I11L - Risk Adjusted LeveragingThis trading system, called "I11L - Risk Adjusted Leveraging", is designed to manage trades based on the current market volatility relative to its historical average. The system calculates the target number of open trades based on the ATR (Average True Range) indicator and adjusts the leverage accordingly. The system opens and closes trades using a pyramiding approach, allowing multiple positions to be opened at the same time.
Here's a step-by-step explanation of the system:
1. Calculate the ATR with a 14-day period and normalize it by dividing it by the current closing price.
2. Calculate the 100-day simple moving average (SMA) of the normalized ATR.
3. Calculate the ratio of the normalized ATR to its 100-day SMA.
4. Determine the target leverage based on the inverse of the ratio (2 / ratio).
5. Calculate the target number of open trades by multiplying the target leverage by 5.
6. Plot the target number of open trades and the current number of open trades on the chart.
7. Check if there's an opportunity to buy (if the current number of open trades is less than the target) or close a trade (if the current number of open trades is more than the target plus 1).
8. If there's an opportunity to buy, open a long trade and add the trade's name to the openTrades array.
9. If there's an opportunity to close a trade and there are trades in the openTrades array, close the most recent trade by referencing the array and remove it from the array.
This system aims to capture trends in the market by dynamically adjusting the number of open trades and leverage based on the market's volatility. It uses an array to keep track of open trades, allowing for better control over the opening and closing of individual trades.
Cari dalam skrip untuk "想象图：箱线图+折线组合，横轴为国家，纵轴为响应指数（0-100），箱线显示均值±标准差，叠加红色虚线标注各国确诊高峰时间点"
Modified Mannarino Market Risk Indicator MMMRI MMRIModified Mannarino Market Risk Indicator MMMRI was developed by "Nobody Special Finance" as an enhancement to the original MMRI developed by Gregory Mannarino. The original and modified version were created as a way to gauge current level of risk in the market. This published indicator includes both versions along with ability to customize the symbols, denominators, and ratio factors that are used within their formulas. Additional options have been included to colorize the candles, plot, and level fills, as well as the option to show or hide a table containing the realtime values for both versions, along with the current dollar strength and 10Y yield.
Levels of market risk are denoted by dashed lines which represent the following levels: 0-50 slight risk, 50-100 low risk, 100-200 moderate risk, 200-300 high risk, 300+ extreme risk. The plot displays whichever of the following two formulas has been selected in the indicator settings, the default choice has been set to MMMRI:
MMRI = (USD Strength * USD Interest Rate) / 1.61
MMMRI = (Debt / GDP) * (USD Strength * USD Interest Rate) / 1.61
NOTICE: This is an example script and not meant to be used as an actual strategy. By using this script or any portion thereof, you acknowledge that you have read and understood that this is for research purposes only and I am not responsible for any financial losses you may incur by using this script!
RSI is in Normal Distribution?Does RSI Follow a Normal Distribution?
The value of RSI was converted to a value between 0~2, 2~4, ..., 98~100, and the number of samples was graphed.
The Z values are expressed so that the values corresponding to 30 and 70 of the RSI can be compared with the standard normal distribution.
Additionally, when using the RSI period correction function of the 'RSI Candle Advanced V2' indicator that I made before, it shows no change in standard deviation.
RSI는 정규분포를 따를까요
RSI의 값을 0~2, 2~4, ..., 98~100 사이 값으로 변환하고 그 표본 갯수를 그래프로 표현하였습니다.
Z 값은 RSI의 30, 70에 해당하는 값을 표준정규분포와 비교할 수 있도록 표현하였습니다.
추가적으로 제가 예전에 만들었던 'RSI Candle Advanced V2' 지표의 RSI 기간 보정 함수를 사용할 경우 표준편차의 변화가 없음을 보입니다.
Negroni MA & RSI Strategy, plus trade entry and SL/TP optionsI will start with the context, and some things to think about when using a strategy tool to back-test ideas.
CONTEXT
FIRST: This is derived from other people's work, but I honestly hadn't found a mixed indicator MA strategy tool that does what this now does. If it is out there, apologies!!
This tool can help back-test various MA trends (SMA, EMA, HMA, VWMA); as well as factoring in RSI levels (or not); and can factor in a fixed HTF MA (or not). You can apply a 'retest entry' or a 'breakout entry', and you can also apply various risk mgt for SL/TP orders: 1) No SL/TP; or 2) a fixed %, or 3) dynamic ATR multipliers.
Find below, some details explaining what this tool is attempting to do.
Thank you, tack, salute!
THINGS TO REVIEW (it is not just about 'profitability'!!)
Whilst discretion is always highly encouraged as a trader, and a 100% indicator-driven strategy is VERY unlikely to yield sustainable results going forward, at the very least back-testing your strategies can help provide some guidance, not just on win rate Vs profit factor, but other things including:
a) Trade frequency: if a strategy has an 75% win rate and profit factor of 4, with all your parameters and confluence checks, but only triggers 3 trades every 5 years, is that realistically implementable to your trading situation if you have a $10,000 account?
b) Trade entry type: is it consistently better to wait for a retest of an 'MA zone', or is it better to market buy/sell on breakout of the 'MA zone'?
c) Risk management (SL/TP): is it consistently better to have a fixed static % for SL/TP ("I always place my stops 2% away, whether it is EURUSD or BTCUSDT"), or would you be better placed to try using an ATR multiplier of the respective assets?
d) Moving average type: is your old faithful 100 EMA really serving you well, or is the classic SMA more reliable, or how about the HMA, or the VWMA? Is the 100/200 cross holding up, or do you need something more sensitive? Is there any significant difference between a 10 EMA/20 EMA trend zone compared to a 13 EMA /25 EMA zone?
e) Confluence: Do added confluence checks (RSI, higher timeframe MA) actually improve profitability? But even if they do, is at the cost of cutting too many trades?
INPUTS AND PARAMETERS
Choice 1) Entry Strategy: Retest or Breakout - You can select both!
[ ]:
a) RETEST entry strat: price crosses UNDER FastMA INTO the 'MA trend zone'.
b) BREAKOUT entry strat: price crosses OVER FastMA OUT the 'MA trend zone'.
Choice 2) Risk Management (SL and TP) - You can select more than 1 strategy!
a) No SL/TP: Long trades are closed when the LOW crosses back UNDER the fastMA again, and shorts are closed when the HIGH crosses back OVER the fastMA again.
b) Static % SL/TP: Your SL/TP will be a fixed % away from avg. position price... WARNING: You should change this for various asset classes; FX vol is not the same as crypto altcoin vol!
c) Dynamic ATR SL/TP: Your SL/TP is a multiple of your selected ATR range (default is 50, see 'info' when you select ATR range). ATR accounts for the change in vol of different asset classes somewhat, HOWEVER... you should probably still not have the same multiplier trading S&P500 as you would trading crypto altcoins!
Then select your preferred parameters: EMA, SMA, HMA, VWMA, etc. You can mix and match, and most options have a info/tooltip guide.
RSI note: If you don't care for RSI levels, then set buy signal at 1... i.e always buys! Similarly set sell signal at 99.
ATR note: standard ATR length is usually 14, however... your SL/TP will move POST entry, and can tighten or widen your initial SL/TP... for better AND usually for worse! Go find a trade (strat 3) on the chart, look at the SL/TP lines, now change the number to 5, you'll see.
Fixed HTF MA note: If you don't care for HTF MA confluence, just change the timeframe/options to match the 'Slow MA' options you've chosen.
Smart QQE ModSmart QQE - Chart Overlay
Smart QQE shows QQE Trend and RSI plot on chart to determine the trend direction and eliminate false signals.
QQE is obtained from original code by Glaz and rescaled to fit on chart. RSI 50 level acts as Zero which is plotted as a Bollinger on chart.
This is not a Bollinger band . its an RSI channel with levels 0-100 plotted around the mid band. The RSI Mid Band is calculated based on RSI value.
Trend:
Price above RSI Mid band is uptrend
Price below RSI Mid band is Down Trend
The Green line - Discount Zone - 0-RSI level - Oversold Zone
The Red Line - Premium Zone - 100 - RSI level - Overbought Zone
Buy / Sell signals
QQE Buy and Sell signals are plotted based on crossovers of RSI and Fast RSI crossovers.
QQE trend is colored based on the crossover.
Candle color:
candle color determines the Original QQE Trend.
Blue - QQE line above Threshold level in Buy Zone
Pink - QQE line below Threshold level in Sell Zone
Entries are to be made with proper confirmation.
HULL MA is provided as a MA Ribbon for additional confirmation. This MA can be changed to various forms Like EMA , SMA , WMA , HMA , RMA the open and close of the MA are plotted so it determines the exact Trend reversal of the price.
Credits to @Glaz QQE Threshold
Dynamo
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Overview
Dynamo is built to be the Swiss-knife for price-movement & strength detection, it aims to provide a holistic view of the current price across multiple dimensions. This is achieved by combining 3 very specific indicators(RSI, Stochastic & ADX) into a single view. Each of which serve a different purpose, and collectively provide a simple, yet powerful tool to gauge the true nature of price-action.
Background
Dynamo uses 3 technical analysis tools in conjunction to provide better insights into price movement, they are briefly explained below:
Relative Strength Index(RSI)
RSI is a popular indicator that is often used to measure the velocity of price change & the intensity of directional moves. RSI computes the relative strength of the current price by comparing the security’s bullish strength versus bearish strength for a given period, i.e. by comparing average gain to average loss.
It is a range bound(0-100) variable that generates a bullish reading if average gain is higher, and a bullish reading if average loss is higher. Values over 50 are generally considered bullish & values less than 50 indicate a bearish market. Values over 70 indicate an overbought condition, and values below 30 indicate oversold condition.
Stochastic
Stochastic is an indicator that aims to measure the momentum in the market, by comparing most recent closing price of the security to its price range for a given period. It is based on the assumption that price tends to close near the recent high in an up trend, and it closes near the recent low during a down trend.
It is also range bound(0-100), values over 80 indicate overbought condition and values below 20 indicate oversold condition.
Average Directional Index(ADX)
ADX is an indicator that can quantify trend strength, it is derived from two underlying indices, known as Directional Movement Index(DMI). +DMI represents strength of the up trend, and -DMI represents strength of the down trend, and ADX is the average of the two.
ADX is non-directional or trend-neutral, which means, it does not follow the direction of the price, instead ADX will rise only when there is a strong trend, it does not matter if it’s an up trend or a down trend. Typical ranges of ADX are 25-50 for a strong trend, anything below 25 is considered as no trend or weak trend. ADX can frequently shoot upto higher values, but it generally finds exhaustion levels around the 60-75 range.
About the script
All these indicators are very powerful tools, but just like any other indicator they have their limitations. Stochastic & ADX can generate false signals in volatile markets, meaning price wouldn’t always follow through with what’s being indicated. ADX may even fail to generate a signal in less volatile markets, simply because it is based on moving averages, it tends to react slower to price changes. RSI can also lose it’s effectiveness when markets are trending strong, as it can stay in the overbought or oversold ranges for an extended period of time.
Dynamo aims to provide the trader with a much broader perspective by bringing together these contrasting indicators into a single simplified view. When Stochastic becomes less reliable in highly volatile conditions, one can cross validate their deduction by looking at RSI patterns. When RSI gets stuck in overbought or oversold range, one can refer to ADX to get better picture about the current trend. Similarly, various combinations of rules & setups can be formulated to get a more deterministic view, when working with either of these indicators.
There many possible use cases for a tool like this, and it totally depends on how you want to use it. An obvious option is to use it to trigger signals only after it has been confirmed by two or more indicators, for example, RSI & Stochastic make a great combination for cross-over or cross-under strategies. Some of the other options include trend detection, strength detection, reversals or price rejection points, possible duration of a trend, and all of these can very easily be translated into effective entry and exit points for trades.
How to use it
Dynamo is an easy-to-use tool, just add it to your chart and you’re good to start with your market analysis. Output consists of three overlapping plots, each of which tackle price movement from a slightly different angle.
Stochastic: A momentum indicator that plots the current closing price in relation to the price-range over a given period of time.
Can be used to detect the direction of the price movement, potential reversals, or duration of an up/down move.
Plotted as grey coloured histograms in the background.
Relative Strength Index(RSI): RSI is also a momentum indicator that measures the velocity with which the price changes.
Can be used to detect the speed of the price movement, RSI divergences can be a nice way to detect directional changes.
Plotted as an aqua coloured line.
Average Directional Index(ADX): ADX is an indicator that is used to measure the strength of the current trend.
Can be used to measure how strong the price movement is, both up and down, or to establish long terms trends.
Plotted as an orange coloured line.
Features
Provides a well-rounded view of the market movement by amalgamating some of the best strength indicators, helping traders make better informed decisions with minimal effort.
Simplistic plots that aim to convey clean signals, as a result, reducing clutter on the chart, and hopefully in the trader's head too.
Combines different types of indicators into a single view, which leads to an optimised use of the precious screen real-estate.
Final Note
Dynamo is designed to be minimalistic in functionality and in appearance, as it is being built to be a general purpose tool that is not only beginner friendly, but can also be highly-configurable to meet the needs of pro traders.
Thresholds & default values for the indicators are only suggestions based on industry standards, they may not be an exact match for all markets & conditions. Hence, it is advisable for the user to test & adjust these values according their securities and trading styles.
The chart highlights one of many possible setups using this tool, and it can used to create various types of setups & strategies, but it is also worth noting that the usability & the effectiveness of this tool also depends on the user’s understanding & interpretation of the underlying indicators.
Lastly, this tool is only an indicator and should only be perceived that way. It does not guarantee anything, and the user should do their own research before committing to trades based on any indicator.
Crypto McClellan Oscillator (SLN Fix)This is an adaption of the Mcclellan Oscillator for crypto. Instead of tracking the S&P500 it tracks a selection of cryptos to make sure the indicator follows this sector instead.
Full credit goes to the creator of this indicator: Fadior. It has since been fixed by SLN.
The following description explains the standard McClellan Oscillator. Full credit to Investopedia , my fav source of financial explanations.
The same principles applies to its use in the crypto sector, but please be cautious of the last point, the limitations. Since crypto is more volatile, that could amplify choppy behavior.
This is not financial advice, please be extremely cautious. This indicator is only suitable as a confirmation signal and needs support of other signals to be profitable.
This indicator usually produces the best signals on slightly above daily time frame. I personally like 2 or 3 day, but you have to find the settings suitable for your trading style.
What Is the McClellan Oscillator?
The McClellan Oscillator is a market breadth indicator that is based on the difference between the number of advancing and declining issues on a stock exchange, such as the New York Stock Exchange (NYSE) or NASDAQ.
The indicator is used to show strong shifts in sentiment in the indexes, called breadth thrusts. It also helps in analyzing the strength of an index trend via divergence or confirmation.
The McClellan Oscillator formula can be applied to any stock exchange or group of stocks.
A reading above zero helps confirm a rise in the index, while readings below zero confirm a decline in the index.
When the index is rising but the oscillator is falling, that warns that the index could start declining too. When the index is falling and the oscillator is rising, that indicates the index could start rising soon. This is called divergence.
A significant change, such as moving 100 points or more, from a negative reading to a positive reading is called a breadth thrust. It may indicate a strong reversal from downtrend to uptrend is underway on the stock exchange.
How to Calculate the McClellan Oscillator
To get the calculation started, track Advances - Declines on a stock exchange for 19 and 39 days. Calculate a simple average for these, not exponential moving average (EMA).
Use these simple values as the Prior Day EMA values in the 19- and 39-day EMA formulas.
Calculate the 19- and 39-day EMAs.
Calculate the McClellan Oscillator value.
Now that the value has been calculated, on the next calculation use this value for the Prior Day EMA. Start calculating EMAs for the formula instead of simple averages.
If using the adjusted formula, the steps are the same, except use ANA instead of using Advances - Declines.
What Does the McClellan Oscillator Tell You?
The McClellan Oscillator is an indicator based on market breadth which technical analysts can use in conjunction with other technical tools to determine the overall state of the stock market and assess the strength of its current trend.
Since the indicator is based on all the stocks in an exchange, it is compared to the price movements of indexes that reflect that exchange, or compared to major indexes such as the S&P 500.
Positive and negative values indicate whether more stocks, on average, are advancing or declining. The indicator is positive when the 19-day EMA is above the 39-day EMA, and negative when the 19-day EMA is below the 39-day EMA.
A positive and rising indicator suggests that stocks on the exchange are being accumulated. A negative and falling indicator signals that stocks are being sold. Typically such action confirms the current trend in the index.
Crossovers from positive to negative, or vice versa, may signal the trend has changed in the index or exchange being tracked. When the indicator makes a large move, typically of 100 points or more, from negative to positive territory, that is called a breadth thrust.
It means a large number of stocks moved up after a bearish move. Since the stock market tends to rise over time, this a positive signal and may indicate that a bottom in the index is in and prices are heading higher overall.
When index prices and the indicator are moving in different directions, then the current index trend may lack strength. Bullish divergence occurs when the oscillator is rising while the index is falling. This indicates the index could head higher soon since more stocks are starting to advance.
Bearish divergence is when the index is rising and the indicator is falling. This means fewer stocks are keeping the advance going and prices may start to head lower.
Limitations of Using the McClellan Oscillator
The indicator tends to produce lots of signals. Breadth thrusts, divergence, and crossovers all occur with some frequency, but not all these signals will result in the price/index moving in the expected direction.
The indicator is prone to producing false signals and therefore should be used in conjunction with price action analysis and other technical indicators.
The indicator can also be quite choppy, moving between positive and negative territory rapidly. Such action indicates a choppy market, but this isn't evident until the indicator has made this whipsaw move a few times.
Good luck and a big thanks to Fadior!
TOMMAR#TOMMAR #MultiMovingAverages #MMAR
Dear fellow traders, this is Tommy, and today I'd like to introduce you to the Multi-Moving Averages Ribbon (MMAR) indicator, which I believe to be one of the best MMAR indicators available on TradingView. Moving Averages is a popular technical analysis tool used to smooth out price data by creating an average of past price data points over a specified time period. They can be used to identify trends and provide a clearer view of price action, as well as generate buy and sell signals by observing crossovers between different moving average lines.
In the MMAR indicator, we have incorporated 12 different types of Moving Averages, including Simple Moving Averages (SMA), Exponential Moving Averages (EMA), Weighted Moving Averages (WMA), Hull Moving Averages (HMA), and Smoothed Moving Averages (SMMA), among others. This allows traders to choose the optimal type for their preferred trading commodities.
One common technique in technical analysis is using multiple Moving Averages with varying lengths, which provides a more comprehensive view of price action. By analyzing multiple Moving Averages with different timeframes, traders can better understand both short- and long-term trends and make more informed trading decisions. Some of the well-known combinations of multiple moving averages used by traders are (5, 9, 14, 21, 45), (6, 11, 16, 22, 51), [8, 13, 21, 55), (50, 100, 200), and (60, 120, 240).
Another way to gauge the strength of the market trend is to look for the arrangement of the Moving Averages. If they are in a sequential order, with the shortest on top and the longest on the bottom, it is most likely a bullish trend. On the other hand, if they are arranged in reverse order, with the shortest on the bottom and the longest on top, it is most likely a bearish trend. The 'Trend Light' in the indicator settings will automatically signal when the Moving Averages are in either an orderly or reverse arrangement.
Lastly, I have added a useful feature to the indicator: the 'MA Projection'. This feature projects and forecasts the Moving Averages in the future, allowing traders to easily identify confluence zones in future candlesticks. Please note that the projection levels may change in the case of extreme price action that significantly affects the Moving Averages.
This is free so any Tradingview users can use this indicator. Just search TOMMAR in the indicator section located on top of the chart.
#TOMMAR #MultiMovingAverages #MMAR
안녕하세요 트레이더 여러분, 토미입니다. 오늘 여러분들에게 소개드릴 지표는 다양한 길이의 이동평균선 조합을 사용할 수 있는 MMAR (Multiple Moving Averages Ribbon)입니다. 아마 제가 만든 MMAR 지표가 트레이딩뷰에서 가장 쓸만할 겁니다. 이동평균선, 줄여서 이평선은 말 그대로 특정 기간 범위 내의 주가들을 평균한 값들로 이루어진 선입니다. 제가 이평선 관련된 강의 자료는 예전에 올려드린 바 있으니 더 자세한 내용이 궁금하신 분들은 아래 링크/이미지 클릭하시길 바랍니다.
본 지표는 Simple Moving Averages (SMA), Exponential Moving Averages (EMA), Weighted Moving Averages (WMA), Hull Moving Averages (HMA), 그리고 Smoothed Moving Averages (SMMA) 등을 포함해 총 12개 종류의 이평선 지표를 사용할 수 있습니다. 또한 각 이평선의 길이들도 하나하나 일일이 설정하실 수 있습니다. 예를 들어 요즘에 자주 보이는 이평선들의 조합이 , , , , 그리고 등등이 존재하는데 여러분의 취향에 맞게 설정하여 사용하시면 됩니다.
몇 가지 주요 기능에 대해서 설명 드리겠습니다. 설정에서 ‘Trend Light’를 키면 이평선들의 정배열 혹은 역배열 여부를 쉽게 볼 수 있습니다. 이평선이 정배열일때는 맨 아래의 이평선에 초록불이, 역배열일때는 맨 위의 이평선에 빨간불이 켜지며 둘 다 아닐 땐 아무 불도 켜지지 않습니다. 또한 ‘MA Projection’을 키면 이평선들의 미래 예측 값들을 확장해줍니다. 당연히 가격 변동이 갑자기 크게 나오면 이평선 예측 확장 레벨들이 확 바뀌겠죠.
지표창에 TOMMAR 검색하시거나 아래 즐겨찾기 인디케이터에 넣기 클릭하시면 누구나 사용하실 수 있습니다~ 여러분의 구독, 좋아요, 댓글은 저에게 큰 힘이 됩니다.
Cryptos Pump Hunter[liwei666]🔥 Cryptos Pump Hunter captured high volatility symbols in real-time, Up to 40 symbols can be monitored at same time.
Help you find the most profitable symbol with excellent visualization.
🔥 Indicator Design logic
🎯 The core pump/dump logic is quite simple
1. calc past bars highest and lowest High price, get movement by this formula
" movement = (highest - lowest) / lowest * 100 "
2. order by 'movement' value descending, you will get a volatility List
3. use Table tool display List, The higher the 'movement', the higher the ranking.
🔥 Settings
🎯 2 input properties impact on the results, 2 input impact on display effects, others look picture below.
pump_bars_cnt : lookback bar to calc pump/dump
resolution for pump : 1min to 1D
show_top1 : when ranking list top1 change, will draw a label
show pump : when symbol over threhold, draw a pump lable
🔥 How TO USE
🎯 only trade high volatility symbols
1. focus on top1 symbol on Table panel at top-right postion, trading symbols at label in chart.
2. Short when 'postion' ~ 0, Long when 'postion' ~ 1 on Table Cell
🎯 Monitor the symbols you like
1. 100+ symbols added in script, cancel remarks in code line if symbol is your want
2. add 1 line code if symbol not exist. if you want monitor 'ETHUSDTPERP ', then add
" ETHUSDTPERP = create_symbol_obj('BINANCE:ETHUSDTPERP'), array.unshift(symbol_a, ETHUSDTPERP ) "
🎯 Alert will be add soon, any questions or suggestion please comment below, I would appreciate it greatly.
Hope this indicator will be useful for you :)
enjoy! 🚀🚀🚀
Machine Learning: Lorentzian Classification█ OVERVIEW
A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to predict the direction of future price movements when used as the distance metric for a novel implementation of an Approximate Nearest Neighbors (ANN) algorithm.
█ BACKGROUND
In physics, Lorentzian space is perhaps best known for its role in describing the curvature of space-time in Einstein's theory of General Relativity (2). Interestingly, however, this abstract concept from theoretical physics also has tangible real-world applications in trading.
Recently, it was hypothesized that Lorentzian space was also well-suited for analyzing time-series data (4), (5). This hypothesis has been supported by several empirical studies that demonstrate that Lorentzian distance is more robust to outliers and noise than the more commonly used Euclidean distance (1), (3), (6). Furthermore, Lorentzian distance was also shown to outperform dozens of other highly regarded distance metrics, including Manhattan distance, Bhattacharyya similarity, and Cosine similarity (1), (3). Outside of Dynamic Time Warping based approaches, which are unfortunately too computationally intensive for PineScript at this time, the Lorentzian Distance metric consistently scores the highest mean accuracy over a wide variety of time series data sets (1).
Euclidean distance is commonly used as the default distance metric for NN-based search algorithms, but it may not always be the best choice when dealing with financial market data. This is because financial market data can be significantly impacted by proximity to major world events such as FOMC Meetings and Black Swan events. This event-based distortion of market data can be framed as similar to the gravitational warping caused by a massive object on the space-time continuum. For financial markets, the analogous continuum that experiences warping can be referred to as "price-time".
Below is a side-by-side comparison of how neighborhoods of similar historical points appear in three-dimensional Euclidean Space and Lorentzian Space:
This figure demonstrates how Lorentzian space can better accommodate the warping of price-time since the Lorentzian distance function compresses the Euclidean neighborhood in such a way that the new neighborhood distribution in Lorentzian space tends to cluster around each of the major feature axes in addition to the origin itself. This means that, even though some nearest neighbors will be the same regardless of the distance metric used, Lorentzian space will also allow for the consideration of historical points that would otherwise never be considered with a Euclidean distance metric.
Intuitively, the advantage inherent in the Lorentzian distance metric makes sense. For example, it is logical that the price action that occurs in the hours after Chairman Powell finishes delivering a speech would resemble at least some of the previous times when he finished delivering a speech. This may be true regardless of other factors, such as whether or not the market was overbought or oversold at the time or if the macro conditions were more bullish or bearish overall. These historical reference points are extremely valuable for predictive models, yet the Euclidean distance metric would miss these neighbors entirely, often in favor of irrelevant data points from the day before the event. By using Lorentzian distance as a metric, the ML model is instead able to consider the warping of price-time caused by the event and, ultimately, transcend the temporal bias imposed on it by the time series.
For more information on the implementation details of the Approximate Nearest Neighbors (ANN) algorithm used in this indicator, please refer to the detailed comments in the source code.
█ HOW TO USE
Below is an explanatory breakdown of the different parts of this indicator as it appears in the interface:
Below is an explanation of the different settings for this indicator:
General Settings:
Source - This has a default value of "hlc3" and is used to control the input data source.
Neighbors Count - This has a default value of 8, a minimum value of 1, a maximum value of 100, and a step of 1. It is used to control the number of neighbors to consider.
Max Bars Back - This has a default value of 2000.
Feature Count - This has a default value of 5, a minimum value of 2, and a maximum value of 5. It controls the number of features to use for ML predictions.
Color Compression - This has a default value of 1, a minimum value of 1, and a maximum value of 10. It is used to control the compression factor for adjusting the intensity of the color scale.
Show Exits - This has a default value of false. It controls whether to show the exit threshold on the chart.
Use Dynamic Exits - This has a default value of false. It is used to control whether to attempt to let profits ride by dynamically adjusting the exit threshold based on kernel regression.
Feature Engineering Settings:
Note: The Feature Engineering section is for fine-tuning the features used for ML predictions. The default values are optimized for the 4H to 12H timeframes for most charts, but they should also work reasonably well for other timeframes. By default, the model can support features that accept two parameters (Parameter A and Parameter B, respectively). Even though there are only 4 features provided by default, the same feature with different settings counts as two separate features. If the feature only accepts one parameter, then the second parameter will default to EMA-based smoothing with a default value of 1. These features represent the most effective combination I have encountered in my testing, but additional features may be added as additional options in the future.
Feature 1 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 2 - This has a default value of "WT" and options are: "RSI", "WT", "CCI", "ADX".
Feature 3 - This has a default value of "CCI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 4 - This has a default value of "ADX" and options are: "RSI", "WT", "CCI", "ADX".
Feature 5 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Filters Settings:
Use Volatility Filter - This has a default value of true. It is used to control whether to use the volatility filter.
Use Regime Filter - This has a default value of true. It is used to control whether to use the trend detection filter.
Use ADX Filter - This has a default value of false. It is used to control whether to use the ADX filter.
Regime Threshold - This has a default value of -0.1, a minimum value of -10, a maximum value of 10, and a step of 0.1. It is used to control the Regime Detection filter for detecting Trending/Ranging markets.
ADX Threshold - This has a default value of 20, a minimum value of 0, a maximum value of 100, and a step of 1. It is used to control the threshold for detecting Trending/Ranging markets.
Kernel Regression Settings:
Trade with Kernel - This has a default value of true. It is used to control whether to trade with the kernel.
Show Kernel Estimate - This has a default value of true. It is used to control whether to show the kernel estimate.
Lookback Window - This has a default value of 8 and a minimum value of 3. It is used to control the number of bars used for the estimation. Recommended range: 3-50
Relative Weighting - This has a default value of 8 and a step size of 0.25. It is used to control the relative weighting of time frames. Recommended range: 0.25-25
Start Regression at Bar - This has a default value of 25. It is used to control the bar index on which to start regression. Recommended range: 0-25
Display Settings:
Show Bar Colors - This has a default value of true. It is used to control whether to show the bar colors.
Show Bar Prediction Values - This has a default value of true. It controls whether to show the ML model's evaluation of each bar as an integer.
Use ATR Offset - This has a default value of false. It controls whether to use the ATR offset instead of the bar prediction offset.
Bar Prediction Offset - This has a default value of 0 and a minimum value of 0. It is used to control the offset of the bar predictions as a percentage from the bar high or close.
Backtesting Settings:
Show Backtest Results - This has a default value of true. It is used to control whether to display the win rate of the given configuration.
█ WORKS CITED
(1) R. Giusti and G. E. A. P. A. Batista, "An Empirical Comparison of Dissimilarity Measures for Time Series Classification," 2013 Brazilian Conference on Intelligent Systems, Oct. 2013, DOI: 10.1109/bracis.2013.22.
(2) Y. Kerimbekov, H. Ş. Bilge, and H. H. Uğurlu, "The use of Lorentzian distance metric in classification problems," Pattern Recognition Letters, vol. 84, 170–176, Dec. 2016, DOI: 10.1016/j.patrec.2016.09.006.
(3) A. Bagnall, A. Bostrom, J. Large, and J. Lines, "The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms." ResearchGate, Feb. 04, 2016.
(4) H. Ş. Bilge, Yerzhan Kerimbekov, and Hasan Hüseyin Uğurlu, "A new classification method by using Lorentzian distance metric," ResearchGate, Sep. 02, 2015.
(5) Y. Kerimbekov and H. Şakir Bilge, "Lorentzian Distance Classifier for Multiple Features," Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017, DOI: 10.5220/0006197004930501.
(6) V. Surya Prasath et al., "Effects of Distance Measure Choice on KNN Classifier Performance - A Review." .
█ ACKNOWLEDGEMENTS
@veryfid - For many invaluable insights, discussions, and advice that helped to shape this project.
@capissimo - For open sourcing his interesting ideas regarding various KNN implementations in PineScript, several of which helped inspire my original undertaking of this project.
@RikkiTavi - For many invaluable physics-related conversations and for his helping me develop a mechanism for visualizing various distance algorithms in 3D using JavaScript
@jlaurel - For invaluable literature recommendations that helped me to understand the underlying subject matter of this project.
@annutara - For help in beta-testing this indicator and for sharing many helpful ideas and insights early on in its development.
@jasontaylor7 - For helping to beta-test this indicator and for many helpful conversations that helped to shape my backtesting workflow
@meddymarkusvanhala - For helping to beta-test this indicator
@dlbnext - For incredibly detailed backtesting testing of this indicator and for sharing numerous ideas on how the user experience could be improved.
On Balance Volume Scaled - OBV ScaledThe main idea of this oscillator is to place the OBV oscillator and its oscillation around the range of 0 and around -50 to +50 and for this scaling of the "On Balance Volume" oscillator, I have used Min-max normalization.
Since this oscillator does not have a specific minimum and maximum, just setting the maximum and minimum does not seem the best thing to do. As in this case, we will constantly observe sudden changes and we will have problems such as volatility. On the one hand, we will constantly deal with sudden changes and problems such as volatility. Also on the other hand, the continuous collisions of the high/low(+50 & -50) and index and returning from that is another thing that we are going to deal with.
Therefore, to solve these problems and create more flexible maximum and minimum ranges, another similar method has been used. Choosing the maximum of our normalization to the size of the moving average of 100 candles of the index maximum and choosing the minimum of normalization to the size of the moving average of 100 candles of the minimums of the OBV index, and then normalizing the OBV index with the Min-max method with those ranges, is the recommended method ,which has been used to eliminate problems. In this case, we will not have any problem hitting 50 and returning or hitting -50 and returning. Also, our scaled OBV index will have the ability to touch and cross 50 and -50 and can fluctuate without problems.
Stockbee Momentum BurstThis is a script to color code bars based on the bullish- and bearish combination.
Bullish Combination
Percent: Price >= 4% from yesterday and Volume today > Yesterday
Dollar: Price >= 0.9 dollar from open
Base Requirements
- Price > Yesterday's close
- Price > Open
- Price is within 30% of high
- Todays price range >= Yesterdays price range
- Yesterday's move <= 2%
- Volume >= 100 000
Bearish Combination
Percent: Price <= 4% from yesterday and Volume today > Yesterday
Dollar: Price <= 0.9 dollar from open
Base Requirements
- Price < Yesterday's close
- Price < Open
- Price is within 30% of low
- Todays price range >= Yesterdays price range
- Yesterday's move >= -2%
- Minimum volume for each of last 3 days >= 100 000
Momentum Filter
These are based on the 10 and 20 EMA crossover, where the former above would indicate upward momentum and below downward momentum. This can help to narrow down the color code to continuation phases. The linked option will override all other momentum filters, bullish candles will be displayed when EMA 10 > 20 and bearish candles when EMA 10 < 20.
Double RSI + BBRSI stands for Relative Strength Index.
Bollinger Bands stands for a channel open by standard deviation values plotting upper, lower lines.
Double RSI with Bollinger bands adapted Bollinger bands to RSI not using overlay mode. It tries to filter fake signals while giving more good signals according to volatility even below overbought areas or above oversold areas. This way you can use greater values for RSI, like 25 and 100, increasing smoothness with less market noise.
We added an extra gap spacer to smooth Bollinger bands while widening the channel with a lower multiplier.
I found better results when Fast RSI crosses back into Bollinger bands channel.
You can play with the following settings:
• Source
Close is the most used
• Fast RSI length
Default to 25
• Slow RSI length
Default to 100
• RSI Smoothing
To filter out some graphic noise
• RSI Overbought, Oversold
Regular overbought, oversold lines handled by a single value. For 70/30, set it to 20 although with longer RSI something around 15 is enough.
• Bollinger Spacer
Ads thickness to the channel with lower multiplier
• Bollinger Length
Regular Bollinger length applied to slow RSI
• Bollinger Multiplier
Regular Bollinger multiplier applied to slow RSI
Disclaimer:
For study purposes only, trading without a good risk management can be regrettable, do your own research, always add confirmations, use it as is, at your own risk.
Entry helperHello traders,
This is a script I use daily as a scalper and it helps me a lot, maybe it can help you, this is why I am sharing it!
PART 1 - DESCRIPTION
This program is specifically designed to help scalpers but can be used for all types of trading but won't be as useful.
This script is what I call an entry helper as it calculates dynamically the position size, stop loss and take profit levels and more.
When scalping and placing market entry orders, the price can move significantely while you are calculating your position size according to your stop loss, capital, risk and especially close price that changes very quickly, this results in a risk that is not ideally controlled and personally was a source of frustration and stress. I wanted to enter my quantity and stop loss values as fast as possible and make the process easier.
This script automates the calculation of the position size, stop loss and take profit levels according the the users input and prints the data visibly on the screen so it is easy to copy by the trader. It allows the trader to be confident that his risk is as controlled as possible.
The script is easy to use and set up, this guide will help you if you have any difficulies or questions.
PART 2 - HOW TO USE THE SCRIPT
- SET THE CAPITAL SETTINGS
1 - Set your capital value in $
- SET THE TRADE SETTINGS
2 - Set your trade side (BUY or SELL)
3 - Set you desired risk in % of your capital
- ENTRY SETTINGS
4 - Set your entry from 2 different options
|MARKET| (default option)
This option will place the entry level at the last available price
|LIMIT|
This option allows you to input a fixed price level for the entry
- STOP LOSS SETTINGS
5 - Select your stop loss placement from 4 different options
|EXTREMA STOP LOSS| (default option)
This option will place the stop loss at the highest/lowest (extrema) price level within the last N candles
|ATR EXTREMA|
This option uses the same price level as the EXTREMA STOP LOSS but will add/soustract the last ATR value (calculated on the N last candles) multiplied by a coefficient that you input
|TICKS EXTREMA|
This option uses the same price level as the EXTREMA STOP LOSS but will add/soustract a number of ticks that you input
|PRICE LEVEL|
This option allows you to input a fixed price level for the stop loss
- TAKE PROFIT SETTINGS
6 - Select your take profit from 3 different options
|NONE| (default option)
This option will not display any take profit level, I have added this option as I don't have take profit targets
|RR|
This option uses a risk to reward ratio (reward/risk) that you input, it will automatically calculate the take profit level that corresponds
|PRICE LEVEL|
This option allows you to input a fixed price level for the take profit
- QUANTITY AND FEE SETTINGS
7 - Set the quantity settings, it represents the quantity in a lot (usually 100 000 in forex, 100 in stocks 1 for crypto currencies)
8 - Set the fee per quantity (turning lot)
- VISUAL SETTINGS
9 - Show or remove the tab
- TAB SETTINGS
10 - Select the data that you want to display in the tab (the tab will adapt automatically)
NOTES:
The vertical dashed line shows what candle has been used for the calculation of the stop loss, it allows you to visualize what candle the script has selected in case of an EXTREMA stop loss option.
I hope this helps you out! Any suggestions are welcome and I hope that the guide is clear enough.
Happy trading!
Percent ResearchPercent Research is an indicator that will plot a color / column on the chart in case custom requirements are met.
The requirements are:
- Price : Price requirement (equal or above input).
- Change % Up : Amount the price have moved up in percent (equal or above input).
- Change % Down : Amount the price have moved down in percent (equal or below input).
- Change Interval : Amount of bars the above move happened over.
- Volume : Volume requirement (equal or above input).
- Volume Interval : Amount of bars in a row that each require the above volume.
Example: In case one wants to plot whenever price has made a 20 percent move up or down in a week with minimum 100 000 volume for each of the last 2 days one can use.
Change % Up: 20
Change % Down: -20
Change Interval: 5
Volume: 100 000
Volume Interval: 2
The indicator will plot a color on the chart whenever the requirements are met, which then can be used to look into price action for each colored time period.
The values can be customized dependent on preference, example 100% movers over a month or 20% movers over a week etc.
Ultimate Strategy Template (Advanced Edition)Hello traders
This script is an upgraded version of that one below
New features
- Upgraded to Pinescript version 5
- Added the exit SL/TP now in real-time
- Added text fields for the alerts - easier to send the commands to your trading bots
Step 1: Create your connector
Adapt your indicator with only 2 lines of code and then connect it to this strategy template.
For doing so:
1) Find in your indicator where are the conditions printing the long/buy and short/sell signals.
2) Create an additional plot as below
I'm giving an example with a Two moving averages cross.
Please replicate the same methodology for your indicator wether it's a MACD , ZigZag , Pivots , higher-highs, lower-lows or whatever indicator with clear buy and sell conditions.
//@version=5
indicator(title='Moving Average Cross', shorttitle='Moving Average Cross', overlay=true, precision=6, max_labels_count=500, max_lines_count=500)
type_ma1 = input.string(title='MA1 type', defval='SMA', options= )
length_ma1 = input(10, title=' MA1 length')
type_ma2 = input.string(title='MA2 type', defval='SMA', options= )
length_ma2 = input(100, title=' MA2 length')
// MA
f_ma(smoothing, src, length) =>
rma_1 = ta.rma(src, length)
sma_1 = ta.sma(src, length)
ema_1 = ta.ema(src, length)
iff_1 = smoothing == 'EMA' ? ema_1 : src
iff_2 = smoothing == 'SMA' ? sma_1 : iff_1
smoothing == 'RMA' ? rma_1 : iff_2
MA1 = f_ma(type_ma1, close, length_ma1)
MA2 = f_ma(type_ma2, close, length_ma2)
// buy and sell conditions
buy = ta.crossover(MA1, MA2)
sell = ta.crossunder(MA1, MA2)
plot(MA1, color=color.new(color.green, 0), title='Plot MA1', linewidth=3)
plot(MA2, color=color.new(color.red, 0), title='Plot MA2', linewidth=3)
plotshape(buy, title='LONG SIGNAL', style=shape.circle, location=location.belowbar, color=color.new(color.green, 0), size=size.normal)
plotshape(sell, title='SHORT SIGNAL', style=shape.circle, location=location.abovebar, color=color.new(color.red, 0), size=size.normal)
/////////////////////////// SIGNAL FOR STRATEGY /////////////////////////
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title='🔌Connector🔌', display = display.data_window)
Basically, I identified my buy, sell conditions in the code and added this at the bottom of my indicator code
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title="🔌Connector🔌", transp=100)
Important Notes
🔥 The Strategy Template expects the value to be exactly 1 for the bullish signal, and -1 for the bearish signal
Now you can connect your indicator to the Strategy Template using the method below or that one
Step 2: Connect the connector
1) Add your updated indicator to a TradingView chart
2) Add the Strategy Template as well to the SAME chart
3) Open the Strategy Template settings and in the Data Source field select your 🔌Connector🔌 (which comes from your indicator)
From then, you should start seeing the signals and plenty of other stuff on your chart
🔥 Note that whenever you'll update your indicator values, the strategy statistics and visual on your chart will update in real-time
Settings
- Color Candles: Color the candles based on the trade state ( bullish , bearish , neutral)
- Close positions at market at the end of each session: useful for everything but cryptocurrencies
- Session time ranges: Take the signals from a starting time to an ending time
- Close Direction: Choose to close only the longs, shorts, or both
- Date Filter: Take the signals from a starting date to an ending date
- Set the maximum losing streak length with an input
- Set the maximum winning streak length with an input
- Set the maximum consecutive days with a loss
- Set the maximum drawdown (in % of strategy equity)
- Set the maximum intraday loss in percentage
- Limit the number of trades per day
- Limit the number of trades per week
- Stop-loss: None or Percentage or Trailing Stop Percentage or ATR - I'll add shortly multiple options for the trailing stop loss
- Take-Profit: None or Percentage or ATR - I'll add also a trailing take profit
- Risk-Reward based on ATR multiple for the Stop-Loss and Take-Profit
Special Thanks
Special thanks to @JosKodify as I borrowed a few risk management snippets from his website: kodify.net
Best
Dave
Strategy Myth-Busting #20 - HalfTrend+HullButterfly - [MYN]#20 on the Myth-Busting bench, we are automating the " I Found Super Easy 1 Minute Scalping System And Backtest It 100 Times " strategy from " Jessy Trading " who claims 30.58% net profit over 100 trades in a couple of weeks with a 51% win rate and profit factor of 1.56 on EURUSD .
This one surprised us quite a bit. Despite the title of this strategy indicating this is on the 1 min timeframe, the author demonstrates the backtesting manually on the 5 minute timeframe. Given the simplicity of this strategy only incorporating a couple of indicators, it's robustness being able to be profitable in both low and high timeframes and on multiple symbols was quite refreshing.
The 3 settings which we need to pay most attention to here is the Hull Butterfly length, HalfTrend amplitude and the Max Number Of Bars Between Hull and HalfTrend Trigger. Depending on the timeframe and symbol, these settings greatly impact the performance outcomes of the strategy. I've listed a couple of these below.
And as always, If you know of or have a strategy you want to see myth-busted or just have an idea for one, please feel free to message me.
This strategy uses a combination of 3 open-source public indicators:
Hull Butterfly Oscillator by LuxAlgo
HalfTrend by Everget
Trading Rules
5 min candles but higher / lower candles work too.
Stop loss at swing high/low
Take Profit 1.5x the risk
Long
Hull Butterfly gives us green column, Wait for HalfTrend to present an up arrow and enter trade.
Short
Hull Butterfly gives us a red column , Wait for HalfTrend to present a down arrow and enter trade.
Alternative Trading Settings for different time frames
1 Minute Timeframe
Move the Hull Butterfly length from the default 11 to 9
Move the HalfTrend Amplitude from the default 2 to 1
Enabling ADX Filter with a 25 threshold
2 Hour Timeframe
Move the HalfTrend Amplitude from the default 2 to 1
Laddered Take Profits from 14.5% to 19% with an 8% SL
Volume percentrank[TV1]Volume percentrank
Volume normalized by percentile.
The indicator calculates the percentile of the trading volume . The volume in the base asset or quote asset can be selected as data. To calculate the volume of a quoted asset, the closing price or another standard method for calculating the price of a bar can be used.
A feature of percentile calculation with a small data sample length is low accuracy. Despite the fact that the script allows you to calculate a percentile with a length of 1, using a percentile length less than 100 is not recommended.
The percentile calculation method does not allow correctly calculating the percentile at the beginning of the chart due to the lack of all data in the selection, therefore, when the date of the first bar changes (this happens on small timeframes if the TradingView subscription does not allow you to see all historical data), the indicator will be repainted up to the bar number equal to the percentile sample length.
Huge values of the percentile length may cause a script error. If the indicator doesn't work, just make the percentile length smaller.
Объем, нормализованный по процентилью.
Индикатор вычисляет процентиль объема торгов. В качестве данных может быть выбран объем в базовом(base) активе или котировочном(quote) активе. Для расчета объема в котировочном активе может использоваться цена закрытия либо другой стандартный метод расчета цены бара.
Особенностью расчета процентиля при малой длине выборки данных является малая точность. Не смотря на то, что скрипт позволяет вычиcлить процентиль с длинной 1, использовать длину процентиля меньше 100 не рекомендуется.
Метод расчета процентиля не позволяет корректно рассчитать процентиль в начале графика из-за отсутствия всех данных в выборке, поэтому при изменении даты первого бара (это происходит на малых таймфреймах, если подписка TradingView не позволяет видеть все исторические данные) индикатор подвержен перерисовке вплоть до номера бара равного длине выборки процентиля.
Большие значения длины процентиля могут приводить к ошибке скрипта. Если индикатор не работает, просто сделайте длину процентиля меньше.
Minervini QualifierThe Minervini Qualifier indicator calculates the qualifying conditions from Mark Minervini’s book “Trade like a Stock Market Wizard”.
The condition matching is been shown as fill color inside an SMA 20day envelope curve.
If the envelope color is red, current close price is below the SMA20 and when blue, current close price is above the SMA20. The fill color can be transparent (not matching qualifying conditions), yellow (matching all conditions except close is still below SMA50), green (all conditions match, SMA200 trending for at least one month up) or blue (all conditions match, SMA200 trending up for at least 5 months)
As I wanted also to see which of the qualifying conditions match over time, I’ve added add. lines, each representing one conditions. If it matches, line color is blue, or red if not. Use the data windows (right side), so you know what line represents which condition. Can be turned on/off (default:on)
In addition, a relative strength is been calculated, to compare the stock to a reference index. It is just one possible way to calculate it, might be different to what Mark Minervini is using. If the shown value (top right) is above 100, stock performs better compared to reference index (can be set in settings), when below 100, stock performs worse compared to reference index. Can be turned on/off (default:on)
How to use it:
For more details, read Mark’s book and watch his videos.
Limitations:
It gives only useful information on daily timeframe
(No financial advise, for testing purposes only)
Colorful Channelwhat is "Colorful Channel"?
it is a overbought - oversold indicator.
what it does?
It fills the area between the upper band of the channel and the price line with green, the area between the lower band of the channel and the price line with red color and applies a transparency to these colors according to the distance of the price from the channel lines. thus allowing users to get an idea of ??the current value of the price.
how it does it?
subtracts the percentage of the price from the lower channel from the transparency ratio of the red color, and the percentage of the price from the upper band from the transparency ratio of the green color. thus, a different transparency value is obtained for each percentile. In addition, the transparency rate can be changed with a multiplier determined by the users.
how to use it?
In the "lenght" section, you select the number of bars that the indicator will base backwards on. For example, if length=100, the indicator determines the upper and lower bands according to the distances from the highest and lowest values ??within 100 bar. so the length part works like donchian channels.
You can change the transparency ratio of the colors in the "transp" section.
1 saat transp
yan trend
4 saat
Crypto and FX PSCA simple tool to calculate crypto position size and FX lot size.
How to use:
1. Use TradingView measurement tool or position tool to know how wide is your stop loss.
2. Set the equity and risk parameters.
2. For crypto, input the PERCENTAGE in stop loss;
For FX, input the PIPS.
3. Position size will be displayed in the panel.
Notes:
>Position size is in USDT for Cryptocurrencies
>Lot size for forex.
Forex contract size is your account type set by the broker:
Standard = 100,000 units = ~$10/pip
Mini = 10,000 units = ~$1/pip
Micro = 1,000 units = ~$0.10/pip
Nano = 100 units = ~$0. 01/pip
Credits:
trananhvu149
hanabil
Intrabar Efficiency Ratio█ OVERVIEW
This indicator displays a directional variant of Perry Kaufman's Efficiency Ratio, designed to gauge the "efficiency" of intrabar price movement by comparing the sum of movements of the lower timeframe bars composing a chart bar with the respective bar's movement on an average basis.
█ CONCEPTS
Efficiency Ratio (ER)
Efficiency Ratio was first introduced by Perry Kaufman in his 1995 book, titled "Smarter Trading". It is the ratio of absolute price change to the sum of absolute changes on each bar over a period. This tells us how strong the period's trend is relative to the underlying noise. Simply put, it's a measure of price movement efficiency. This ratio is the modulator utilized in Kaufman's Adaptive Moving Average (KAMA), which is essentially an Exponential Moving Average (EMA) that adapts its responsiveness to movement efficiency.
ER's output is bounded between 0 and 1. A value of 0 indicates that the starting price equals the ending price for the period, which suggests that price movement was maximally inefficient. A value of 1 indicates that price had travelled no more than the distance between the starting price and the ending price for the period, which suggests that price movement was maximally efficient. A value between 0 and 1 indicates that price had travelled a distance greater than the distance between the starting price and the ending price for the period. In other words, some degree of noise was present which resulted in reduced efficiency over the period.
As an example, let's say that the price of an asset had moved from $15 to $14 by the end of a period, but the sum of absolute changes for each bar of data was $4. ER would be calculated like so:
ER = abs(14 - 15)/4 = 0.25
This suggests that the trend was only 25% efficient over the period, as the total distanced travelled by price was four times what was required to achieve the change over the period.
Intrabars
Intrabars are chart bars at a lower timeframe than the chart's. Each 1H chart bar of a 24x7 market will, for example, usually contain 60 intrabars at the LTF of 1min, provided there was market activity during each minute of the hour. Mining information from intrabars can be useful in that it offers traders visibility on the activity inside a chart bar.
Lower timeframes (LTFs)
A lower timeframe is a timeframe that is smaller than the chart's timeframe. This script determines which LTF to use by examining the chart's timeframe. The LTF determines how many intrabars are examined for each chart bar; the lower the timeframe, the more intrabars are analyzed, but fewer chart bars can display indicator information because there is a limit to the total number of intrabars that can be analyzed.
Intrabar precision
The precision of calculations increases with the number of intrabars analyzed for each chart bar. As there is a 100K limit to the number of intrabars that can be analyzed by a script, a trade-off occurs between the number of intrabars analyzed per chart bar and the chart bars for which calculations are possible.
Intrabar Efficiency Ratio (IER)
Intrabar Efficiency Ratio applies the concept of ER on an intrabar level. Rather than comparing the overall change to the sum of bar changes for the current chart's timeframe over a period, IER compares single bar changes for the current chart's timeframe to the sum of absolute intrabar changes, then applies smoothing to the result. This gives an indication of how efficient changes are on the current chart's timeframe for each bar of data relative to LTF bar changes on an average basis. Unlike the standard ER calculation, we've opted to preserve directional information by not taking the absolute value of overall change, thus allowing it to be utilized as a momentum oscillator. However, by taking the absolute value of this oscillator, it could potentially serve as a replacement for ER in the design of adaptive moving averages.
Since this indicator preserves directional information, IER can be regarded as similar to the Chande Momentum Oscillator (CMO) , which was presented in 1994 by Tushar Chande in "The New Technical Trader". Both CMO and ER essentially measure the same relationship between trend and noise. CMO simply differs in scale, and considers the direction of overall changes.
█ FEATURES
Display
Three different display types are included within the script:
• Line : Displays the middle length MA of the IER as a line .
Color for this display can be customized via the "Line" portion of the "Visuals" section in the script settings.
• Candles : Displays the non-smooth IER and two moving averages of different lengths as candles .
The `open` and `close` of the candle are the longest and shortest length MAs of the IER respectively.
The `high` and `low` of the candle are the max and min of the IER, longest length MA of the IER, and shortest length MA of the IER respectively.
Colors for this display can be customized via the "Candles" portion of the "Visuals" section in the script settings.
• Circles : Displays three MAs of the IER as circles .
The color of each plot depends on the percent rank of the respective MA over the previous 100 bars.
Different colors are triggered when ranks are below 10%, between 10% and 50%, between 50% and 90%, and above 90%.
Colors for this display can be customized via the "Circles" portion of the "Visuals" section in the script settings.
With either display type, an optional information box can be displayed. This box shows the LTF that the script is using, the average number of lower timeframe bars per chart bar, and the number of chart bars that contain LTF data.
Specifying intrabar precision
Ten options are included in the script to control the number of intrabars used per chart bar for calculations. The greater the number of intrabars per chart bar, the fewer chart bars can be analyzed.
The first five options allow users to specify the approximate amount of chart bars to be covered:
• Least Precise (Most chart bars) : Covers all chart bars by dividing the current timeframe by four.
This ensures the highest level of intrabar precision while achieving complete coverage for the dataset.
• Less Precise (Some chart bars) & More Precise (Less chart bars) : These options calculate a stepped LTF in relation to the current chart's timeframe.
• Very precise (2min intrabars) : Uses the second highest quantity of intrabars possible with the 2min LTF.
• Most precise (1min intrabars) : Uses the maximum quantity of intrabars possible with the 1min LTF.
The stepped lower timeframe for "Less Precise" and "More Precise" options is calculated from the current chart's timeframe as follows:
Chart Timeframe Lower Timeframe
Less Precise More Precise
< 1hr 1min 1min
< 1D 15min 1min
< 1W 2hr 30min
> 1W 1D 60min
The last five options allow users to specify an approximate fixed number of intrabars to analyze per chart bar. The available choices are 12, 24, 50, 100, and 250. The script will calculate the LTF which most closely approximates the specified number of intrabars per chart bar. Keep in mind that due to factors such as the length of a ticker's sessions and rounding of the LTF, it is not always possible to produce the exact number specified. However, the script will do its best to get as close to the value as possible.
Specifying MA type
Seven MA types are included in the script for different averaging effects:
• Simple
• Exponential
• Wilder (RMA)
• Weighted
• Volume-Weighted
• Arnaud Legoux with `offset` and `sigma` set to 0.85 and 6 respectively.
• Hull
Weighting
This script includes the option to weight IER values based on the percent rank of absolute price changes on the current chart's timeframe over a specified period, which can be enabled by checking the "Weigh using relative close changes" option in the script settings. This places reduced emphasis on IER values from smaller changes, which may help to reduce noise in the output.
█ FOR Pine Script™ CODERS
• This script imports the recently published lower_ltf library for calculating intrabar statistics and the optimal lower timeframe in relation to the current chart's timeframe.
• This script uses the recently released request.security_lower_tf() Pine Script™ function discussed in this blog post .
It works differently from the usual request.security() in that it can only be used on LTFs, and it returns an array containing one value per intrabar.
This makes it much easier for programmers to access intrabar information.
• This script implements a new recommended best practice for tables which works faster and reduces memory consumption.
Using this new method, tables are declared only once with var , as usual. Then, on the first bar only, we use table.cell() to populate the table.
Finally, table.set_*() functions are used to update attributes of table cells on the last bar of the dataset.
This greatly reduces the resources required to render tables.
Look first. Then leap.
Encoder DecoderLibrary "EncoderDecoder"
Simple example how to encode some values into float number and then decode it back to original values
f_calctype()
Encode parameter
Returns: encoded value
f_calctype()
Decode parameter
Returns: decoded value
f_srctype()
Encode parameter
Returns: encoded value
f_srctype()
Decode parameter
Returns: decoded value
f_encode(calc_type, src_type, tf, length)
Encodes 4 paramters into float number
Parameters:
calc_type : 1st paramter to encode (its values defined in f_calctype functions) max number of values that can be encoded = 100
src_type : 2nd paramter to encode (its values defined in f_src_type functions) max number of values that can be encoded = 100
tf : 3rd paramter to encode (may be int number with format.price precision length!)
length : 4th paramter to encode (may be any int number)
Returns: float number
f_decode()
Decodes 4 paramters into tuple
Returns: tuple
