LengthAdaptationCollection of dynamic length adaptation algorithms. Mostly from various Adaptive Moving Averages (they are usually just EMA otherwise). Now you can combine Adaptations with any other Moving Averages or Oscillators (see my other libraries), to get something like Deviation Scaled RSI or Fractal Adaptive VWMA. This collection is not encyclopaedic. Suggestions are welcome.
chande(src, len, sdlen, smooth, power) Chande's Dynamic Length
Parameters:
src : Series to use
len : Reference lookback length
sdlen : Lookback length of Standard deviation
smooth : Smoothing length of Standard deviation
power : Exponent of the length adaptation (lower is smaller variation)
Returns: Calculated period
Taken from Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length
Original default power value is 1, but I use 0.5
A variant of this algorithm is also included, where volume is used instead of price
vidya(src, len, dynLow) Variable Index Dynamic Average Indicator (VIDYA)
Parameters:
src : Series to use
len : Reference lookback length
dynLow : Lower bound for the dynamic length
Returns: Calculated period
Standard VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
I took the adaptation part, as it is just an EMA otherwise
vidyaRS(src, len, dynHigh) Relative Strength Dynamic Length - VIDYA RS
Parameters:
src : Series to use
len : Reference lookback length
dynHigh : Upper bound for the dynamic length
Returns: Calculated period
Based on Vitali Apirine's modification (Stocks and Commodities, January 2022) of VIDYA algorithm. The period oscillates from the Upper Bound down (fast)
I took the adaptation part, as it is just an EMA otherwise
kaufman(src, len, dynLow, dynHigh) Kaufman Efficiency Scaling
Parameters:
src : Series to use
len : Reference lookback length
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
Returns: Calculated period
Based on Efficiency Ratio calculation orifinally used in Kaufman Adaptive Moving Average developed by Perry J. Kaufman
I took the adaptation part, as it is just an EMA otherwise
ds(src, len) Deviation Scaling
Parameters:
src : Series to use
len : Reference lookback length
Returns: Calculated period
Based on Derivation Scaled Super Smoother (DSSS) by John F. Ehlers
Originally used with Super Smoother
RMS originally has 50 bar lookback. Changed to 4x length for better flexibility. Could be wrong.
maa(src, len, threshold) Median Average Adaptation
Parameters:
src : Series to use
len : Reference lookback length
threshold : Adjustment threshold (lower is smaller length, default: 0.002, min: 0.0001)
Returns: Calculated period
Based on Median Average Adaptive Filter by John F. Ehlers
Discovered and implemented by @cheatcountry:
I took the adaptation part, as it is just an EMA otherwise
fra(len, fc, sc) Fractal Adaptation
Parameters:
len : Reference lookback length
fc : Fast constant (default: 1)
sc : Slow constant (default: 200)
Returns: Calculated period
Based on FRAMA by John F. Ehlers
Modified to allow lower and upper bounds by an unknown author
I took the adaptation part, as it is just an EMA otherwise
mama(src, dynLow, dynHigh) MESA Adaptation - MAMA Alpha
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
Returns: Calculated period
Based on MESA Adaptive Moving Average by John F. Ehlers
Introduced in the September 2001 issue of Stocks and Commodities
Inspired by the @everget implementation:
I took the adaptation part, as it is just an EMA otherwise
doAdapt(type, src, len, dynLow, dynHigh, chandeSDLen, chandeSmooth, chandePower) Execute a particular Length Adaptation from the list
Parameters:
type : Length Adaptation type to use
src : Series to use
len : Reference lookback length
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
chandeSDLen : Lookback length of Standard deviation for Chande's Dynamic Length
chandeSmooth : Smoothing length of Standard deviation for Chande's Dynamic Length
chandePower : Exponent of the length adaptation for Chande's Dynamic Length (lower is smaller variation)
Returns: Calculated period (float, not limited)
doMA(type, src, len) MA wrapper on wrapper: if DSSS is selected, calculate it here
Parameters:
type : MA type to use
src : Series to use
len : Filtering length
Returns: Filtered series
Demonstration of a combined indicator: Deviation Scaled Super Smoother
Wyszukaj w skryptach "俄罗斯军工股+俄乌战争+股价走势+2022"
ICT Index Futures Vertical LinesLearning to trade is complicated enough, so to make the process it little less daunting, I decided to create something helpful.
This indicator relieves you of drawing the same lines and levels over and over each trading day.
It also provides key price levels for you to watch when the trading session starts.
This project is inspired by the ICT 2022 Mentorship.
This indicator was designed and tested to practice and trade the CME Index Futures like Nasdaq, S&P500 and the DOW.
The concepts by ICT are known to work on other markets like Crypto, but I haven't tested that so use at your own risk.
Features:
When a new trading day starts, the following lipstick is put on the chart:
Vertical lines:
- Globex (Overnight) Session Start @17.00
- New York Midnight @ 00:00
- New York AM Session Start @ 08.30
- CME Open @ 09.30
- New York Lunch Start @12.00
- New York PM Session Start @13.00
- New York PM Session End @ 16.30
Important levels:
- Globex (Overnight) Session Opening price
- Globex (Overnight) Session High
- Globex (Overnight) Session Low
- New York Mignight Opening Price
Additional features:
- Shows the day of the week at the bottom for your convenience
- London Killzone Vertical lines
- London Killzone Highlight
- NY Lunch No-Trade-Zone Highlight
- Important levels have a small label to show their meaning and price
- Almost everything is customizable: colors, line types, times, etc
- Customizable timezone setting in case you don't want to work on ICT's recommended timezone (New York UTC-4)
- Toggle to Show only Today's drawings on the chart. You can choose to display all chart lipstick from almost a month of trading data to do your research. Ideal if you want to determine the daily profiles for example.
Screenshots:
London Killzone Highlight turned on:
Multi Day Lipstick:
Hammers & Stars Candle [2022]Hammer and Shooting Star Candle analysis >)
“The home crowd have been right behind their team and there are boos for the officials as they walk off at half-time. Elanga was unfortunate not to win the free-kick, although the clearer foul on him had come before the final tumble. He is entitled to think the ref would bring that back. Instead, United are a goal down.
PCT Trailing Stoploss-Strategy
I am not a financial advisor and this is not financial advice. This is provided for illustration only for a Percent based Trailing Stoploss.
I have been looking for a Percent based Trailing Stoploss and have not been able to find one that would work for me so I wrote my own. This works in both a Strategies and Indicators I put comments inline of where to add or remove comments// depending on which one you are aiming for. The simple EMA crossovers are there just to give the strategy something to do. To use, copy the Stoploss section, the inputs at the top, and the reset under the sell section to your own script. When I first started out I found the code for removing the redundant signals and then used it in most of my scripts. For the life of me I do not remember where I got it - either in a comment in Tradingview or on a reddit post, so if you wrote that part, thank you.
About me:
I have only been working with PineScript since January 2022 and have never been much of a coder from a professional standpoint but decided to jump in and learn Pine mainly because I am a crap, emotional trader and this way I can remove that aspect of it and lose my money programmatically as opposed to making it a conscious decision :)
Bitcoin Power Law Bands (BTC Power Law) Indicator█ OVERVIEW
The 'Bitcoin Power Law Bands' indicator is a set of three US dollar price trendlines and two price bands for bitcoin , indicating overall long-term trend, support and resistance levels as well as oversold and overbought conditions. The magnitude and growth of the middle (Center) line is determined by double logarithmic (log-log) regression on the entire USD price history of bitcoin . The upper (Resistance) and lower (Support) lines follow the same trajectory but multiplied by respective (fixed) factors. These two lines indicate levels where the price of bitcoin is expected to meet strong long-term resistance or receive strong long-term support. The two bands between the three lines are price levels where bitcoin may be considered overbought or oversold.
All parameters and visuals may be customized by the user as needed.
█ CONCEPTS
Long-term models
Long-term price models have many challenges, the most significant of which is getting the growth curve right overall. No one can predict how a certain market, asset class, or financial instrument will unfold over several decades. In the case of bitcoin , price history is very limited and extremely volatile, and this further complicates the situation. Fortunately for us, a few smart people already had some bright ideas that seem to have stood the test of time.
Power law
The so-called power law is the only long-term bitcoin price model that has a chance of survival for the years ahead. The idea behind the power law is very simple: over time, the rapid (exponential) initial growth cannot possibly be sustained (see The seduction of the exponential curve for a fun take on this). Year-on-year returns, therefore, must decrease over time, which leads us to the concept of diminishing returns and the power law. In this context, the power law translates to linear growth on a chart with both its axes scaled logarithmically. This is called the log-log chart (as opposed to the semilog chart you see above, on which only one of the axes - price - is logarithmic).
Log-log regression
When both price and time are scaled logarithmically, the power law leads to a linear relationship between them. This in turn allows us to apply linear regression techniques, which will find the best-fitting straight line to the data points in question. The result of performing this log-log regression (i.e. linear regression on a log-log scaled dataset) is two parameters: slope (m) and intercept (b). These parameters fully describe the relationship between price and time as follows: log(P) = m * log(T) + b, where P is price and T is time. Price is measured in US dollars , and Time is counted as the number of days elapsed since bitcoin 's genesis block.
DPC model
The final piece of our puzzle is the Dynamic Power Cycle (DPC) price model of bitcoin . DPC is a long-term cyclic model that uses the power law as its foundation, to which a periodic component stemming from the block subsidy halving cycle is applied dynamically. The regression parameters of this model are re-calculated daily to ensure longevity. For the 'Bitcoin Power Law Bands' indicator, the slope and intercept parameters were calculated on publication date (March 6, 2022). The slope of the Resistance Line is the same as that of the Center Line; its intercept was determined by fitting the line onto the Nov 2021 cycle peak. The slope of the Support Line is the same as that of the Center Line; its intercept was determined by fitting the line onto the Dec 2018 trough of the previous cycle. Please see the Limitations section below on the implications of a static model.
█ FEATURES
Inputs
• Parameters
• Center Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the grey line in the middle
• Resistance Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the red line at the top
• Support Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the green line at the bottom
• Controls
• Plot Line Fill: N/A
• Plot Opportunity Label: Controls the display of current price level relative to the Center, Resistance and Support Lines
Style
• Visuals
• Center: Control, color, opacity, thickness, price line control and line style of the Center Line
• Resistance: Control, color, opacity, thickness, price line control and line style of the Resistance Line
• Support: Control, color, opacity, thickness, price line control and line style of the Support Line
• Plots Background: Control, color and opacity of the Upper Band
• Plots Background: Control, color and opacity of the Lower Band
• Labels: N/A
• Output
• Labels on price scale: Controls the display of current Center, Resistance and Support Line values on the price scale
• Values in status line: Controls the display of current Center, Resistance and Support Line values in the indicator's status line
█ HOW TO USE
The indicator includes three price lines:
• The grey Center Line in the middle shows the overall long-term bitcoin USD price trend
• The red Resistance Line at the top is an indication of where the bitcoin USD price is expected to meet strong long-term resistance
• The green Support Line at the bottom is an indication of where the bitcoin USD price is expected to receive strong long-term support
These lines envelope two price bands:
• The red Upper Band between the Center and Resistance Lines is an area where bitcoin is considered overbought (i.e. too expensive)
• The green Lower Band between the Support and Center Lines is an area where bitcoin is considered oversold (i.e. too cheap)
The power law model assumes that the price of bitcoin will fluctuate around the Center Line, by meeting resistance at the Resistance Line and finding support at the Support Line. When the current price is well below the Center Line (i.e. well into the green Lower Band), bitcoin is considered too cheap (oversold). When the current price is well above the Center Line (i.e. well into the red Upper Band), bitcoin is considered too expensive (overbought). This idea alone is not sufficient for profitable trading, but, when combined with other factors, it could guide the user's decision-making process in the right direction.
█ LIMITATIONS
The indicator is based on a static model, and for this reason it will gradually lose its usefulness. The Center Line is the most durable of the three lines since the long-term growth trend of bitcoin seems to deviate little from the power law. However, how far price extends above and below this line will change with every halving cycle (as can be seen for past cycles). Periodic updates will be needed to keep the indicator relevant. The user is invited to adjust the slope and intercept parameters manually between two updates of the indicator.
█ RAMBLINGS
The 'Bitcoin Power Law Bands' indicator is a useful tool for users wishing to place bitcoin in a macro context. As described above, the price level relative to the three lines is a rough indication of whether bitcoin is over- or undervalued. Users wishing to gain more insight into bitcoin price trends may follow the author's periodic updates of the DPC model (contact information below).
█ NOTES
The author regularly posts on Twitter using the @DeFi_initiate handle.
█ THANKS
Many thanks to the following individuals, who - one way or another - made the 'Bitcoin Power Law Bands' indicator possible:
• TradingView user 'capriole_charles', whose open-source 'Bitcoin Power Law Corridor' script was the basis for this indicator
• Harold Christopher Burger, whose Bitcoin’s natural long-term power-law corridor of growth article (2019) was the basis for the 'Bitcoin Power Law Corridor' script
• Bitcoin Forum user "Trololo", who posted the original power law model at Logarithmic (non-linear) regression - Bitcoin estimated value (2014)
Trend Follower Strategy v2 [divonn1994]The Trend Follower Strategy that I made classifies red and green candles into tiny, small, and big sizes and will send buy or sell signals depending on if the candle is classified as "big" so you get into and out of a position when there is a big candle. Out during a big green candle to take profit. Out during a big red candle in case the market is turning down. It also won't enter a position unless there is positive EMA momentum.
For the chart there is a Buy and a Sell signal. Buy = 1, Sell = 0, and when the value crosses above or below 0.5 it will trigger a long position or close the long position. The graph isn't necessary to the strategy, but can help with visualizing the trade patterns in the past if you like.
This strategy works best so far with these coins at time of posting (March 4th, 2022):
KCSUSDT (621x profit), HTUSDT (45x profit), LUNAUSDT (45x profit), BNBBTC (1553x profit), ETHBTC (219x profit), KCSBTC (1222x profit), LUNABTC (83x profit), FTMBTC (52x profit).
It can work with other pairings, but I personally like these pairings best. I didn't test it with coins outside of the top 100 coins by market cap. Use it however you want.
Works best on 1 Day charts.
The strategy would rather be in the market than out. It gets out when it see's a red flag, but can immediately go back in in the next bar if the red flags are all gone. So it makes a lot of trades.
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Update: This is the same strategy I uploaded before but I made the code Open for anyone to check it out and so it has a similar description as the previous version. Let me know what you think. I'd remove the old version if I could, but I guess it's site policy to not be able to remove scripts that have been uploaded.
SET13_INDEXThe average RSI of top 13 marketcaptilization in SET50, as list by below.
1. PTT
2. AOT
3. ADVANC
4. CPALL
5. GULF
6. PTTEP
7. SCC
8. SCB
9. KBANK
10. BDMS
11. EA
12. OR
13. SCGP
Note that OR started trading on February 2021 so that the indicator will not appear before that period and
the top 13 marketcapiliation ranked on 22 February 2022 so pls becareful about look ahead bias.
Relative Strength Volatility Adjusted Ema [CC]The Relative Strength Volatility Adjusted Exponential Moving Average was created by Vitali Apirine (Stocks and Commodities Mar 2022) and this is his final indicator of his recent Relative Strength series. I published both of the previous indicators, Relative Strength Volume Adjusted Exponential Moving Average and Relative Strength Exponential Moving Average
This indicator is particularly unique because it uses the Volatility Index (VIX) symbol as the default to determine volatility and uses this in place of the current stock's price into a typical relative strength calculation. As you can see in the chart, it follows the price much closer than the other two indicators and so of course this means that this indicator is best for choppy markets and the other two are better for trending markets. I would of course recommend to experiment with this one and see what works best for you.
I have included strong buy and sell signals in addition to normal ones so strong signals are darker in color and normal signals are lighter in color. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators or scripts you would like to see me publish!
JPM VIX Signal - Non OverlayJPMorgan Chase & Co . strategists have identified what they say is a near bulletproof indicator to strengthen their argument that stock markets are poised to rally.
The buy signal is triggered when the Cboe Volatility Index ( VIX ) rises by more than 50% of its 1-month (30 day) moving average, which it last did on January 25th 2022, according to the strategists led by Mislav Matejka. The indicator has proven 100% accurate outside of recessions over the last three decades.
Instructions:
Symbol - SPY
Timeframe - Daily
Signal - Indicator exceeds horizontal line of 1.5
JPMorgan VIX Buy SignalJPMorgan Chase & Co. strategists have identified what they say is a near bulletproof indicator to strengthen their argument that stock markets are poised to rally.
The buy signal is triggered when the Cboe Volatility Index (VIX) rises by more than 50% of its 1-month (30 day) moving average, which it last did on January 25th 2022, according to the strategists led by Mislav Matejka. The indicator has proven 100% accurate outside of recessions over the last three decades.
Instructions:
Symbol - VIX
Timeframe - Daily
Red Triangle - Close / 30 Day SMA >= 1.5
INVEST BTC RSI (from @tradinglord)RSI VERSION - no alerts
The script highlight point of interest for investors using EMA , RSI and a bit of criticism.
The script is built to be used on a weekly timeframe
When RSI is bellow 35 it can be interesting to invest in BTC , on the opposite when it is above 80 can be where to take a bit of profits.
Also using EMA to flow with the power of the trend or change your bias depending on conditions.
Feel free to use the included alerts to be informed when RSI is telling you something.
The idea is quite simple, and you will not gain x100 your investment, but with these kind of investments and some patience you could make your way out.
Obviously not financial advice, understand what you are doing.
"Sometimes it's better to be rational monkey than a greedy baboon" - Tradinglord 2022
INVEST BTC (from @tradinglord)The script highlight point of interest for investors using EMA, RSI and a bit of criticism.
The script is built to be used on a weekly timeframe
When RSI is bellow 35 it can be interesting to invest in BTC, on the opposite when it is above 80 can be where to take a bit of profits.
Also using EMA to flow with the power of the trend or change your bias depending on conditions.
Feel free to use the included alerts to be informed when RSI is telling you something.
The idea is quite simple, and you will not gain x100 your investment, but with these kind of investments and some patience you could make your way out.
Obviously not financial advice, understand what you are doing.
"Sometimes it's better to be rational monkey than a greedy baboon" - Tradinglord 2022
FX Mini-Day/Index Dividers V2This is a combination of the Mini-Day Separator Indicator, timings based off the research by Tom Henstridge/@LiquiditySniper and additional Index KZ delineations, based on ICT's 2022 Youtube Mentorship.
*It borrows some minor code from Enricoamato997 . Credit where it is due!
This is a joint effort by myself, @vbwilkes / Offseason Vince and @Tom_FOREX / TraderTom on the Index/Index Future portion.
Index Future Example
Forex Example
Ehlers Hann Relative Strength Index [CC]The Hann Relative Strength Index was created by John Ehlers (Stocks and Commodities Jan 2022 pgs 26-28) and this indicator builds upon his Hann Window Indicator to create an unique rsi indicator that doesn't rely on overbought or oversold levels to determine a reversal point and also provides a very superior smoothing without any of the lag associated with traditional smoothing. A much more useful RSI than the standard version in my honest opinion. Short term you buy when the line turns green and sell when it turns red. Medium to long term you buy when the indicator rises above the 0 line and sell when it falls below the 0 line. I have included strong buy and sell signals in addition to normal ones so strong signals are darker in color and normal signals are lighter in color.
Let me know if there are any other indicators or scripts you would like to see me publish!
Ehlers Elegant Oscillator [CC]The Elegant Oscillator was created by John Ehlers (Stocks and Commodities Feb 2022 pg 21) and for those of you who don't know, he introduced the indicators for the Fisher Transform and Inverse Fisher Transform and this is a new updated version to that idea based on his latest research. This uses a soft limiter which he says is superior to a hard limiter. There are several ways to interpret this indicator. First if the indicator is above the 0 line then it is a long term bullish trend and below 0 a long term bearish trend. Second this indicator can be used for reversal points with the peaks and valleys. Finally when the indicator line starts moving higher for example it is a bullish short term trend and vice versa. I have included strong buy and sell signals in addition to normal ones so strong signals are darker in color and normal are lighter in color. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators or scripts you would like to see me publish!
Relative Strength Volume Adjusted Exponential Moving Avg [CC]The Relative Strength Volume Adjusted Exponential Moving Average was created by Vitali Apirine (Stocks and Commodities Feb 2022 pgs 14-18) and this is very similar of course to the last Relative Strength Exponential Moving Average . It works under the same concept with using overbought and oversold methods to adjust the moving average and with this particular version you will notice that sudden drops or increases won't follow super closely so this can be useful along with the other as a good complementary indicator to use with each other to determine the short and medium term trend and to give good entry and exit points. I have strong buy and sell signals in addition to normal ones so darker colors are strong and lighter colors are normal. Buy when the indicator line turns green and sell when it turns red.
Let me know if there are any other indicators or scripts you would like to see me publish!
McNamara Tally [CC]The McNamara's Tally was created by Nolan McNamara (Stocks and Commodities Feb 2022 pgs 44-45) and this aims to fix the issues with both the On Balance Volume and the Accumulation/Distribution Line by using a variation of Wilder's True Range to keep track of volume flow to better differentiate between bullish volume and bearish volume. I added a signal line to this indicator to provide clear buy and sell signals since the original didn't' have any so feel free to experiment and see if you come up with a better signal system. Buy when the indicator line turns green and sell when it turns red. I have included strong buy and sell signals in addition to normal ones so stronger signals are darker in color and normal signals are lighter in color.
Let me know if there are any other indicators or scripts you would like to see me publish!
Relative Strength Exponential Moving Average [CC]The Relative Strength Exponential Moving Average was created by Vitali Apirine (Stocks and Commodities Jan 2022 pgs 22-25) and this is a handy moving average that combines a typical overbought/oversold mechanic with an overall trend indicator. Even though the typical length is so large it reacts extremely quickly when the stock becomes overbought or oversold. Because of this the indicator by itself doesn't work as well during choppy periods so Vitali recommends using a moving average crossover system during choppy so do one indicator with the default length of 50 and use a different length of 10 so when the shorter length crosses over the longer length then buy and vice versa you would sell. Generally speaking buy when the line turns green and sell when it turns red. I have used strong buy and sell signals in addition to normal signals so strong signals are darker in color and normal signals are lighter in color.
Let me know if there are any other scripts or indicators you would like me to publish!
FII and DII Data
Greetings to All of you.
The script is plotting FII & DII activity of buying or selling on Daily TimeFrame of Nifty Spot.
Will display only on Nifty 50 Spot on Daily TimeFrame . Codes are hardcoded because novice to PineScripting.
Data is collected from NSE website on daily basis, it's an manual process.
Observation:
Start date of observation is 16th Nov., 2021. FII bought worth 14,240 & kept buying for next 2 days & bought stocks worth 17,760 crores. But market kept falling as we can see in the chart.
Now FII's started selling & in next 5 days they sold stocks worth 18,698 crores. What makes sense from this is might have cut their losses early. FII's kept selling & Nifty made an low of 16410.
FII had sold stocks worth -21,954 crores.
FII are negative & the top green box which you see is FII & DII activity from 19th Oct 2021 when Nifty Spot made an High of 18604.45.
As of 14th Jan.,2022 they are still Negative & DII are extremely positive.
NOTE: DII have not sold any thing yet. They are PLUS +74,428 Crore . Now if they start selling we need to take care of our portfolio.
Hope the information might help in someway.
Take Care & Stay Safe why because Health is Wealth.
PS: If you have any better way to improve the hard coded codes please enlighten. Thank you in advance
Period Dollar Cost Average BacktesterHere is a simple script to calculate the profits and other dollar cost average strategy statistics. This strategy was created to avoid asset price volatility, so the pump and dump scheme does not affect the portfolio. By dividing the investment amount into periods, the investor doesn’t need to analyze the market, fundamental analysis, or anything. The goal is to increase the asset holdings and avoid fast and robust price movements.
This indicator has some configurations.
Amount to buy: the amount to buy at each time
Broker fee %: the fee percentage that the broker has for spot trade
Frequency: the frequency of the investments. Example: 1 Day means that every day, it will buy an amount of the asset
Starting Date: when the indicator will start the investment simulation
Ending Date: when the indicator will end the investment simulation
InfoCell With/Height: it relates to the panel for view purposes. Change the values to fit better on your screen.
This indicator has three lines:
Total Invested (green): total amount invested at the end of the period
Total Net Profit (pink): total profit by converting the amount of the asset bought at the latest closing price
Holding Profits (yellow): the amount that would be in the portfolio if the investor had invested all the capital in a signal trade at the beginning of the period.
The statistics panel has some information to help you understand buying the asset in one or more trades. So, besides those three lines that were mentioned above, here are the other statistics:
Entry Price: The price of the asset when the first investment was made
Gross Profit: Total amount of profit, not excluding the losses
Gross Losses: Total amount of losses, not excluding the profits
Profit Factor: The Gross Profit divided by the Gross Loss. A value above 1 means it’s profitable.
Profit/Trades: Net profit per trade. This includes the broker fees.
Recovery Factor: The Net profit divided by the relative drawdown. The higher the recovery factor, the faster the recovery of a loss
Total Asset Bought: The amount of the asset that was bought at the end of the investment plan
Absolute Drawdown: The total amount of losses that made the account balance go below its initial value
Relative Drawdown: The max drawdown that occurred, no matter the account balance amount
Total Trades: number of times the investment was made in the selected period
Total Fee: total Fee that was spent on the total investment
Total Winning Trades: the total amount of winning trades. A trade is considered a winner if the net profit is up compared with the latest investment.
Total Losing Trades: the total amount of losing trades. A trade is considered a loser if the net profit is down compared to the latest investment.
Max consecutive wins: the max amount of consecutive winning trades
Max consecutive losses: the max amount of consecutive losing trades
The chart above uses the default configuration of the indicator. Placed on the BTCUSD market, taking the time range of January 1st, 2018 to January 1st, 2022, 4 years. Buying a BTC amount with 10 USDT every day in that period would generate a more than 500% profit. Compared to the profit amount by just holding the count, which was close to 350% profit, the dollar cost average by period would be much more profitable.
Drawdown + Labels BINANCE:BTCUSDT
Indicador de reducciones de precio con etiqueta.
El indicador toma por defecto el valor máximo histórico y a partir de ese valor realiza los siguientes cálculos:
Reducción del 50% = Máximo Histórico*(50/100)
Reducción del 60% = Máximo Histórico*(40/100)
Reducción del 70% = Máximo Histórico*(30/100)
Reducción del 80% = Máximo Histórico*(20/100)
Reducción del 90% = Máximo Histórico*(10/100)
En el grafico se mostrará una etiqueta a la derecha por defecto, el valor que corresponde a cada reducción.
Ejemplo:
Fecha: 04 de Enero de 2022
Máximo Histórico de BTC = $ 69,000 (Línea color Naranja)
Reducción del 50% = $ 34,500 (Línea color Morada)
Reducción del 60% = $ 27,600 (Línea color Marrón)
Reducción del 70% = $ 20,700 (Línea color Verde)
Reducción del 80% = $ 13,800 (Línea color Roja)
Reducción del 90% = $ 6,900 (Línea color Aqua)
Reducción del 100% = $ 0 (Línea color Negro)
Espero les ayude, saludos.
Kitti-Playbook Fibonacci retracement GAGE Click Start Date Jan 1 2022
OVERVIEW :Kitti-Playbook Fibonacci retracement GAGE R0.00
Easy for visualize Fibonacci retracement level by point then click
CONCEPTS
0)Point and Click on chart to point Origin
1)Calculate Minimum Line from Origin point
2)Calculate Maximum Line from New Low
3)Calculation Fibo scale
a) Fibonacci Retracement of 61.8% form Maximum level , Default = close
b) Fibonacci Retracement of 78.6% form Maximum level . Default = close
c) Fibonacci Retracement of 88.7% form Maximum level
d) Fibonacci Retracement of 94.2% form Maximum level
4)Information Display
GAGE Scale Number from origin point
Highest Lowest Value from origin point
pandas_taLibrary "pandas_ta"
Level: 3
Background
Today is the first day of 2022 and happy new year every tradingviewers! May health and wealth go along with you all the time. I use this chance to publish my 1st PINE v5 lib : pandas_ta
This is not a piece of cake like thing, which cost me a lot of time and efforts to build this lib. Beyond 300 versions of this script was iterated in draft.
Function
Library "pandas_ta"
PINE v5 Counterpart of Pandas TA - A Technical Analysis Library in Python 3 at github.com
The Original Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns.
I realized most of indicators except Candlestick Patterns because tradingview built-in Candlestick Patterns are even more powerful!
I use this to verify pandas_ta python version indicators for myself, but I realize that maybe many may need similar lib for pine v5 as well.
Function Brief Descriptions (Pls find details in script comments)
bton --> Binary to number
wcp --> Weighted Closing Price (WCP)
counter --> Condition counter
xbt --> Between
ebsw --> Even Better SineWave (EBSW)
ao --> Awesome Oscillator (AO)
apo --> Absolute Price Oscillator (APO)
xrf --> Dynamic shifted values
bias --> Bias (BIAS)
bop --> Balance of Power (BOP)
brar --> BRAR (BRAR)
cci --> Commodity Channel Index (CCI)
cfo --> Chande Forcast Oscillator (CFO)
cg --> Center of Gravity (CG)
cmo --> Chande Momentum Oscillator (CMO)
coppock --> Coppock Curve (COPC)
cti --> Correlation Trend Indicator (CTI)
dmi --> Directional Movement Index(DMI)
er --> Efficiency Ratio (ER)
eri --> Elder Ray Index (ERI)
fisher --> Fisher Transform (FISHT)
inertia --> Inertia (INERTIA)
kdj --> KDJ (KDJ)
kst --> 'Know Sure Thing' (KST)
macd --> Moving Average Convergence Divergence (MACD)
mom --> Momentum (MOM)
pgo --> Pretty Good Oscillator (PGO)
ppo --> Percentage Price Oscillator (PPO)
psl --> Psychological Line (PSL)
pvo --> Percentage Volume Oscillator (PVO)
qqe --> Quantitative Qualitative Estimation (QQE)
roc --> Rate of Change (ROC)
rsi --> Relative Strength Index (RSI)
rsx --> Relative Strength Xtra (rsx)
rvgi --> Relative Vigor Index (RVGI)
slope --> Slope
smi --> SMI Ergodic Indicator (SMI)
sqz* --> Squeeze (SQZ) * NOTE: code sufferred from very strange error, code was commented.
sqz_pro --> Squeeze PRO(SQZPRO)
xfl --> Condition filter
stc --> Schaff Trend Cycle (STC)
stoch --> Stochastic (STOCH)
stochrsi --> Stochastic RSI (STOCH RSI)
trix --> Trix (TRIX)
tsi --> True Strength Index (TSI)
uo --> Ultimate Oscillator (UO)
willr --> William's Percent R (WILLR)
alma --> Arnaud Legoux Moving Average (ALMA)
xll --> Dynamic rolling lowest values
dema --> Double Exponential Moving Average (DEMA)
ema --> Exponential Moving Average (EMA)
fwma --> Fibonacci's Weighted Moving Average (FWMA)
hilo --> Gann HiLo Activator(HiLo)
hma --> Hull Moving Average (HMA)
hwma --> HWMA (Holt-Winter Moving Average)
ichimoku --> Ichimoku Kinkō Hyō (ichimoku)
jma --> Jurik Moving Average Average (JMA)
kama --> Kaufman's Adaptive Moving Average (KAMA)
linreg --> Linear Regression Moving Average (linreg)
mgcd --> McGinley Dynamic Indicator
rma --> wildeR's Moving Average (RMA)
sinwma --> Sine Weighted Moving Average (SWMA)
ssf --> Ehler's Super Smoother Filter (SSF) © 2013
supertrend --> Supertrend (supertrend)
xsa --> X simple moving average
swma --> Symmetric Weighted Moving Average (SWMA)
t3 --> Tim Tillson's T3 Moving Average (T3)
tema --> Triple Exponential Moving Average (TEMA)
trima --> Triangular Moving Average (TRIMA)
vidya --> Variable Index Dynamic Average (VIDYA)
vwap --> Volume Weighted Average Price (VWAP)
vwma --> Volume Weighted Moving Average (VWMA)
wma --> Weighted Moving Average (WMA)
zlma --> Zero Lag Moving Average (ZLMA)
entropy --> Entropy (ENTP)
kurtosis --> Rolling Kurtosis
skew --> Rolling Skew
xev --> Condition all
zscore --> Rolling Z Score
adx --> Average Directional Movement (ADX)
aroon --> Aroon & Aroon Oscillator (AROON)
chop --> Choppiness Index (CHOP)
xex --> Condition any
cksp --> Chande Kroll Stop (CKSP)
dpo --> Detrend Price Oscillator (DPO)
long_run --> Long Run
psar --> Parabolic Stop and Reverse (psar)
short_run --> Short Run
vhf --> Vertical Horizontal Filter (VHF)
vortex --> Vortex
accbands --> Acceleration Bands (ACCBANDS)
atr --> Average True Range (ATR)
bbands --> Bollinger Bands (BBANDS)
donchian --> Donchian Channels (DC)
kc --> Keltner Channels (KC)
massi --> Mass Index (MASSI)
natr --> Normalized Average True Range (NATR)
pdist --> Price Distance (PDIST)
rvi --> Relative Volatility Index (RVI)
thermo --> Elders Thermometer (THERMO)
ui --> Ulcer Index (UI)
ad --> Accumulation/Distribution (AD)
cmf --> Chaikin Money Flow (CMF)
efi --> Elder's Force Index (EFI)
ecm --> Ease of Movement (EOM)
kvo --> Klinger Volume Oscillator (KVO)
mfi --> Money Flow Index (MFI)
nvi --> Negative Volume Index (NVI)
obv --> On Balance Volume (OBV)
pvi --> Positive Volume Index (PVI)
dvdi --> Dual Volume Divergence Index (DVDI)
xhh --> Dynamic rolling highest values
pvt --> Price-Volume Trend (PVT)
Remarks
I also incorporated func descriptions and func test script in commented mode, you can test the functino with the embedded test script and modify them as you wish.
This is a Level 3 free and open source indicator library.
Feedbacks are appreciated.
This is not the end of pandas_ta lib publication, but it is start point with pine v5 lib function and I will add more and more funcs into this lib for my own indicators.
Function Name List:
bton()
wcp()
count()
xbt()
ebsw()
ao()
apo()
xrf()
bias()
bop()
brar()
cci()
cfo()
cg()
cmo()
coppock()
cti()
dmi()
er()
eri()
fisher()
inertia()
kdj()
kst()
macd()
mom()
pgo()
ppo()
psl()
pvo()
qqe()
roc()
rsi()
rsx()
rvgi()
slope()
smi()
sqz_pro()
xfl()
stc()
stoch()
stochrsi()
trix()
tsi()
uo()
willr()
alma()
wcx()
xll()
dema()
ema()
fwma()
hilo()
hma()
hwma()
ichimoku()
jma()
kama()
linreg()
mgcd()
rma()
sinwma()
ssf()
supertrend()
xsa()
swma()
t3()
tema()
trima()
vidya()
vwap()
vwma()
wma()
zlma()
entropy()
kurtosis()
skew()
xev()
zscore()
adx()
aroon()
chop()
xex()
cksp()
dpo()
long_run()
psar()
short_run()
vhf()
vortex()
accbands()
atr()
bbands()
donchian()
kc()
massi()
natr()
pdist()
rvi()
thermo()
ui()
ad()
cmf()
efi()
ecm()
kvo()
mfi()
nvi()
obv()
pvi()
dvdi()
xhh()
pvt()
