
This tool is for educational and planning purposes only. Always verify calculations manually and consider market conditions, liquidity, and other factors before placing actual trades. The script assumes that fees are charged on both entry and exit transactions.
Lorentzian Classification - Advanced Trading DashboardLorentzian Classification - Relativistic Market Analysis
A Journey from Theory to Trading Reality
What began as fascination with Einstein's relativity and Lorentzian geometry has evolved into a practical trading tool that bridges theoretical physics and market dynamics. This indicator represents months of wrestling with complex mathematical concepts, debugging intricate algorithms, and transforming abstract theory into actionable trading signals.
The Theoretical Foundation
Lorentzian Distance in Market Space
Traditional Euclidean distance treats all feature differences equally, but markets don't behave uniformly. Lorentzian distance, borrowed from spacetime geometry, provides a more nuanced similarity measure:
d(x,y) = Σ ln(1 + |xi - yi|)
This logarithmic formulation naturally handles:
Scale invariance: Large price moves don't overwhelm small but significant patterns
Outlier robustness: Extreme values are dampened rather than dominating
Non-linear relationships: Captures market behavior better than linear metrics
K-Nearest Neighbors with Relativistic Weighting
The algorithm searches historical market states for patterns similar to current conditions. Each neighbor receives weight inversely proportional to its Lorentzian distance:
w = 1 / (1 + distance)
This creates a "gravitational" effect where closer patterns have stronger influence on predictions.
The Implementation Challenge
Creating meaningful market features required extensive experimentation:
Price Features: Multi-timeframe momentum (1, 2, 3, 5, 8 bar lookbacks) Volume Features: Relative volume analysis against 20-period average
Volatility Features: ATR and Bollinger Band width normalization Momentum Features: RSI deviation from neutral and MACD/price ratio
Each feature undergoes min-max normalization to ensure equal weighting in distance calculations.
The Prediction Mechanism
For each current market state:
Feature Vector Construction: 12-dimensional representation of market conditions
Historical Search: Scan lookback period for similar patterns using Lorentzian distance
Neighbor Selection: Identify K nearest historical matches
Outcome Analysis: Examine what happened N bars after each match
Weighted Prediction: Combine outcomes using distance-based weights
Confidence Calculation: Measure agreement between neighbors
Technical Hurdles Overcome
Array Management: Complex indexing to prevent look-ahead bias
Distance Calculations: Optimizing nested loops for performance
Memory Constraints: Balancing lookback depth with computational limits
Signal Filtering: Preventing clustering of identical signals
Advanced Dashboard System
Main Control Panel
The primary dashboard provides real-time market intelligence:
Signal Status: Current prediction with confidence percentage
Neighbor Analysis: How many historical patterns match current conditions
Market Regime: Trend strength, volatility, and volume analysis
Temporal Context: Real-time updates with timestamp
Performance Analytics
Comprehensive tracking system monitors:
Win Rate: Percentage of successful predictions
Signal Count: Total predictions generated
Streak Analysis: Current winning/losing sequence
Drawdown Monitoring: Maximum equity decline
Sharpe Approximation: Risk-adjusted performance estimate
Risk Assessment Panel
Multi-dimensional risk analysis:
RSI Positioning: Overbought/oversold conditions
ATR Percentage: Current volatility relative to price
Bollinger Position: Price location within volatility bands
MACD Alignment: Momentum confirmation
Confidence Heatmap
Visual representation of prediction reliability:
Historical Confidence: Last 10 periods of prediction certainty
Strength Analysis: Magnitude of prediction values over time
Pattern Recognition: Color-coded confidence levels for quick assessment
Input Parameters Deep Dive
Core Algorithm Settings
K Nearest Neighbors (1-20): More neighbors create smoother but less responsive signals. Optimal range 5-8 for most markets.
Historical Lookback (50-500): Deeper history improves pattern recognition but reduces adaptability. 100-200 bars optimal for most timeframes.
Feature Window (5-30): Longer windows capture more context but reduce sensitivity. Match to your trading timeframe.
Feature Selection
Price Changes: Essential for momentum and reversal detection Volume Profile: Critical for institutional activity recognition Volatility Measures: Key for regime change detection Momentum Indicators: Vital for trend confirmation
Signal Generation
Prediction Horizon (1-20): How far ahead to predict. Shorter horizons for scalping, longer for swing trading.
Signal Threshold (0.5-0.9): Confidence required for signal generation. Higher values reduce false signals but may miss opportunities.
Smoothing (1-10): EMA applied to raw predictions. More smoothing reduces noise but increases lag.
Visual Design Philosophy
Color Themes
Professional: Corporate blue/red for institutional environments Neon: Cyberpunk cyan/magenta for modern aesthetics
Matrix: Green/red hacker-inspired palette Classic: Traditional trading colors
Information Hierarchy
The dashboard system prioritizes information by importance:
Primary Signals: Largest, most prominent display
Confidence Metrics: Secondary but clearly visible
Supporting Data: Detailed but unobtrusive
Historical Context: Available but not distracting
Trading Applications
Signal Interpretation
Long Signals: Prediction > threshold with high confidence
Look for volume confirmation
- Check trend alignment
- Verify support levels
Short Signals: Prediction < -threshold with high confidence
Confirm with resistance levels
- Check for distribution patterns
- Verify momentum divergence
- Market Regime Adaptation
Trending Markets: Higher confidence in directional signals
Ranging Markets: Focus on reversal signals at extremes
Volatile Markets: Require higher confidence thresholds
Low Volume: Reduce position sizes, increase caution
Risk Management Integration
Confidence-Based Sizing: Larger positions for higher confidence signals
Regime-Aware Stops: Wider stops in volatile regimes
Multi-Timeframe Confirmation: Align signals across timeframes
Volume Confirmation: Require volume support for major signals
Originality and Innovation
This indicator represents genuine innovation in several areas:
Mathematical Approach
First application of Lorentzian geometry to market pattern recognition. Unlike Euclidean-based systems, this naturally handles market non-linearities.
Feature Engineering
Sophisticated multi-dimensional feature space combining price, volume, volatility, and momentum in normalized form.
Visualization System
Professional-grade dashboard system providing comprehensive market intelligence in intuitive format.
Performance Tracking
Real-time performance analytics typically found only in institutional trading systems.
Development Journey
Creating this indicator involved overcoming numerous technical challenges:
Mathematical Complexity: Translating theoretical concepts into practical code
Performance Optimization: Balancing accuracy with computational efficiency
User Interface Design: Making complex data accessible and actionable
Signal Quality: Filtering noise while maintaining responsiveness
The result is a tool that brings institutional-grade analytics to individual traders while maintaining the theoretical rigor of its mathematical foundation.
Best Practices
- Parameter Optimization
- Start with default settings and adjust based on:
Market Characteristics: Volatile vs. stable
Trading Timeframe: Scalping vs. swing trading
Risk Tolerance: Conservative vs. aggressive
Signal Confirmation
Never trade on Lorentzian signals alone:
Price Action: Confirm with support/resistance
Volume: Verify with volume analysis
Multiple Timeframes: Check higher timeframe alignment
Market Context: Consider overall market conditions
Risk Management
Position Sizing: Scale with confidence levels
Stop Losses: Adapt to market volatility
Profit Targets: Based on historical performance
Maximum Risk: Never exceed 2-3% per trade
Disclaimer
This indicator is for educational and research purposes only. It does not constitute financial advice or guarantee profitable trading results. The Lorentzian classification system reveals market patterns but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose.
Market dynamics are inherently uncertain, and past performance does not guarantee future results. This tool should be used as part of a comprehensive trading strategy, not as a standalone solution.
Bringing the elegance of relativistic geometry to market analysis through sophisticated pattern recognition and intuitive visualization.
Thank you for sharing the idea. You're more than a follower, you're a leader!
@vasanthgautham1221
Trade with precision. Trade with insight.
— Dskyz , for DAFE Trading Systems
Risk Calculator PRO — manual lot size + auto lot-suggestionWhy risk management?
90 % of traders blow up because they size positions emotionally. This tool forces Risk-First Thinking: choose the amount you’re willing to lose, and the script reverse-engineers everything else.
Key features
1. Manual or Market Entry – click “Use current price” or type a custom entry.
2. Setup-based ₹-Risk – four presets (A/B/C/D). Edit to your workflow.
3. Lot-Size Input + Auto Lot Suggestion – you tell the contract size ⇒ script tells you how many lots.
4. Auto-SL (optional) – tick to push stop-loss to exactly 1-lot risk.
5. Instant Targets – 1 : 2, 1 : 3, 1 : 4, 1 : 5 plotted and alert-ready.
6. P&L Preview – table shows potential profit at each R-multiple plus real ₹ at SL.
7. Margin Column – enter per-lot margin once; script totals it for any size.
8. Clean Table UI – dark/light friendly; updates every 5 bars.
9. Alert Pack – SL, each target, plus copy-paste journal line on the chart.
How to use
1. Add to chart > “Format”.
2. Type the lot size for the symbol (e.g., 1250 for Natural Gas, 1 for cash equity).
3. Pick Side (Buy / Sell) & Setup grade.
4. ✅ If you want the script to place SL for you, tick Auto-SL (risk = 1 lot).
5. Otherwise type your own Stop-loss.
6. Read the table:
• Suggested lots = how many to trade so risk ≤ setup ₹.
• Risk (currency) = real money lost if SL hits.
7. Set TradingView alerts on the built-in conditions (T1_2, SL_hit, etc.) if you’d like push / email.
8. Copy the orange CSV label to Excel / Sheets for journalling.
Best practices
• Never raise risk to “fit” a trade. Lower size instead.
• Review win-rate vs. R multiple monthly; adjust setups A–D accordingly.
• Test Auto-SL in replay before going live.
Disclaimer
This script is educational. Past performance ≠ future results. The author isn’t responsible for trading losses.
Reverse Keltner Channel StrategyReverse Keltner Channel Strategy
Overview
The Reverse Keltner Channel Strategy is a mean-reversion trading system that capitalizes on price movements between Keltner Channels. Unlike traditional Keltner Channel strategies that trade breakouts, this system takes the contrarian approach by entering positions when price returns to the channel after overextending.
Strategy Logic
Long Entry Conditions:
Price crosses above the lower Keltner Channel from below
This signals a potential reversal after an oversold condition
Position is entered at market price upon signal confirmation
Long Exit Conditions:
Take Profit: Price reaches the upper Keltner Channel
Stop Loss: Placed at half the channel width below entry price
Short Entry Conditions:
Price crosses below the upper Keltner Channel from above
This signals a potential reversal after an overbought condition
Position is entered at market price upon signal confirmation
Short Exit Conditions:
Take Profit: Price reaches the lower Keltner Channel
Stop Loss: Placed at half the channel width above entry price
Key Features
Mean Reversion Approach: Takes advantage of price tendency to return to mean after extreme moves
Adaptive Stop Loss: Stop loss dynamically adjusts based on market volatility via ATR
Visual Signals: Entry points clearly marked with directional triangles
Fully Customizable: All parameters can be adjusted to fit various market conditions
Customizable Parameters
Keltner EMA Length: Controls the responsiveness of the channel (default: 20)
ATR Multiplier: Determines channel width/sensitivity (default: 2.0)
ATR Length: Affects volatility calculation period (default: 10)
Stop Loss Factor: Adjusts risk management aggressiveness (default: 0.5)
Best Used On
This strategy performs well on:
Currency pairs with defined ranging behavior
Commodities that show cyclical price movements
Higher timeframes (4H, Daily) for more reliable signals
Markets with moderate volatility
Risk Management
The built-in stop loss mechanism automatically adjusts to market conditions by calculating position risk relative to the current channel width. This approach ensures that risk remains proportional to potential reward across varying market conditions.
Notes for Optimization
Consider adjusting the EMA length and ATR multiplier based on the specific asset and timeframe:
Lower values increase sensitivity and generate more signals
Higher values produce fewer but potentially more reliable signals
As with any trading strategy, thorough backtesting is recommended before live implementation.
Past performance is not indicative of future results. Always practice sound risk management.
