Support and resistance levels (Day, Week, Month) + EMAs + SMAs(ENG): This Pine 5 script provides various tools for configuring and displaying different support and resistance levels, as well as moving averages (EMA and SMA) on charts. Using these tools is an essential strategy for determining entry and exit points in trades.
Support and Resistance Levels
Daily, weekly, and monthly support and resistance levels play a key role in analyzing price movements:
Daily levels: Represent prices where a cryptocurrency has tended to bounce within the current trading day.
Weekly levels: Reflect strong prices that hold throughout the week.
Monthly levels: Indicate the most significant levels that can influence price movement over the month.
When trading cryptocurrencies, traders use these levels to make decisions about entering or exiting positions. For example, if a cryptocurrency approaches a weekly resistance level and fails to break through it, this may signal a sell opportunity. If the price reaches a daily support level and starts to bounce up, it may indicate a potential long position.
Market context and trading volumes are also important when analyzing support and resistance levels. High volume near a level can confirm its significance and the likelihood of subsequent price movement. Traders often combine analysis across different time frames to get a more complete picture and improve the accuracy of their trading decisions.
Moving Averages
Moving averages (EMA and SMA) are another important tool in the technical analysis of cryptocurrencies:
EMA (Exponential Moving Average): Gives more weight to recent prices, allowing it to respond more quickly to price changes.
SMA (Simple Moving Average): Equally considers all prices over a given period.
Key types of moving averages used by traders:
EMA 50 and 200: Often used to identify trends. The crossing of the 50-day EMA with the 200-day EMA is called a "golden cross" (buy signal) or a "death cross" (sell signal).
SMA 50, 100, 150, and 200: These periods are often used to determine long-term trends and support/resistance levels. Similar to the EMA, the crossings of these averages can signal potential trend changes.
Settings Groups:
EMA Golden Cross & Death Cross: A setting to display the "golden cross" and "death cross" for the EMA.
EMA 50 & 200: A setting to display the 50-day and 200-day EMA.
Support and Resistance Levels: Includes settings for daily, weekly, and monthly levels.
SMA 50, 100, 150, 200: A setting to display the 50, 100, 150, and 200-day SMA.
SMA Golden Cross & Death Cross: A setting to display the "golden cross" and "death cross" for the SMA.
Components:
Enable/disable the display of support and resistance levels.
Show level labels.
Parameters for adjusting offset, display of EMA and SMA, and their time intervals.
Parameters for configuring EMA and SMA Golden Cross & Death Cross.
EMA Parameters:
Enable/disable the display of 50 and 200-day EMA.
Color and style settings for EMA.
Options to use bar gaps and the "LookAhead" function.
SMA Parameters:
Enable/disable the display of 50, 100, 150, and 200-day SMA.
Color and style settings for SMA.
Options to use bar gaps and the "LookAhead" function.
Effective use of support and resistance levels, as well as moving averages, requires an understanding of technical analysis, discipline, and the ability to adapt the strategy according to changing market conditions.
(RUS) Данный Pine 5 скрипт предоставляет разнообразные инструменты для настройки и отображения различных уровней поддержки и сопротивления, а также скользящих средних (EMA и SMA) на графиках. Использование этих инструментов является важной стратегией для определения точек входа и выхода из сделок.
Уровни поддержки и сопротивления
Дневные, недельные и месячные уровни поддержки и сопротивления играют ключевую роль в анализе движения цен:
Дневные уровни: Представляют собой цены, на которых криптовалюта имела тенденцию отскакивать в течение текущего торгового дня.
Недельные уровни: Отражают сильные цены, которые сохраняются в течение недели.
Месячные уровни: Указывают на наиболее значимые уровни, которые могут влиять на движение цены в течение месяца.
При торговле криптовалютами трейдеры используют эти уровни для принятия решений о входе в позицию или закрытии сделки. Например, если криптовалюта приближается к недельному уровню сопротивления и не удается его преодолеть, это может стать сигналом для продажи. Если цена достигает дневного уровня поддержки и начинает отскакивать вверх, это может указывать на возможность открытия длинной позиции.
Контекст рынка и объемы торговли также важны при анализе уровней поддержки и сопротивления. Высокий объем при приближении к уровню может подтвердить его значимость и вероятность последующего движения цены. Трейдеры часто комбинируют анализ различных временных рамок для получения более полной картины и улучшения точности своих торговых решений.
Скользящие средние
Скользящие средние (EMA и SMA) являются еще одним важным инструментом в техническом анализе криптовалют:
EMA (Exponential Moving Average): Экспоненциальная скользящая средняя, которая придает большее значение последним ценам. Это позволяет более быстро реагировать на изменения в ценах.
SMA (Simple Moving Average): Простая скользящая средняя, которая равномерно учитывает все цены в заданном периоде.
Основные виды скользящих средних, которые используются трейдерами:
EMA 50 и 200: Часто используются для выявления трендов. Пересечение 50-дневной EMA с 200-дневной EMA называется "золотым крестом" (сигнал на покупку) или "крестом смерти" (сигнал на продажу).
SMA 50, 100, 150 и 200: Эти периоды часто используются для определения долгосрочных трендов и уровней поддержки/сопротивления. Аналогично EMA, пересечения этих средних могут сигнализировать о возможных изменениях тренда.
Группы настроек:
EMA Golden Cross & Death Cross: Настройка для отображения "золотого креста" и "креста смерти" для EMA.
EMA 50 & 200: Настройка для отображения 50-дневной и 200-дневной EMA.
Уровни поддержки и сопротивления: Включает настройки для дневных, недельных и месячных уровней.
SMA 50, 100, 150, 200: Настройка для отображения 50, 100, 150 и 200-дневных SMA.
SMA Golden Cross & Death Cross: Настройка для отображения "золотого креста" и "креста смерти" для SMA.
Компоненты:
Включение/отключение отображения уровней поддержки и сопротивления.
Показ ярлыков уровней.
Параметры для настройки смещения, отображения EMA и SMA, а также их временных интервалов.
Параметры для настройки EMA и SMA Golden Cross & Death Cross.
Параметры EMA:
Включение/отключение отображения 50 и 200-дневных EMA.
Настройки цвета и стиля для EMA.
Опции для использования разрыва баров и функции "LookAhead".
Параметры SMA:
Включение/отключение отображения 50, 100, 150 и 200-дневных SMA.
Настройки цвета и стиля для SMA.
Опции для использования разрыва баров и функции "LookAhead".
Эффективное использование уровней поддержки и сопротивления, а также скользящих средних, требует понимания технического анализа, дисциплины и умения адаптировать стратегию в зависимости от изменяющихся условий рынка.
Średnie kroczące
Improved Volume Based Indicator# Improved Volume Based Indicator
## Overview
The Improved Volume Based Indicator is a technical analysis tool designed to identify potential trading opportunities based on volume patterns, price action, and trend direction. This indicator combines volume analysis with moving averages and the Average True Range (ATR) to generate buy and sell signals.
## Key Components
1. Volume Analysis
- Tracks consecutive volume direction (up or down) for 3 periods
- Calculates volume ratio compared to a short-term moving average
2. Trend Direction
- Uses a 200-period Exponential Moving Average (EMA) to determine overall trend
3. Volatility Measurement
- Incorporates the Average True Range (ATR) for stop-loss and take-profit calculations
## Signal Generation
### Buy Signal Criteria
1. Three consecutive periods of up volume (close > open)
2. Volume ratio > 1.5 (current volume is 50% higher than the short-term average)
3. Current price is above the 200 EMA
### Sell Signal Criteria
1. Three consecutive periods of down volume (close < open)
2. Volume ratio > 1.5 (current volume is 50% higher than the short-term average)
3. Current price is below the 200 EMA
## Risk Management
The indicator calculates stop-loss and take-profit levels based on the ATR:
- Stop Loss: ATR * 1.5 (default)
- Take Profit: ATR * 2.5 (default)
These levels are adjustable through input parameters.
## Usage
1. Add the indicator to your chart
2. Adjust input parameters as needed:
- Volume Period (2-5)
- ATR Period (default 14)
- ATR Multipliers for Stop Loss and Take Profit
- EMA Period (default 200)
3. Monitor for buy and sell signals
4. Use the provided stop-loss and take-profit levels for risk management
## Interpretation
- Buy signals suggest potential upward price movement
- Sell signals suggest potential downward price movement
- Always consider other factors and perform additional analysis before making trading decisions
## Limitations
- This indicator may generate false signals in choppy or ranging markets
- It's best used in conjunction with other technical analysis tools and fundamental analysis
- Past performance does not guarantee future results
Remember to thoroughly test this indicator on historical data and in various market conditions before using it in live trading.
---
# 改進的基於交易量的指標
＃＃ 概述
改進的基於成交量的指標是一種技術分析工具，旨在根據成交量模式、價格行為和趨勢方向識別潛在的交易機會。此指標將成交量分析與移動平均線和平均真實波動幅度 (ATR) 結合起來，以產生買入和賣出訊號。
＃＃ 關鍵部件
1. 成交量分析
- 追蹤 3 個週期的連續成交量方向（向上或向下）
- 計算與短期移動平均線相比的成交量比率
2. 趨勢方向
- 使用 200 週期指數移動平均線 (EMA) 來確定整體趨勢
3. 波動率測量
- 納入平均真實波動範圍 (ATR) 以進行停損和停盈計算
## 訊號生成
### 購買訊號標準
1. 連續三個週期的成交量上漲（收盤>開盤）
2.成交量比率>1.5（目前成交量較短期平均高50%）
3. 當前價格高於200 EMA
### 賣出訊號標準
1.連續三個週期的成交量下跌（收盤<開盤）
2.成交量比率>1.5（目前成交量較短期平均高50%）
3. 目前價格低於200 EMA
＃＃ 風險管理
此指標根據 ATR 計算停損和止盈水準：
- 停損：ATR * 1.5（預設）
- 止盈：ATR * 2.5（預設）
這些等級可透過輸入參數進行調整。
＃＃ 用法
1. 將指標加入您的圖表中
2. 根據需要調整輸入參數：
- 卷期 (2-5)
- ATR 週期（預設 14）
- 用於停損和止盈的 ATR 乘數
- EMA 週期（預設 200）
3. 監控買賣訊號
4. 使用提供的停損和停利水準進行風險管理
＃＃ 解釋
- 買進訊號表示價格可能上漲
- 賣出訊號表示價格可能下跌
- 在做出交易決策之前始終考慮其他因素並進行額外分析
## 限制
- 此指標可能會在波動或波動的市場中產生錯誤訊號
- 最好與其他技術分析工具和基本面分析結合使用
- 過去的表現並不能保證未來的結果
請記住，在實際交易中使用該指標之前，請根據歷史數據和各種市場條件徹底測試該指標。
Multi-Timeframe Trend IndicatorMulti-Timeframe Trend Indicator
The “Multi-Timeframe Trend Indicator” is a versatile tool designed to help traders identify trends across multiple timeframes using Exponential Moving Averages (EMAs). This indicator is suitable for both novice and experienced traders. It allows users to customize the lengths of the short and long EMAs, providing a clear visualization of the trend direction (UP, DOWN, SIDE) for various intervals including 1 minute, 5 minutes, 15 minutes, 30 minutes, 1 hour, and 4 hours. The indicator offers extensive customization options, enabling adjustments for table position, colors, and more to suit individual trading preferences.
How the Calculation Works
The Multi-Timeframe Trend Indicator uses EMAs to calculate trends. EMAs give more weight to recent prices, making them responsive to new information. The short EMA, calculated over a shorter period, reacts quickly to price changes, while the long EMA, calculated over a longer period, smooths out fluctuations to show the overall trend.
For each timeframe, the indicator calculates both the short EMA and the long EMA. If the short EMA is above the long EMA, the trend is considered “UP”. If the short EMA is below the long EMA, the trend is “DOWN”. If the absolute difference between the short and long EMAs is within a user-defined threshold, the trend is classified as “SIDE” (sideways).
This calculation is repeated for multiple timeframes: 1 minute, 5 minutes, 15 minutes, 30 minutes, 1 hour, and 4 hours. The results are displayed in a table, providing a comprehensive view of the trend direction across different timeframes.
How the Code Works
Input Parameters: Users can input the lengths of the short and long EMAs and the threshold for identifying sideways trends. These inputs allow for a high degree of customization to match individual trading strategies.
Trend Calculation Function: The trend function calculates the trend direction based on the EMAs. It uses the math.abs function to find the absolute difference between the EMAs and determines if the trend is “UP”, “DOWN”, or “SIDE” based on the threshold.
Requesting Data for Multiple Timeframes: The script uses the request.security function to fetch price data and calculate the EMAs for different timeframes independently of the current chart timeframe. This ensures consistency in trend analysis regardless of the displayed timeframe.
Creating and Updating the Table: A table is created to display the trend directions for each timeframe. The table’s position and appearance can be customized. The trend data for each timeframe is color-coded (green for UP, red for DOWN, gray for SIDE) and displayed in the table.
Customization Options: Users can customize the colors, table position, and EMA lengths through the indicator settings, providing flexibility to adapt the indicator to their trading style.
Disclaimer
This indicator is for informational purposes only and should not be considered financial advice. It does not predict future price movements and does not guarantee accurate trend calculations, as market conditions can vary. Trading involves substantial risk and is not suitable for everyone. Always conduct your own research before making any trading decisions.
VWMA Multiple TimeframesVWMA Multiple Timeframes Indicator
This TradingView indicator plots the Volume Weighted Moving Average (VWMA) across multiple timeframes on your chart. The VWMA is a type of moving average that gives more weight to periods with higher volume, making it a valuable tool for traders who want to incorporate volume into their technical analysis.
Features:
Multi-timeframe Analysis: This indicator calculates and plots the VWMA on five different timeframes:
Weekly (W)
Daily (D)
4 Hours (240 minutes)
1 Hour (60 minutes)
15 Minutes
Visual Representation: Each timeframe's VWMA is plotted with a different color, making it easy to distinguish between them on the chart:
Weekly VWMA: Gray
Daily VWMA: Blue
4 Hours VWMA: Red
1 Hour VWMA: Green
15 Minutes VWMA: Purple
How to Use:
Trend Identification: Use the VWMA to identify the direction of the trend on different timeframes. For example, if the VWMA is trending upwards on multiple timeframes, it indicates a strong upward trend.
Support and Resistance: The VWMA can act as dynamic support or resistance levels. Price bouncing off a VWMA line might indicate a continuation of the trend.
Volume Confirmation: The VWMA considers volume, making it useful for confirming the strength of price movements. High volume moves that cause the VWMA to change direction can be more significant than low volume moves.
This indicator is ideal for traders who use multi-timeframe analysis and want to incorporate volume into their trend and support/resistance identification. Feel free to customize the periods and timeframes to suit your trading style.
Normalized Hull Moving Average Oscillator w/ ConfigurationsThis indicator uniquely uses normalization techniques applied to the Hull Moving Average (HMA) and allows the user to choose between a number of different types of normalization, each with their own advantages. This indicator is one in a series of experiments I've been working on in looking at different methods of transforming data. In particular, this is a more usable example of the power of data transformation, as it takes the Hull Moving Average of Alan Hull and turns it into a powerful oscillating indicator.
The indicator offers multiple types of normalization, each with its own set of benefits and drawbacks. My personal favorites are the Mean Normalization , which turns the data series into one centered around 0, and the Quantile Transformation , which converts the data into a data set that is normally distributed.
I've also included the option of showing the mean, median, and mode of the data over the period specified by the length of normalization. Using this will allow you to gather additional insights into how these transformations affect the distribution of the data series.
Types of Normalization:
1. Z-Score
Overview: Standardizes the data by subtracting the mean and dividing by the standard deviation.
Benefits: Centers the data around 0 with a standard deviation of 1, reducing the impact of outliers.
Disadvantages: Works best on data that is normally distributed
Notes: Best used with a mid-longer length of transformation.
2. Min-Max
Overview: Scales the data to fit within a specified range, typically 0 to 1.
Benefits: Simple and fast to compute, preserves the relationships among data points.
Disadvantages: Sensitive to outliers, which can skew the normalization.
Notes: Best used with mid-longer length of transformation.
3. Mean Normalization
Overview: Subtracts the mean and divides by the range (max - min).
Benefits: Centers data around 0, making it easier to compare different datasets.
Disadvantages: Can be affected by outliers, which influence the range.
Notes: Best used with a mid-longer length of transformation.
4. Max Abs Scaler
Overview: Scales each feature by its maximum absolute value.
Benefits: Retains sparsity and is robust to large outliers.
Disadvantages: Only shifts data to the range , which might not always be desirable.
Notes: Best used with a mid-longer length of transformation.
5. Robust Scaler
Overview: Uses the median and the interquartile range for scaling.
Benefits: Robust to outliers, does not shift data as much as other methods.
Disadvantages: May not perform well with small datasets.
Notes: Best used with a longer length of transformation.
6. Feature Scaling to Unit Norm
Overview: Scales data such that the norm (magnitude) of each feature is 1.
Benefits: Useful for models that rely on the magnitude of feature vectors.
Disadvantages: Sensitive to outliers, which can disproportionately affect the norm. Not normally used in this context, though it provides some interesting transformations.
Notes: Best used with a shorter length of transformation.
7. Logistic Function
Overview: Applies the logistic function to squash data into the range .
Benefits: Smoothly compresses extreme values, handling skewed distributions well.
Disadvantages: May not preserve the relative distances between data points as effectively.
Notes: Best used with a shorter length of transformation. This feature is actually two layered, we first put it through the mean normalization to ensure that it's generally centered around 0.
8. Quantile Transformation
Overview: Maps data to a uniform or normal distribution using quantiles.
Benefits: Makes data follow a specified distribution, useful for non-linear scaling.
Disadvantages: Can distort relationships between features, computationally expensive.
Notes: Best used with a very long length of transformation.
Conclusion
This indicator is a powerful example into how normalization can alter and improve the usability of a data series. Each method offers unique insights and benefits, making this indicator a useful tool for any trader. Try it out, and don't hesitate to reach out if you notice any glaring flaws in the script, room for improvement, or if you just have questions.
No Lag SupertrendNo Lag Supertrend indicator improves upon the original supertrend by incorporating calculation methods that enhance responsiveness and accuracy. Traditional supertrend indicators often suffer from lag, which can delay signals and affect trading decisions. No Lag Supertrend addresses this issue through the use of KAMA (Kaufman’s Adaptive Moving Average) and Hull ATR (Average True Range) calculations.
Goals of No Lag Supertrend:
- Lag reduction: one of the main issues with traditional supertrend indicators is their lag, which can result in delayed entry and exit signals. By integrating KAMA and Hull ATR, the no lag supertrend minimizes this delay, providing more timely signals.
- Market Noise Filtering: The combined use of KAMA and Hull ATR effectively filters out market noise, ensuring that signals are based on significant price movements rather than minor fluctuations.
- Consistency Across Different Market Conditions: The adaptive nature of KAMA and the smooth responsiveness of Hull ATR ensure that the No Lag Supertrend performs consistently across various market conditions, from trending to volatile markets.
Credits: This code is based on the TradingView supertrend but improved the ATR calculations.
Perfect Order Alert USDJPY/BTCUSD/XAUUSDPerfect Order Alert USDJPY/BTCUSD/XAUUSD 日本語解説は下記
This indicator detects the perfect order of three moving averages and displays on the Panel in an easy-to-understand visual manner whether there is an uptrend, downtrend, or non-trend for each time leg.
This indicator detects perfect orders for the three currency pairs USDJPY/BTCUSD/XAUUSD on the 5-minute, 15-minute, 1-hour, and 4-hour time frames, and displays them on the Panel on the chart, with “▲” for up, “▼” for down, and “ー” for non-trend, so that you can quickly determine the trend. The panel is displayed on the chart.
In order to check for perfect orders without missing them, it is also possible to set up alerts that notify you of all the time frames and currency pairs as well.
Functions
Displaying 4H, 1H, 15M, 5M, up (▲), down (▼), other (-), of USDJPY/BTCUSD/XAUUSD on the panel.
*(By default, 20EMA, 75EMA, and 200EMA are hidden.)
Display position setting of the panel (You can choose from upper left, upper top, upper right, lower left, lower bottom, or lower right).
Panel color and text color change function
The moving average line can be hidden by default.
Moving average period change
Moving average color and thickness can be changed.
EMA/SMA switchable
Alert function - One alert can be set for each currency pair and time frame ▲▼, which is very useful.
Perfect Order Alert
You can use it even if you have a free account with only one alert setting.
To use the alert function, go to the Tradingview default alert settings, select “USDJPY/BTCUSD/XAUUSD” for the top item of conditions, and select “Call Alert() function” in the frame just below it!
_* Supplementary explanation: ＿＿＿＿＿＿＿＿＿＿＿＿
Please note that due to the limitation of the script, only 3 currency pairs and 4 time frames are displayed with 12 items (Panels for currency pairs other than USDJPY/BTCUSD/XAUUSD are also created, but they are indicators for other scripts, so if you are interested in other currency pairs, please use those. If you are interested in other currency pairs, please use them.)
Please note that we may change the functions or delete the indicator itself without prior notice.
Translated with DeepL.com (free version)
Reference image of the setting screenReference image of the setting screen
設定画面参考画像
3本の移動平均線のパーフェクトオーダーを検知し、時間足ごとに上昇トレンドか下降トレンドかノントレンドかを視覚的にわかりやすくPanelに表示するインジゲーターです。
このインジゲーターは、USDJPY/BTCUSD/XAUUSDの３通貨ペアの５分足、15分足、１時間足、４時間足のパーフェクトオーダーを検知して、チャートに表示されるPanelに、上昇は「▲」下降は「▼」ノントレンドは「ー」と、すぐに判断できる表示にしてあります。
パーフェクトオーダーを逃さずチェックできるように、それぞれの時間足や通貨ペアも全てを通知してくれるアラート設定が可能なのも特徴です。
機能紹介
・USDJPY/BTCUSD/XAUUSDの4H,1H,15M,5M,の上昇(▲),下降(▼),その他(-),をパネルに表示
※(デフォルトでは20EMA,75EMA,200EMAの３本で非表示にしてあります）
・パネルの表示位置設定（左上、上、右上、左下、下、右下、から選択できます。）
・パネルの色とテキスト色変更機能
・移動平均線表示非表示機能（デフォルトでは表示OFFにしてあります。）
・移動平均線期間変更
・移動平均線色と太さ変更
・EMA/SMA切り替え可能
・アラート機能ー１つのアラート設定で通貨ペアと時間足▲▼一つ一つを細かく教えてくれるので便利。
※パーフェクト オーダーアラート
無料アカウントで１つしかアラート設定できなくても使えます。
アラート機能はTradingviewデフォルトのアラート設定から、条件の一番上の項目を「USDJPY/BTCUSD/XAUUSD」選択、そのすぐ下の枠に「Alert()関数の呼び出し」を選択でOK！
＿※ 補足説明＿＿＿＿＿＿＿＿＿＿＿＿
・スクリプトの制限の為、３通貨ペアと４つの時間足の１２項目で表示させていますのでご了承ください
(USDJPY/BTCUSD/XAUUSD以外の通貨ペアのPanelも作成していますが別スクリプトのインジゲーターになりますので他の通貨ペアも興味がある方はそちらをお使いください）
・予告なしで機能の変更やインジゲーター自体の削除等行う事もあるかもなのでご了承ください。
SOL & BTC EMA with BTC/SOL Price Difference % and BTC Dom EMAThis script is designed to provide traders with a comprehensive analysis of Solana (SOL) and Bitcoin (BTC) by incorporating Exponential Moving Averages (EMAs) and price difference percentages. It also includes the BTC Dominance EMA to offer insights into the overall market dominance of Bitcoin.
Features:
SOL EMA: Plots the Exponential Moving Average (EMA) for Solana (SOL) based on a customizable period length.
BTC EMA: Plots the Exponential Moving Average (EMA) for Bitcoin (BTC) based on a customizable period length.
BTC Dominance EMA: Plots the Exponential Moving Average (EMA) for BTC Dominance, which helps in understanding Bitcoin's market share relative to other cryptocurrencies.
BTC/SOL Price Difference %: Calculates and plots the percentage difference between BTC and SOL prices, adjusted for their respective EMAs. This helps in identifying relative strength or weakness between the two assets.
Background Highlight: Colors the background to visually indicate whether the BTC/SOL price difference percentage is positive (green) or negative (red), aiding in quick decision-making.
Inputs:
SOL Ticker: Symbol for Solana (default: BINANCE
).
BTC Ticker: Symbol for Bitcoin (default: BINANCE
).
BTC Dominance Ticker: Symbol for Bitcoin Dominance (default: CRYPTOCAP
.D).
EMA Length: The length of the EMA (default: 20 periods).
Usage:
This script is intended for traders looking to analyze the relationship between SOL and BTC, using EMAs to smooth out price data and highlight trends. The BTC/SOL price difference percentage can help traders identify potential trading opportunities based on the relative movements of SOL and BTC.
Note: Leverage trading involves significant risk and may not be suitable for all investors. Ensure you have a good understanding of the market conditions and employ proper risk management techniques.
Trend DetectorThe Trend Detector indicator is a powerful tool to help traders identify and visualize market trends with ease. This indicator uses multiple moving averages (MAs) of different timeframes to provide a comprehensive view of market trends, making it suitable for traders of all experience levels.
█ USAGE
This indicator will automatically plot the chosen moving averages (MAs) on your chart, allowing you to visually assess the trend direction. Additionally, a table displaying the trend data for each selected MA timeframe is included to provide a quick overview.
█ FEATURES
1. Customizable Moving Averages: The indicator supports various types of moving averages, including Simple (SMA) , Exponential (EMA) , Smoothed (RMA) , Weighted (WMA) , and Volume-Weighted (VWMA) . You can select the type and length for each MA.
2. Multiple Timeframes: Plot moving averages for different timeframes on a single chart, including fast (short-term) , mid (medium-term) , and slow (long-term) MAs.
3. Trend Detector Table: A customizable table displays the trend direction (Up or Down) for each selected MA timeframe, providing a quick and easy way to assess the market's overall trend.
4. Customizable Appearance: Adjust the colors, frame, border, and text of the Trend Detector Table to match your chart's style and preferences.
5. Wait for Timeframe Close: Option to wait until the selected timeframe closes to plot the MA, which will remove the gaps.
█ CONCLUSION
The Trend Detector indicator is a versatile and user-friendly tool designed to enhance your trading strategy. By providing a clear visualization of market trends across multiple timeframes, this indicator helps you make informed trading decisions with confidence and trade with the market trend. Whether you're a day trader or a long-term investor, this indicator is an essential addition to your trading toolkit.
█ IMPORTANT
This indicator is a tool to aid in your analysis and should not be used as the sole basis for trading decisions. It is recommended to use this indicator in conjunction with other tools and perform comprehensive market analysis before making any trades.
Happy trading!
T3 [RATE OF CHANGE] by SKiNNiEHDeveloped by Tim Tillson, the Tilson Moving Average (T3) is a trend indicator with the advantage of having less lag than other ones. That is, a faster moving average. The T3 moving average is an "indicator of an indicator" as it includes several EMAs of another EMA. Unlike other moving averages, the t3 adds the so-called volume factor, a value between 0 and 1.
The T3 RATE OF CHANGE by SKiNNiEH is a unique indicator that integrates the T3 moving average with a normalized Rate of Change (RoC) calculation. Unlike traditional T3 moving averages, this indicator provides additional smoothing modes (SINGLE, DOUBLE & TRIPLE) for the T3, whilst enhancing visual feedback of the plotted line by generating a dynamic line thickness, a dynamic line color & brightness and trade entry bars, offering traders a more dynamic view of market conditions without going "overboard" with settings.
How It Works
Visualization
The T3 line varies in thickness and color based on the RoC values, giving traders visual cues about market strength and direction.
Thicker and brighter lines indicate stronger trends, while thinner and duller lines suggest weaker trends.
Rate of Change Filte r
This filter refines trend detection by using the line thickness measurement.
Adjustable from 0 (disabled) to 4, where higher settings only consider stronger trends for signals.
The T3 line turns gray when the filter is triggered or when the RoC is extremely low, signaling a weak or neutral market.
T3 Calculation (mode)
SINGLE
The T3 calculation is applied once to the closing price.
This mode has the least smoothing effect and the least lag. It reacts more quickly to price changes but is less smooth.
DOUBLE
The T3 calculation is applied twice sequentially.
The first T3 calculation smooths the closing price.
The second T3 calculation smooths the result of the first T3 calculation.
This mode provides more smoothing and introduces more lag compared to SINGLE mode. It is smoother but reacts slower to price changes.
TRIPLE
The T3 calculation is applied three times sequentially.
The first T3 calculation smooths the closing price.
The second T3 calculation smooths the result of the first T3 calculation.
The third T3 calculation smooths the result of the second T3 calculation.
This mode provides the most smoothing and introduces the most lag by reacting the slowest to price changes.
Rate of Change (RoC) Calculation
The script calculates the Rate of Change (RoC) for the T3 values based on the selected mode (SINGLE, DOUBLE, TRIPLE). The RoC measures the percentage change between the most recent value and a value in the past. The measurement is then normalized in three different ranges.
Normalization 5: Determines T3 line thickness on a scale from 0 - 5
Normalization 10: Determines T3 color brightness on a scale from 0 - 10
Normalization 100: Determines Rate of Change percentage
Rate of Change Filter
The script uses the RoC filter to refine the trend detection logic. By using the line thickness measurement, a filter can be enabled by setting this input on 1 - 4. As an example, setting this to 4 means that only a line thickness of 5 would be considered for a trade signal. Setting this to 0 disables the filter. The T3 line will turn gray when the filter is triggered, the T3 line can also turn gray without the filter, when the Rate of Change is extremely low.
Trade Signals
A trade signal is printed as a vertical green or red bar when the following conditions are met:
Long:
Closing price is above the T3 line
Rate of Change percentage is above 0
Previous trade signal was a short signal **
Rate of Change is not filtered
Short:
Closing price is below the T3 line
Rate of Change percentage is below 0
Previous trade signal was a long signal **
Rate of Change is not filtered
** Or this is the very first recorded trade signal
It should be noted that the trade signals in this script are trade entry signals, not trade exit signals. Use at your own risk.
Instructions for Use
Setting Up the Indicator
Apply the indicator to your trading chart.
Choose the desired T3 mode (SINGLE, DOUBLE, TRIPLE) based on your need for smoothing and lag.
Set the desired length (lookback period).
Set the desired factor between 0 and 1 (increments of 0.1)
Choose an overall line thickness and brightness that suits your screen and taste preferences.
Apply the Rate of Change filter. Setting this to 0 will disable the filter
Tip: use the trade entry vertical bars as a visual calibration tool the adjust mode, length, factor and filter.
Interpreting Visual Cues
Observe the T3 line's thickness: thicker lines indicate stronger trends, while thinner lines suggest weaker trends.
Observe the T3 line's color and color brightness: green indicates a more bullish trend, while red indicates a more bearish trend. A brighter color suggest a stronger trend. A gray color means the RoC is very low / neutral, or the RoC filter is active.
Observe the T3 line's location relative to price: below price indicates a more bullish trend, above price indicates a more bearish trend. The T3 line distance from price can also be an indication of trend strength.
Observe vertical bars: a vertical bar is printed green when long conditions are met, a vertical bar is printed red when short conditions are met. See the rules that explain the trigger for this bar above.
Alerts
Go to the settings tab, set the condition to T3.RoC.S + LONG or SHORT.
Enter an alert name and message.
Configure your notification preferences in the notifications tab and create the alert
Notifications-tab: Choose your notification preferences
Create the alert.
EMA Cross Fibonacci Entry with RetracementThe EMA Cross Fibonacci Entry with Retracement is a trading strategy that combines two popular technical analysis tools: Exponential Moving Averages (EMAs) and Fibonacci retracement levels. Here's a brief overview of how this strategy typically works:
### Exponential Moving Averages (EMAs)
1. **EMAs Calculation**: EMAs give more weight to recent price data, making them more responsive to price changes. Commonly used periods for EMAs in this strategy are the 50-period and 200-period EMAs.
2. **EMA Cross**: The strategy looks for a "golden cross" (short-term EMA crosses above the long-term EMA) as a potential buy signal, and a "death cross" (short-term EMA crosses below the long-term EMA) as a potential sell signal.
### Fibonacci Retracement Levels
1. **Fibonacci Retracement**: This tool is used to identify potential support and resistance levels based on the Fibonacci sequence. The key retracement levels are 23.6%, 38.2%, 50%, 61.8%, and 78.6%.
2. **Drawing Retracement Levels**: Traders draw Fibonacci retracement levels from a significant peak to a significant trough (or vice versa) to identify potential retracement levels where the price might reverse.
### Combining EMA Cross with Fibonacci Retracement
1. **Identify EMA Cross**: First, traders look for an EMA cross. For example, a golden cross where a shorter EMA (e.g., 50 EMA) crosses above a longer EMA (e.g., 200 EMA) suggests a bullish trend.
2. **Wait for Retracement**: After identifying a cross, traders wait for the price to retrace to a Fibonacci level. The key levels to watch are 38.2%, 50%, and 61.8%.
3. **Entry Point**: The entry point is when the price retraces to a Fibonacci level and shows signs of reversal (e.g., bullish candlestick patterns, support at Fibonacci levels). This is typically when traders enter a long position.
4. **Confirmation with EMA**: Ensure that the EMAs support the trend. For a buy entry, the short-term EMA should remain above the long-term EMA.
### Example of a Bullish Entry
1. **Golden Cross**: 50 EMA crosses above 200 EMA.
2. **Retracement**: Price retraces to the 38.2% Fibonacci level.
3. **Entry Signal**: At the 38.2% level, a bullish candlestick pattern (e.g., hammer) forms, indicating potential support.
4. **Entry Point**: Enter a long position at the close of the bullish candlestick.
### Risk Management
1. **Stop Loss**: Place a stop loss below the next Fibonacci retracement level or below the recent swing low to limit potential losses.
2. **Take Profit**: Set a take profit target based on a risk-reward ratio, previous resistance levels, or further Fibonacci extensions.
### Conclusion
The EMA Cross Fibonacci Entry with Retracement strategy is a systematic approach to identifying entry points in a trending market. By combining the responsiveness of EMAs with the predictive power of Fibonacci retracement levels, traders aim to enter trades at optimal points, increasing their chances of success while managing risk effectively.
Fractalyst Moving Average [Adaptive] | FractalystWhat's the indicator purpose and functionality?
Moving averages are widely used technical indicators in trading.
Typically, they provide reliable entry signals in trending markets but can falter during consolidation periods.
Now, imagine a moving average that adjusts to market conditions.
The Fractalyst Moving Average does just that by adapting to the market's noise level, which is the erratic price movement within trends or consolidation phases.
This indicator incorporates market structure into moving averages to more effectively identify potential market trends.
By dynamically calculating moving averages based on external swing highs and lows, it offers robust trend identification and adapts to different market conditions, giving traders valuable insights into current market condition.
------
How does FRMA react in a trending and consolidating market?
When the market trends, the FRMA adjusts quickly to price movements, closely tracking the trend and positioning itself close to prices. This responsiveness allows it to provide timely signals and effectively capture trends.
However, in consolidating markets where there is little net change in price over time, the FRMA reacts slowly. As consolidation prolongs, the FRMA may even cease to move significantly, appearing non-reactive. This characteristic helps minimize false signals and unnecessary trades during periods of market indecision.
Notice how the FRMA tracks prices closely when the market is trending. When the market begins to consolidate, however, the FRMA becomes relatively unresponsive and stays horizontal.
------
What are the underlying calculations behind FRMA?
Identifying Swing Highs and Lows: FRMA begins by identifying the most recent external swing highs and lows, which are key pivot points in the market's price structure.
Defining Market Structure: It calculates the distance between these external swing levels. When price remains confined between these levels, indicating a horizontal market, it signifies minor intermediate ranges or a lack of clear trend direction.
Adapting to Breaks of Structure: When a new break of structure occurs—such as a significant price movement above a previous swing high or below a swing low—the FRMA updates dynamically.
It adjusts its values to reflect the midpoint (50%) of the distance between the external swing highs and lows.
This adjustment helps the FRMA react promptly to changes in different market environments.
------
How to use the FRMA in trading?
In a trend-following context, the FRMA provides clear signals for trading:
Buying Signal: Look to buy when the FRMA is rising. This indicates that the market is in an uptrend, with prices consistently moving higher. Buying at these points aligns with the trend momentum and increases the likelihood of capturing profitable movements.
Selling Signal: Consider selling when the FRMA is falling. A declining FRMA suggests that the market is in a downtrend, where prices are consistently decreasing. Selling during these periods helps capitalize on downward movements and potential profit-taking opportunities.
Avoiding Trades: Avoid trading when the FRMA appears horizontal and the market is consolidating. This indicates a lack of clear trend direction or significant price movement, which can lead to choppy price action and increased risk of false signals. Waiting for the FRMA to resume a clear trend direction can help avoid unnecessary losses in consolidating markets.
Note: These rules are just examples and may generate numerous false signals. Even when the FRMA is less responsive, it can exhibit frequent changes in direction.
Traders should apply additional filters or confirmatory indicators to refine their trading decisions and mitigate the impact of false signals.
Depending on whether they're employing mean-reversion or trend-following trading styles, traders need to adjust other market filters accordingly.
It's crucial to conduct thorough backtesting using various market conditions and filters to validate and optimize their trading strategies effectively.
This process helps traders identify the settings that best align with their trading goals and market conditions.
------
What makes this moving average unique compared to others?
Yes, it's another moving average, but the Fractalyst Adaptive Moving Average stands out for a compelling reason.
Its calculation is more sophisticated, leveraging market structure to identify potential consolidation and trending environments, similar to conventional moving averages such as SMA and EMA.
------
How does the FRMA's stack up against the other moving averages?
Since markets are always evolving, using adaptive strategy elements like the FRMA certainly makes a whole lot of sense.
However, from a practical standpoint, the only way to find out would be to exhaustively backtest the various moving averages across all markets of interest.
Establishing equivalency between the FRMA and other moving averages may be a little challenging, since the FRMA does not use a single integer value for its lookback period.
Assuming the backtests produced roughly equal results, I’d personally prefer to use the FRMA. Its adaptive qualities give me confidence that the strategy can weather changing market conditions.
------
User-inputs and customizations
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Multiple Non-Linear Regression [ChartPrime]This indicator is designed to perform multiple non-linear regression analysis using four independent variables: close, open, high, and low prices. Here's a breakdown of its components and functionalities:
Inputs:
Users can adjust several parameters:
Normalization Data Length: Length of data used for normalization.
Learning Rate: Rate at which the algorithm learns from errors.
Smooth?: Option to smooth the output.
Smooth Length: Length of smoothing if enabled.
Define start coefficients: Initial coefficients for the regression equation.
Data Normalization:
The script normalizes input data to a range between 0 and 1 using the highest and lowest values within a specified length.
Non-linear Regression:
It calculates the regression equation using the input coefficients and normalized data. The equation used is a weighted sum of the independent variables, with coefficients adjusted iteratively using gradient descent to minimize errors.
Error Calculation:
The script computes the error between the actual and predicted values.
Gradient Descent: The coefficients are updated iteratively using gradient descent to minimize the error.
// Compute the predicted values using the non-linear regression function
predictedValues = nonLinearRegression(x_1, x_2, x_3, x_4, b1, b2, b3, b4)
// Compute the error
error = errorModule(initial_val, predictedValues)
// Update the coefficients using gradient descent
b1 := b1 - (learningRate * (error * x_1))
b2 := b2 - (learningRate * (error * x_2))
b3 := b3 - (learningRate * (error * x_3))
b4 := b4 - (learningRate * (error * x_4))
Visualization:
Plotting of normalized input data (close, open, high, low).
The indicator provides visualization of normalized data values (close, open, high, low) in the form of circular markers on the chart, allowing users to easily observe the relative positions of these values in relation to each other and the regression line.
Plotting of the regression line.
Color gradient on the regression line based on its value and bar colors.
Display of normalized input data and predicted value in a table.
Signals for crossovers with a midline (0.5).
Interpretation:
Users can interpret the regression line and its crossovers with the midline (0.5) as signals for potential buy or sell opportunities.
This indicator helps users analyze the relationship between multiple variables and make trading decisions based on the regression analysis. Adjusting the coefficients and parameters can fine-tune the model's performance according to specific market conditions.
Moving Average Crossover Swing StrategyMoving Average Crossover Swing Strategy
**Overview:**
The basic concept of this strategy is to generate a signal when a faster/shorter length moving average crosses over (for Longs) or crosses under (for Shorts) a medium/longer length moving average. All of which are customizable. This strategy can work on any timeframe, however the daily is the timeframe used for the default settings and screenshots, as it was designed to be a multi-day swing strategy. Once a signal has been confirmed with a candle close, based on user options, the strategy will enter the trade on the open of the next candle.
The crossover strategy is nothing new to trading, but what can make this strategy unique and helpful, is the addition of further confirmation points, ATR based stop loss and take profit targets, optional early exit criteria, customizable to your needs and style, and just about everything visual can be toggled on/off. This strategy is based on a Trend (MA) indicator and a Momentum (MACD) indicator. While a Volume-based indicator is not shown here, one could consider using their favorite from that category to further compliment the signal idea.
It should be noted that depending on the time frame, direction(s) chosen, the signal options, confirmation options, and exit options selected, that a ticker may not produce more than 100 trades on the back test. Depending on your style and frequency, one could consider adjusting options and/or testing multiple tickers. It should also be noted that this strategy simply tests the underlying stock prices, not options contracts. And of course, testing this strategy against historical data does not assume that the same results will occur in future price action.
Shoutout given to Ripster's Clouds Indicator as pieces of that code were taken and modified to create both the Cloud visualization effects, and the Moving Average Pair Plots that are implemented in this strategy.
BASIC DEFAULTS
All can be changed as normal
Initial capital = 10,000
Order Sizing = 25% of equity (use the "Inputs" tab to modify this)
Pyramiding = 0
Commission = 0.65 USD per order
Price Verification = 1 tick
Slippage = 1 tick
RISK MANAGMENT
You will notice two different percentage options and ATR multipliers. This strategy will adjust position sizing by not exceeding either one of those % values based on the ATR (Average True Range) of the symbol and the multipliers selected, should the stock hit the stop loss price.
For Example, lets assume these values are true:
Account size = $10,000,
Max Risk = 1% of account size
Max Position Size = 25% of the account size
Stock Price = 23.45
ATR = 3.5
ATR Stop Loss Multiplier = 1.4
Then the formulas would be:
ACCT_SIZE * MaxRisk_% = 10000 * .01 = $100 (MaxCashRisk)
-----
MaxCashRisk / (ATR * ATR_SL_MULTIPLIER) = 100 / (3.5 * 1.4) = 20.4 Shares based on Max Cash Risk
-----
(ACCT_SIZE * MaxEquity_%) / STOCK_PRICE = (10000 * .25) / 23.45 = 106.61 Shares based on Max Equity Allocation
The minimum value of each of those options is then used, which in this case would be to purchase 20 shares so as not to exceed the max dollar risk should the stock reach the stop loss target. Likewise, if the ATR were to be much lower, say 0.48 cents, and all else the same, then the strategy would purchase the 106 shares based on Max Equity Allocation because the Max Cash Risk would require 149.25 shares.
MOVING AVERAGE OPTIONS
Select between and change the length & type of up to 5 pairs (10 total) of moving averages
The "Show Cloud-x" option will display a fill color between the "a" and "b" pairs
All moving averages lines can be toggled on/off in the "Style" tab, as well as adjusting their colors.
Visualization features do not affect calculations, meaning you could have all or nothing on the chart and the strategy will still produce results
SIGNAL CHOICES
Choose the fast/shorter length MA and the medium/longer length MA to determine the entry signal
CONFIRMATION OPTIONS
Both of these have customizable values and can be toggled on/off
A candle close over a slower/much longer length moving average
An additional cross-over (cross-under for Shorts) on the MACD indicator using default MACD values. While the MACD indicator is not necessary to have on the chart, it can help to add that for visualization. The calculations will perform whether the indicator is on the chart or not.
EARLY EXIT CRITERIA
Both can be toggled on/off with customizable values
MA Cross Exit will exit the trade early if the select moving averages cross-under (for longs) or cross-over (for shorts), indicating a potential reversal.
Max Bars in Trades will act as a last-resort exit by simply calculating the amount of full bars the trade has been open, and exiting on the opening of the next bar. For example: the default value is 8 bars, so after 8 full bars in the trade, if no other exit has been triggered (Stop Loss, Take Profit, or MA Cross(if enabled)), then the trade will exit at the opening of the 9th bar.
Finally, there is a table displaying the amount of trades taken for each side, and the amount & percent of both early exits. This table can be turned off in the "Style" tab
ADDITIONAL PLOTS
MACD (Moving Average Convergence/Divergence):
- The MACD is an optional confirmation indicator for this strategy.
- Plotting the indicator is not necessary for the strategy to work, but it can be helpful to visually see the status and position of the MACD if this feature is enabled in the strategy
- This helps to identify if there is also momentum behind the entry signal
AlgoBuilder [Mean-Reversion] | FractalystWhat's the strategy's purpose and functionality?
This strategy is designed for both traders and investors looking to rely and trade based on historical and backtested data using automation.
The main goal is to build profitable mean-reversion strategies that outperform the underlying asset in terms of returns while minimizing drawdown.
For example, as for a benchmark, if the S&P 500 (SPX) has achieved an estimated 10% annual return with a maximum drawdown of -57% over the past 20 years, using this strategy with different entry and exit techniques, users can potentially seek ways to achieve a higher Compound Annual Growth Rate (CAGR) while maintaining a lower maximum drawdown.
Although the strategy can be applied to all markets and timeframes, it is most effective on stocks, indices, future markets, cryptocurrencies, and commodities and JPY currency pairs given their trending behaviors.
In trending market conditions, the strategy employs a combination of moving averages and diverse entry models to identify and capitalize on upward market movements. It integrates market structure-based moving averages and bands mechanisms across different timeframes and provides exit techniques, including percentage-based and risk-reward (RR) based take profit levels.
Additionally, the strategy has also a feature that includes a built-in probability function for traders who want to implement probabilities right into their trading strategies.
Performance summary, weekly, and monthly tables enable quick visualization of performance metrics like net profit, maximum drawdown, profit factor, average trade, average risk-reward ratio (RR), and more.
This aids optimization to meet specific goals and risk tolerance levels effectively.
-----
How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
Trading:
1. Trading:
- Designed for traders looking to capitalize on bullish trending markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for active trading with a focus on mean-reversion and risk per trade approach.
◓: Mode | %: Risk percentage per trade
3. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully investing in the asset during bullish conditions.
- ◓: Mode | %: Risk not applied (In investing mode, the strategy uses 10% of equity to buy the asset)
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What's is FRMA? How does the triple bands work? What are the underlying calculations?
Middle Band (FRMA):
The middle band is the core of the FRMA system. It represents the Fractalyst Moving Average, calculated by identifying the most recent external swing highs and lows in the market structure.
By determining these external swing pivot points, which act as significant highs and lows within the market range, the FRMA provides a unique moving average that adapts to market structure changes.
Upper Band:
The upper band shows the average price of the most recent external swing highs.
External swing highs are identified as the highest points between pivot points in the market structure.
This band helps traders identify potential overbought conditions when prices approach or exceed this upper band.
Lower Band:
The lower band shows the average price of the most recent external swing lows.
External swing lows are identified as the lowest points between pivot points in the market structure.
The script utilizes this band to identify potential oversold conditions, triggering entry signals as prices approach or drop below the lower band.
Adjustments Based on User Inputs:
Users can adjust how the upper and lower bands are calculated based on their preferences:
Upper/Lower: This method calculates the average bands using the prices of external swing highs and lows identified in the market.
Percentage Deviation from FRMA: Alternatively, users can opt to calculate the bands based on a percentage deviation from the middle FRMA. This approach provides flexibility to adjust the width of the bands relative to market conditions and volatility.
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What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
This comparison filter can be turned on (>) or off.
For example, you can set the filter so that MA#1 > MA#2, meaning the first moving average must be above the second one before the script looks for entry conditions. This adds an extra layer of trend confirmation, ensuring that trades are only taken in more favorable market conditions.
⍺: MA Period | Σ: MA Timeframe
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What entry modes are used in this strategy? What are the underlying calculations?
The strategy by default uses two different techniques for the entry criteria with user-adjustable left and right bars: Breakout and Fractal.
1. Breakout Entries :
- The strategy looks for pivot high points with a default period of 3.
- It stores the most recent high level in a variable.
- When the price crosses above this most recent level, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot high left bars period | ◨: Pivot high right bars period
2. Fractal Entries :
- The strategy looks for pivot low points with a default period of 3.
- When a pivot low is detected, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot low left bars period | ◨: Pivot low right bars period
2. Hunt Entries :
- The strategy identifies a candle that wicks through the lower FRMA band.
- It waits for the next candle to close above the low of the wick candle.
- When this condition is met and the bar is closed, the strategy takes the buy entry.
By utilizing these entry modes, the strategy aims to capitalize on bullish price movements while ensuring that the necessary conditions are met to validate the entry points.
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What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
Initial Stop-Loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14).
Example - ATR (14) * 2
⍺: ATR period | Σ: ATR Multiplier
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
Example - ADR (20) * 2
⍺: ADR period | Σ: ADR Multiplier
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
Application in Strategy (ATR/ADR):
- The strategy calculates the current bar's ADR/ATR with a user-defined period.
- It then multiplies the ADR/ATR by a user-defined multiplier to determine the initial stop-loss level.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
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What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
Example:
If the entry price is $100, the initial risk is $10, and the RR ratio is 2, the break-even level is $100 + ($10 * 2) = $120.
FRMA Based:
Moves the stop-loss to break-even when the price hits the FRMA level at which the entry was taken.
Calculation:
Break-even level = FRMA level at the entry
Example:
If the FRMA level at entry is $102, the break-even level is set to $102 when the price reaches $102.
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
When Both Percentage (%) Based and RR Based Take Profit Levels Are Off:
The script will adjust the take profit level to the higher FRMA band set within user inputs.
Calculation:
Take profit level = Higher FRMA band length/timeframe specified by the user.
This ensures that when neither percentage-based nor risk-to-reward-based take profit methods are enabled, the strategy defaults to using the higher FRMA band as the take profit level, providing a consistent and structured approach to profit-taking.
For TP1 and TP2, it's specifying the price levels at which the position is partially or fully closed based on the chosen method (percentage or RR) above the entry price.
These calculations are crucial for managing risk and optimizing profitability in the strategy.
⍺: BE/TP type (%/RR) | Σ: how many RR/% above the current price
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What's the ADR filter? What does it do? What are the underlying calculations?
The Average Day Range (ADR) measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
The period of the ADR filter used in this strategy is tied to the same period you've used for your initial stop-loss.
Users can define the minimum ADR they want to be met before the script looks for entry conditions.
ADR Bias Filter:
- Compares the current bar ADR with the ADR (Defined by user):
- If the current ADR is higher, it indicates that volatility has increased compared to ADR (DbU).(⬆)
- If the current ADR is lower, it indicates that volatility has decreased compared to ADR (DbU).(⬇)
Calculations:
1. Calculate ADR:
- Average the high prices over the specified period.
- Average the low prices over the same period.
- Find the difference between these average values in %.
2. Current ADR vs. ADR (DbU):
- Calculate the ADR for the current bar.
- Calculate the ADR (DbU).
- Compare the two values to determine if volatility has increased or decreased.
By using the ADR filter, the strategy ensures that trades are only taken in favorable market conditions where volatility meets the user's defined threshold, thus optimizing entry conditions and potentially improving the overall performance of the strategy.
>: Minimum required ADR for entry | %: Current ADR comparison to ADR of 14 days ago.
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What's the probability filter? What are the underlying calculations?
The probability filter is designed to enhance trade entries by using buyside liquidity and probability analysis to filter out unfavorable conditions.
This filter helps in identifying optimal entry points where the likelihood of a profitable trade is higher.
Calculations:
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Equilibrium levels.
3. Understanding probability calculations
1. Upon the formation of a new range, the script waits for the price to reach and tap into equilibrium or the 50% level. Status: "⏸" - Inactive
2. Once equilibrium is tapped into, the equilibrium status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
5. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
Example - BSL > 55%
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What's the range length Filter? What are the underlying calculations?
The range length filter identifies the price distance between buyside and sellside liquidity levels in percentage terms. When enabled, the script only looks for entries when the minimum range length is met. This helps ensure that trades are taken in markets with sufficient price movement.
Calculations:
Range Length (%) = ( ( Buyside Level − Sellside Level ) / Current Price ) ×100
Range Bias Identification:
Bullish Bias: The current range price has broken above the previous external swing high.
Bearish Bias: The current range price has broken below the previous external swing low.
Example - Range length filter is enabled | Range must be above 1%
>: Minimum required range length for entry | %: Current range length percentage in a (Bullish/Bearish) range
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What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
Increased Flexibility: The filter provides increased flexibility, allowing traders to adapt the strategy to their specific needs and preferences.
Example - Day filter | Session Filter
θ: Session time | Exchange time-zone
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What tables are available in this script?
Table Type:
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- OFF: Hides the performance table.
Profit Color:
- Allows users to set the color for representing profit in the performance table, helping to quickly distinguish profitable periods.
Loss Color:
- Allows users to set the color for representing loss in the performance table, helping to quickly identify loss-making periods.
These customizable tables provide traders with flexible and detailed performance analysis, aiding in better strategy evaluation and optimization.
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User-input styles and customizations:
To facilitate studying historical data, all conditions and rules can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
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How to Use This Algobuilder to Create a Profitable Edge and System:
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker or prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 100 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade value is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
Automation:
- Once you’re confident in your strategy, you can use the automation section to connect the algorithm to your broker or prop firm.
- Trade a fully automated and backtested trading strategy, allowing for hands-free execution and management.
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What makes this strategy original?
1. Incorporating direct integration of probabilities into the strategy.
2. Utilizing built-in market structure-based moving averages across various timeframes.
4. Offering both investing and trading strategies, facilitating optimization from different perspectives.
5. Automation for efficient execution.
6. Providing a summary table for instant access to key parameters of the strategy.
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How to use automation?
For Traders:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Enter your PineConnector License ID in the designated field.
3. Specify the desired risk level.
4. Provide the Metatrader symbol.
5. Check for chart updates to ensure the automation table appears on the top right corner, displaying your License ID, risk, and symbol.
6. Set up an alert with the strategy selected as Condition and the Message as {{strategy.order.alert_message}}.
7. Activate the Webhook URL in the Notifications section, setting it as the official PineConnector webhook address.
8. Double-check all settings on PineConnector to ensure the connection is successful.
9. Create the alert for entry/exit automation.
For Investors:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Choose "Investing" in the user-input settings.
3. Create an alert with a specified name.
4. Customize the notifications tab to receive alerts via email.
5. Buying/selling alerts will be triggered instantly upon entry or exit order execution.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
(Envelopes)USS Enterprise1. This indicator is created for those who still believe in the functionality of moving averages. Indicator consists of several envelopes of moving averages and two separate averages. The selection of these moving averages is linked to Fibonacci theories and calculations.
2. The indicator shows moving averages (envelopes) of all market participants. From the smallest to the giants.
3. It should be noted that all averages are mainly calibrated to a 15-minute time frame. But I'm not saying that you can't use it on any TF. Because market is fractal.
Groups:
1. (YELLOW ENVELOPES) The first group are scalpers and big traders. Yellow envelope! This is the largest group of traders, but with the smallest capital on the market. Why did I choose this envelope? To show who is in control of the market. The average duration of holding the price of this envelope is 12-16 hours (in trend phase) and therefore it is suitable for intra-day trading. If the price closes below this envelope, we know that their strength was no longer sufficient. However, as long as these two yellow curves do not cross each other, we consider this group of traders to be still dominant/active and their weakening was only partial, for example, due to a pullback, or due to manipulation of the price of stronger players.
2. (LIGHT BLUE ENVELOPE) When I mentioned pullback. Understand it as the return of the price in the trend. But who is capable of these pullbacks in the trend? Our second group of traders. Institutions. (Light blue color). Only their amount of money can cause the price to return to their point of interest and that is the light blue envelope. The average ability to hold the trend of the institutions is something around 1-2 days. If the price closes with a slow decline/rise below this/above this envelope, we can expect that their strength is still large enough. However, if there are movements that seem to cut through this envelope, it is the first indication that the institutions are losing strength. If there is a crossover of any yellow average across both institutional ones, we can expect a much bigger pullback in the trend. This pullback is then again mainly under the control of the institutions (rejections from the light blue envelope.) But where can this pullback go? Another market participant will tell us that!
3. (DARK BLUE ENVELOPE) Market makers are another participant. Their task is to maintain balance on the market. This means that the market does not only go up or only down. That's what the envelope of market makers is for. This envelope is considered a trend defender. What makes it special. It can hold a trend even for days. We can consider the return to this envelope as a supply and demand strategy. In the trend, the price will come back here as a pullback and then rocket back into the original trend. I'll tell you what you probably guessed, yes, we are moving here at the EMA200 level. So if the institutional (light blue) traders lose their strength, believe me that the envelope of the market makers is a very likely stop! When does a trend change occur and not a pullback? If there is a crossing of the light blue average with the entire envelope of market makers. The next test from the other side of this envelope confirms the trend change.
4. Let's skip the black envelope for the moment.
5. (PURPLE ENVELOPE) Let's explain the purple envelope. It is the envelope of market makers and especially hedge funds. What do you think when the price closes below the EMA200 (originally a bull trend) and even tests it below? "We have a trend change now we definitely have a down trend!!!" Uhm. NOPE :D. That's their job. To show you what they want you to believe. What does this result in? Filling their large orders, which eventually means that you were caught and liquidated with your positions. By testing, you will find out how many times you thought there was a trend change, but after you see how the price reacts from the purple envelope, you will understand that until now you did not know at all when a general trend change occurs. When we talk about a trend change in the long term , occurs when the EMA200 (dark blue envelope) crosses this purple envelope. This purple envelope is able to keep the price trending for an average of 3 weeks.
Don't get caught that the trend change is when the price closes below the EMA200.Or "golden cross"
6. (BLACK ENVELOPE) Did we miss something though? So let's go back to the meaning of the black envelope. When you take a good look at the trend and notice all the envelopes lined up nicely and focus on the dark blue envelope and the purple envelope. Don't you feel like you're seeing Fibonacci's return? Or as if you see the price in the premium zone?.78%-88%. Yes, it's exactly this envelope. Sometimes market makers and funds are satisfied with the price in this envelope and are willing to continue buying or selling from this envelope. However, keep in mind, this can be a stop before testing the purple envelope - mostly the range is formed in this black envelope. Expect in such a case that they will test the purple envelope. Otherwise, take this envelope as a sign of a premium zone.
7. (ORANGE,TEAL and RED MA) The Orange,Teal and Red averages show a pure bank level. That is, our mentioned giants on the market. You will see for yourself on the market with what accuracy the banks return to these averages. You will see for yourself that trends really change only at these averages. You must have told yourself several times why and how patterns that resemble a letter are created in the market V or the letter A. Congratulations! Thanks to my indicator, you already know today! Because of these bank averages!!!
I wish you the best of luck with this indicator and hopefully it helps as many people as possible understand trends and how important simple lines can be! Which and how many envelopes or moving averages you will use is entirely up to you!
Warning: Everything published in this description or the functionality of this indicator serves only as educational content! Only YOU are responsible for all profits and losses!
Moving avg with regMoving avg with reg
A Moving avg with reg is a series of moving averages plotted on the same chart, each with different time periods. This visual tool helps traders identify the underlying trend and potential reversal points in the market. By observing the interaction and spacing between the moving averages, traders can gauge the market's strength and momentum.
Key Points:
Trend Identification: Multiple moving averages help confirm the direction of the trend. If the shorter-period moving averages are above the longer-period ones, it indicates an uptrend, and vice versa.
Reversal Signals: When shorter-period moving averages cross longer-period ones, it may signal a potential trend reversal.
Market Strength: The spacing between the moving averages indicates the strength of the trend. Wider spacing suggests a strong trend, while narrow spacing may indicate a weakening trend.
Regression Line
A Regression Line, specifically the Linear Regression Indicator (LRI), is a statistical tool used to determine the direction and strength of a trend by fitting a straight line to the price data over a specified period. This line minimizes the distance between itself and the actual price points, providing a clear visual representation of the trend.
Key Points:
Trend Direction: The slope of the regression line indicates the direction of the trend. A positive slope suggests an uptrend, while a negative slope indicates a downtrend.
Price Deviations: The distance between the actual price and the regression line can highlight overbought or oversold conditions. Large deviations may suggest a potential correction.
Predictive Power: By extending the regression line, traders can make predictions about future price movements based on the current trend.
Stochastic Biquad Band Pass FilterThis indicator combines the power of a biquad band pass filter with the popular stochastic oscillator to provide a unique tool for analyzing price movements.
The Filter Length parameter determines the center frequency of the biquad band pass filter, affecting which frequency band is isolated. Adjusting this parameter allows you to focus on different parts of the price movement spectrum.
The Bandwidth (BW) controls the width of the frequency band in octaves. It represents the bandwidth between -3 dB frequencies for the band pass filter. A narrower bandwidth results in a more focused filtering effect, isolating a tighter range of frequencies.
The %K Length parameter sets the period for the stochastic calculation, determining the range over which the stochastic values are calculated.
The %K Smoothing parameter applies a simple moving average to the %K values to smooth out the oscillator line.
The %D Length parameter sets the period for the %D line, which is a simple moving average of the %K line, providing a signal line for the oscillator.
Key Features of the Stochastic Biquad Band Pass Filter
Biquad filters are known for their smooth response and minimal phase distortion, making them ideal for technical analysis. In this implementation, the biquad filter is configured as a band pass filter, which allows frequencies within a specified band to pass while attenuating frequencies outside this band. This is particularly useful in trading to isolate specific price movements, making it easier to detect patterns and trends within a targeted frequency range.
The stochastic oscillator is a popular momentum indicator that shows the location of the close relative to the high-low range over a set number of periods. Combining it with a biquad band pass filter enhances its effectiveness by focusing on specific frequency bands of price movements.
By incorporating this stochastic biquad band pass filter into your trading toolkit, you can enhance your chart analysis with clearer insights into specific frequency bands of price movements, leading to more informed trading decisions.
Biquad Band Pass FilterThis indicator utilizes a biquad band pass filter to isolate and highlight a specific frequency band in price data, helping traders focus on price movements within a targeted frequency range.
The Length parameter determines the center frequency of the filter, affecting which frequency band is isolated. Adjusting this parameter allows you to focus on different parts of the price movement spectrum.
The Bandwidth (BW) controls the width of the frequency band in octaves. It represents the bandwidth between -3 dB frequencies for the band pass filter. A narrower bandwidth results in a more focused filtering effect, isolating a tighter range of frequencies.
Key Features of Biquad Filters
Biquad filters are a type of digital filter that provides a combination of low-pass, high-pass, band-pass, and notch filtering capabilities. In this implementation, the biquad filter is configured as a band pass filter, which allows frequencies within a specified band to pass while attenuating frequencies outside this band. This is particularly useful in trading to isolate specific price movements, making it easier to detect patterns and trends within a targeted frequency range.
Biquad filters are known for their smooth response and minimal phase distortion, making them ideal for technical analysis. The customizable length and bandwidth allow for flexible adaptation to different trading strategies and market conditions. Designed for real-time charting, the biquad filter operates efficiently without significant lag, ensuring timely analysis.
By incorporating this biquad band pass filter into your trading toolkit, you can enhance your chart analysis with clearer insights into specific frequency bands of price movements, leading to more informed trading decisions.
Biquad High Pass FilterThis indicator utilizes a biquad high pass filter to filter out low-frequency components from price data, helping traders focus on high-frequency movements and detect rapid changes in trends.
The Length parameter determines the cutoff frequency of the filter, affecting how quickly the filter responds to changes in price. A shorter length allows the filter to react more quickly to high-frequency movements.
The Q Factor controls the sharpness of the filter. A higher Q value results in a more precise filtering effect by narrowing the frequency band. However, be cautious when setting the Q factor too high, as it can induce resonance, amplifying certain frequencies and potentially making the filter less effective by introducing unwanted noise.
Key Features of Biquad Filters
Biquad filters are a type of digital filter that provides a combination of low-pass, high-pass, band-pass, and notch filtering capabilities. In this implementation, the biquad filter is configured as a high pass filter, which allows high-frequency signals to pass while attenuating lower-frequency components. This is particularly useful in trading to highlight rapid price movements, making it easier to spot short-term trends and patterns.
Biquad filters are known for their smooth response and minimal phase distortion, making them ideal for technical analysis. The customizable length and Q factor allow for flexible adaptation to different trading strategies and market conditions. Designed for real-time charting, the biquad filter operates efficiently without significant lag, ensuring timely analysis.
By incorporating this biquad high pass filter into your trading toolkit, you can enhance your chart analysis with clearer insights into rapid price movements, leading to more informed trading decisions.
Biquad Low Pass FilterThis indicator utilizes a biquad low pass filter to smooth out price data, helping traders identify trends and reduce noise in their analysis.
The Length parameter acts as the length of the moving average, determining the smoothness and responsiveness of the filter. Adjusting this parameter changes how quickly the filter reacts to price changes.
The Q Factor controls the sharpness of the filter. A higher Q value results in a narrower frequency band, enhancing the precision of the filter. However, be cautious when setting the Q factor too high, as it can induce resonance, amplifying certain frequencies and potentially making the filter less effective by introducing noise.
Key Features of Biquad Filters
Biquad filters are a type of digital filter that provides a combination of low-pass, high-pass, band-pass, and notch filtering capabilities. In this implementation, the biquad filter is configured as a low pass filter, which allows low-frequency signals to pass while attenuating higher-frequency noise. This is particularly useful in trading to smooth out price data, making it easier to spot underlying trends and patterns.
Biquad filters are known for their smooth response and minimal phase distortion, making them ideal for technical analysis. The customizable length and Q factor allow for flexible adaptation to different trading strategies and market conditions. Designed for real-time charting, the biquad filter operates efficiently without significant lag, ensuring timely analysis.
By incorporating this biquad low pass filter into your trading toolkit, you can enhance your chart analysis with clearer insights into price movements, leading to more informed trading decisions.
Strategy SEMA SDI WebhookPurpose of the Code:
The strategy utilizes Exponential Moving Averages (EMA) and Smoothed Directional Indicators (SDI) to generate buy and sell signals. It includes features like leverage, take profit, stop loss, and trailing stops. The strategy is intended for backtesting and automating trades based on the specified indicators and conditions.
Key Components and Functionalities:
1.Strategy Settings:
Overlay: The strategy will overlay on the price chart.
Slippage: Set to 1.
Commission Value: Set to 0.035.
Default Quantity Type: Percent of equity.
Default Quantity Value: 50% of equity.
Initial Capital: Set to 1000 units.
Calculation on Order Fills: Enabled.
Process Orders on Close: Enabled.
2.Date and Time Filters:
Inputs for enabling/disabling start and end dates.
Filters to execute strategy only within specified date range.
3.Leverage and Quantity:
Leverage: Adjustable leverage input (default 3).
USD Percentage: Adjustable percentage of equity to use for trades (default 50%).
Initial Capital: Calculated based on leverage and percentage of equity.
4.Take Profit, Stop Loss, and Trailing Stop:
Inputs for enabling/disabling take profit, stop loss, and trailing stop.
Adjustable parameters for take profit percentage (default 25%), stop loss percentage (default 4.8%), and trailing stop percentage (default 1.9%).
Calculations for take profit, stop loss, trailing price, and maximum profit tracking.
5.EMA Calculations:
Fast and slow EMAs.
Smoothed versions of the fast and slow EMAs.
6.SDI Calculations:
Directional movement calculation for positive and negative directional indicators.
Difference between the positive and negative directional indicators, smoothed.
7.Buy/Sell Conditions:
Long (Buy) Condition: Positive DI is greater than negative DI, and fast EMA is greater than slow EMA.
Short (Sell) Condition: Negative DI is greater than positive DI, and fast EMA is less than slow EMA.
8.Strategy Execution:
If buy conditions are met, close any short positions and enter a long position.
If sell conditions are met, close any long positions and enter a short position.
Exit conditions for long and short positions based on take profit, stop loss, and trailing stop levels.
Close all positions if outside the specified date range.
Usage:
This strategy is used to automate trading based on the specified conditions involving EMAs and SDI. It allows backtesting to evaluate performance based on historical data. The strategy includes risk management through take profit, stop loss, and trailing stops to protect gains and limit losses. Traders can customize the parameters to fit their specific trading preferences and risk tolerance. Differently, it can perform leverage analysis and use it as a template.
By using this strategy, traders can systematically execute trades based on technical indicators, helping to remove emotional bias and improve consistency in trading decisions.
Important Note:
This script is provided for educational and template purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
Internal Bar Strength IBS [Anan]This indicator calculates and displays the Internal Bar Strength (IBS) along with its moving average. The IBS is a measure that represents where the closing price is relative to the high-low range of a given period.
█ Main Formula
The core of this indicator is the Internal Bar Strength (IBS) calculation. The basic IBS formula is:
ibs = (close - low) / (high - low)
I enhanced the original formula by incorporating a user-defined length parameter. This modification allows for greater flexibility in analysis and interpretation. The extended version enables users to adjust the indicator's length according to their specific needs or market conditions. Notably, setting the length parameter to 1 reproduces the behavior of the original formula, maintaining backward compatibility while offering expanded functionality:
ibs = (close - ta.lowest(low, ibs_length)) / (ta.highest(high, ibs_length) - ta.lowest(low, ibs_length))
Where:
- `close` is the closing price of the current bar
- `lowest low` is the lowest low price over the specified IBS length
- `highest high` is the highest high price over the specified IBS length
█ Key Features
- Calculates IBS using a user-defined length
- Applies a moving average to the IBS values
- Offers multiple moving average types
- Includes optional Bollinger Bands or Donchian Channel overlays
- Visualizes bull and bear areas
█ Inputs
- IBS Length: The period used for IBS calculation
- MA Type: The type of moving average applied to IBS (options: SMA, EMA, SMMA, WMA, VWMA, Bollinger Bands, Donchian)
- MA Length: The period used for the moving average calculation
- BB StdDev: Standard deviation multiplier for Bollinger Bands
█ How to Use and Interpret
1. IBS Line Interpretation:
- IBS values range from 0 to 1
- Values close to 1 indicate the close was near the high, suggesting a bullish sentiment
- Values close to 0 indicate the close was near the low, suggesting a bearish sentiment
- Values around 0.5 suggest the close was near the middle of the range
2. Overbought/Oversold Conditions:
- IBS values above 0.8 (teal zone) may indicate overbought conditions
- IBS values below 0.2 (red zone) may indicate oversold conditions
- These zones can be used to identify potential reversal points
3. Trend Identification:
- Consistent IBS values above 0.5 may indicate an uptrend
- Consistent IBS values below 0.5 may indicate a downtrend
4. Using Moving Averages:
- The yellow MA line can help smooth out IBS fluctuations
- Crossovers between the IBS and its MA can signal potential trend changes
5. Bollinger Bands/Donchian Channel:
- When enabled, these can provide additional context for overbought/oversold conditions
- IBS touching or exceeding the upper band may indicate overbought conditions
- IBS touching or falling below the lower band may indicate oversold conditions
Remember that no single indicator should be used in isolation. Always combine IBS analysis with other technical indicators, price action analysis, and broader market context for more reliable trading decisions.
