MTF Stochastic ScannerThis Stochastic scanner can be use to identify overbought and oversold of 10 symbols over multiple timeframes
it will give you a quick overview which pair is more overbough or more oversold and also signals tops and bottoms in the AVG row
light red/green cell = weak bearish (Stoch = 30-20) / bullish (Stoch = 70-80)
medium red/green cell = bearish (Stoch = 20-10) / bullish (Stoch = 80-90)
dark red/green cell = strong bearish (Stoch <= 10) / bullish (Stoch >= 90)
gray cell = neutral (Stoch = 30-70)
Usage
If AVG (average of all 4 timeframes) falls below 20, the cell will get green, indicating a good time to enter long (buy)
If AVG (average of all 4 timeframes) rises above 80, the cell will get red, indicating a good time to enter short (sell)
Use the "MTF Stochastic Scanner" in combination with the " MTF RSI Scanner "
to find tops (RSI MTF avg >=70 AND Stochastic MTF avg >= 80)
or bottoms (RSI MTF avg <= 30 AND Stochastic MTF avg <= 20)
Here is how the two MTF scanners looked on Nov 08 2021 (ATH) »
and here how the MTF scanners looked on June 21 2022
use TradingViews Replay function to check how it would have worked in the past and when not.
As always… there NOT a single indicator that can show to the top & bottom 100% every single time. So use with caution, with other indicators and/or deeper understanding of technicals analysis ☝️☝️☝️
Settings
You can change the timeframes, symbols, Stochastic settings, overbought/oversold levels and colors to your liking
Drag the table onto the price chart, if you want to use it as an overlay.
NOTE:
Because of the 4x10 security requests, it can take up to 1 minute for changed settings to take effect! Please be patient 🙃
If you have any idea on how to optimise the code, please feel free to share 🙏
*** Inspired by "Binance CHOP Dashboard" from @Cazimiro and "RSI MTF Table" from @mobester16 ***
חפש סקריפטים עבור "2021年黄金价格走势"
MTF RSI ScannerThis RSI scanner can be use to identify the relative strength of 10 symbols over multiple timeframes
it will give you a quick overview which pair is more bearish or more bullish and also signals tops and bottoms in the AVG row
light red/green cell = weak bearish (RSI = 45-35) / bullish (RSI = 55-65)
medium red/green cell = bearish (RSI = 35-25) / bullish (RSI = 65-75)
dark red/green cell = strong bearish (RSI <= 25) / bullish (RSI >= 75)
gray cell = neutral (RSI= 45-55)
Usage
If AVG (average of all 4 timeframes) falls below 30, the cell will get green, indicating a good time to enter long (buy)
If AVG (average of all 4 timeframes) rises above 70, the cell will get red, indicating a good time to enter short (sell)
Use the "MTF RSI Scanner" in combination with the "MTF Stochastic Scanner"
to find tops (RSI MTF avg >=70 AND Stochastic MTF avg >= 80)
or bottoms (RSI MTF avg <= 30 AND Stochastic MTF avg <= 20)
Here is how the two MTF scanners looked on Nov 08 2021 (ATH) »
and here how the MTF scanners looked on June 21 2022
use TradingViews Replay function to check how it would have worked in the past and when not.
As always… there NOT a single indicator that can show to the top & bottom 100% every single time. So use with caution, with other indicators and/or deeper understanding of technicals analysis ☝️☝️☝️
Settings
You can change the timeframes, symbols, RSI settings, overbought/oversold levels and colors to your liking
Drag the table onto the price chart, if you want to use it as an overlay.
NOTE:
Because of the 4x10 security requests, it can take up to 1 minute for changed settings to take effect! Please be patient 🙃
If you have any idea on how to optimise the code, please feel free to share 🙏
*** Inspired by "Binance CHOP Dashboard" from @Cazimiro and "RSI MTF Table" from @mobester16 ***
[GarufiCommunity] Multi Indicator: VWAPs, MA, Pivot PointsThis script provides a collection of indicators to help traders look at multiple trends while maintaining a consistent configuration, even when jumping around different timeframes and symbols.
Additionally, this collection is particularly useful when trading decisions involve looking at dozens of indicators and analyzing, in aggregate, their confluence.
With this collection of indicators you can configure anchored VWAPs, MA, and Pivot Points:
- Anchored VWAPs: For each you define a fixed time and date to anchor it in the graph, and it stays consistent even when you change the symbol. An example use case can be setting one of the VWAPs to always start on the first candle on January 1st 2021, and a second VWAP a decade prior, so you don’t need to keep manually adjusting/adding VWAPs to the graph. At the moment you can define up to 4 anchored VWAPs.
- MA and Pivot Points: For each you can set independent timeframes, periods, and types, while using a single configuration panel. This helps reduce the amount of clicking needed when trying different configurations, such as testing different MA and Pivot periods and comparing how each behave in the graph (this personally helps me build trust in indicators). Permits use of up to 3 MAs and 2 Pivot Points.
Lastly, this script leverages and reuses modified code from the sources below:
- Médias e Tempos-v.2.1 by VeraLucia (with permission);
- Multiple Anchored VWAP v1.0 by GuilhermeNogueira (with permission);
- Pivot Point by TradingView.
TASC 2022.04 S&P500 Hybrid Seasonal System█ OVERVIEW
TASC's April 2022 edition of Traders' Tips includes the "Sell In May? Stock Market Seasonality" article authored by Markos Katsanos. This is the code implementing the "Hybrid Seasonal System" from the article.
█ CONCEPTS
In his article, Markos Katsanos takes an updated look at the "Sell in May" adage by reviewing recent historical data for seasonal equity market tendencies. The author explores the development of a trading strategy (a set of buy and sell rules) based on this research.
He starts from the enhanced buy & hold system featured in his July 2021 TASC article, and adds additional technical conditions. These include volatility conditions ( VIX and ATR ) plus the "Volume Flow Indicator" (VFI), which is a custom money flow indicator that Katsanos introduced in his June 2004 TASC article. He provides an example of a trading system that others can test for themselves and modify as they see fit. The author notes that the system could likely be improved further by adding money management conditions (such as a stop-loss), or by adding more technical conditions not considered in the scope of this article.
█ CALCULATIONS
The entry and exit rules that constitute the trading system are defined below. The critical values of VIX, ATR and VFI (specified below) used in the calculations were determined by optimization for a daily chart of the SPY ETF . By default, the strategy only allows long entries. However, the script offers the possibility to initiate short entries upon exiting long trades through the "Long Only" toggle in the script's inputs.
Long Entry Rules
• Seasonal: The seasonal trade is initiated on the first business day October at the open.
• Volatility: In case of high volatility, that is if the VIX is above 60% or the 15-day ATR was above 90% over the past 25 days, the seasonal trade is deferred until later in the month or year, when the volatility subsides.
Exit/Short Entry Rules
• Seasonal: The exit/short signal is triggered on the first business day of August at the open.
• Volatility: The exit/short signal is triggered if VIX is above 120 % (i.e. 2 times the corresponding threshold parameter).
• Money flow (VFI): The exit/short signal is triggered if the VFI crosses under a critical value (-20) while its 10-day moving average is pointing down.
Join TradingView!
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)
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.
ETH vs BTC 200W SMA OverextensionHistorically, when BTC suffers a correction and ETH continues to rally, this hints at an impending market-wide correction. In Jan 2018, ETH rallies while BTC corrects, signalling the end of the bull cycle. In May 2021, ETH rallies while BTC ranges between $50-$60k, then a major correction occurs. This indicator attempts to monitor this phenomenon in order to help spot potential macro tops in the cryptocurrency market.
The indicator takes the price of the asset and divides it by the 200 week SMA value. This gives an over/undervaluation in percentage terms. When ETH becomes significantly more overvalued relative to BTC, the indicator will warn of a potential top forming (see red shaded areas).
This is for edutainment purposes only. Don't make financial decisions based on this indicator.
[e2] Drawing Library :: Horizontal Ray█ OVERVIEW
Library "e2hray"
A drawing library that contains the hray() function, which draws a horizontal ray/s with an initial point determined by a specified condition. It plots a ray until it reached the price. The function let you control the visibility of historical levels and setup the alerts.
█ HORIZONTAL RAY FUNCTION
hray(condition, level, color, extend, hist_lines, alert_message, alert_delay, style, hist_style, width, hist_width)
Parameters:
condition : Boolean condition that defines the initial point of a ray
level : Ray price level.
color : Ray color.
extend : (optional) Default value true, current ray levels extend to the right, if false - up to the current bar.
hist_lines : (optional) Default value true, shows historical ray levels that were revisited, default is dashed lines. To avoid alert problems set to 'false' before creating alerts.
alert_message : (optional) Default value string(na), if declared, enables alerts that fire when price revisits a line, using the text specified
alert_delay : (optional) Default value int(0), number of bars to validate the level. Alerts won't trigger if the ray is broken during the 'delay'.
style : (optional) Default value 'line.style_solid'. Ray line style.
hist_style : (optional) Default value 'line.style_dashed'. Historical ray line style.
width : (optional) Default value int(1), ray width in pixels.
hist_width : (optional) Default value int(1), historical ray width in pixels.
Returns: void
█ EXAMPLES
• Example 1. Single horizontal ray from the dynamic input.
//@version=5
indicator("hray() example :: Dynamic input ray", overlay = true)
import e2e4mfck/e2hray/1 as e2draw
inputTime = input.time(timestamp("20 Jul 2021 00:00 +0300"), "Date", confirm = true)
inputPrice = input.price(54, 'Price Level', confirm = true)
e2draw.hray(time == inputTime, inputPrice, color.blue, alert_message = 'Ray level re-test!')
var label mark = label.new(inputTime, inputPrice, 'Selected point to start the ray', xloc.bar_time)
• Example 2. Multiple horizontal rays on the moving averages cross.
//@version=5
indicator("hray() example :: MA Cross", overlay = true)
import e2e4mfck/e2hray/1 as e2draw
float sma1 = ta.sma(close, 20)
float sma2 = ta.sma(close, 50)
bullishCross = ta.crossover( sma1, sma2)
bearishCross = ta.crossunder(sma1, sma2)
plot(sma1, 'sma1', color.purple)
plot(sma2, 'sma2', color.blue)
// 1a. We can use 2 function calls to distinguish long and short sides.
e2draw.hray(bullishCross, sma1, color.green, alert_message = 'Bullish Cross Level Broken!', alert_delay = 10)
e2draw.hray(bearishCross, sma2, color.red, alert_message = 'Bearish Cross Level Broken!', alert_delay = 10)
// 1b. Or a single call for both.
// e2draw.hray(bullishCross or bearishCross, sma1, bullishCross ? color.green : color.red)
• Example 3. Horizontal ray at the all time highs with an alert.
//@version=5
indicator("hray() example :: ATH", overlay = true)
import e2e4mfck/e2hray/1 as e2draw
var float ath = 0, ath := math.max(high, ath)
bool newAth = ta.change(ath)
e2draw.hray(nz(newAth ), high , color.orange, alert_message = 'All Time Highs Tested!', alert_delay = 10)
Daily Sun Flares Class XThe classification system for solar flares uses the letters A, B, C, M or X, according to the peak flux as measured at the Earth by the GOES spacecraft.
These are daily Class X sun flares. The data was created by counting daily flares of this class based on the peak time of the flare.
2015-01-01 until 2021-08-25
Percentage Levels by TimeframePlots the positive and negative percentage levels from a selection of timeframes and sources for any ticker. You can use this within a pullback trading system. For example, if you historically look at the average pullback of large cap stocks and ETF's, you can use this indicator to plot the levels it could pullback to for an entry to go long. It can be used as potential targets when trading a ticker short. Another use for this is to backtest the set percentage targets using TradingView's bar replay feature to see how ETF's and large cap stocks have reacted at these levels. Note: This is intended to be used at timeframes equal to higher than the chart's as it may cause re-painting issues.
Currently percentage levels are statically set to 1, 3, 5, 10, 15, 20, 25, and 30% levels above and below the chosen source (open, high, low, close). You can also display the data based on timeframes from Daily (1D) all the way up to Yearly (12M)
*Not financial advice but in my opinion the current percentage levels set (see above) are best used for ETF's and Large Cap Stocks.
Jan 2
Release Notes: Added the ability to select the historical bars to look back when plotting levels
Jan 2
Release Notes: To get a better display or proper resolution on your charts, change the view settings to "Scale Price Chart Only"
Jan 2
Release Notes: To add % labels for this indicator on the price axis, change your chart settings to include "Indicator Name Label" & "Indicator Last Value". You can find this under the Label section after hitting the gear icon in the bottom right of your chart.
Jan 2
Release Notes: Added: Custom Line Plot Extension Settings. Ideally both values should be equal to display optimal extended lines. To return to a base setting: '1' = Historical Lookback & '0' = Offset Lines. Also note this is dependent on the timeframe you are viewing on the chart.
Jan 2
Release Notes: Removed indicator from example chart that was not needed.
Jan 2
Release Notes: Updated some comments in the Pine Script
Jan 2
Release Notes: Update: Added commentary and instructions in the indicator settings to address recommended line plot settings for Stocks/ETF's vs Futures
Jan 2
Release Notes: Changed title from "Calculation Method" to "Calculation Source"
Jan 4 2021
Normal use of security() dictates that it only be used at timeframes equal to or higher than the chart's as it may cause re-painting
5212 EMA Strategyver 01
23 December 2021
This strategy using :
- 3 EMA period 50, 100, 200
- stochastic RSI slow
Long Cond :
- Stochastic RSI cross below 20
- EMA 50 > 100 > 200
Short Cond :
- Stochastic RSI cross above 80
- EMA 50 < 100 < 200
Sleeping Mode
- EMA 50 between EMA 100 & EMA 200
F&G_IndexIntroduction.
This indicator shows the behavior of Fear and Greed Index (F&G_Index) for the cryptocurrency market in an intuitive way for traders. This indicator has been modified from a script developed by @cptpat called "Fear and Greed Index FGI (Daily Update) alternative.me" (Tradingview user). The Fear and Greed Index values are taken directly from alternative.me.
The novelty of this proposal is to indicate the extreme levels (lower/upper) of the Fear and Greed Index according to a statistical analysis of the historical data. Also its daily update. It is not recommended to use in isolation. The appropriate way is in consensus with other indicators.
The extreme values.
Two upper and lower limits are established that correspond to the first standard deviation (1·SD) and 1.5 standard deviation (1.5·SD), respectively. These limits will help to know different important levels of greed or fear in the market based on real and historical data. The values obtained for each case are shown below, which will mark the extremes. These values may be modified in the future. If so, they will be updated and the community will be informed.
1·SD higher = 69 (F&G_Index).
1·SD lower = 24 (F&G_Index).
1.5·SD higher = 81 (F&G_Index).
1.5·SD lower = 12 (F&G_Index).
These limits are statistically significant and representative of extreme values of the Fear and Greed Index. Above all, for the case of 1.5·SD higher/lower, whose occurrence of the cases are significantly lower. These data are obtained for a daily record from August 2017 to December 2021, for a total of 1407 data. The occurrence of the Fear and Greed Index value exceeding the indicated levels is shown below.
F&G_Index > 1·SD higher (Greed). Occurrence <22,5%
F&G_Index < 1·SD lower (Fear). Occurrence <19%
1·SD lower < F&G_Index < 1·SD higher (Neutral). Occurrence ≈59%
F&G_Index > 1.5·SD higher (Extreme Greed). Occurrence <8%
F&G_Index < 1.5·SD lower (Extreme Fear). Occurrence <3%
How to use the indicator.
Its use is very simple and intuitive and is based on the levels indicated above. The blue line shows the historical value of F&G_Index. When the value of F&G_Index exceeds the levels indicated above, a vertical band of color will be tinted (brown/red, green/lime green or gray with transparency) as indicated below. This allows you to locate important areas in a very visual way.
F&G_Index > 1·SD higher (Greed). Brown color
F&G_Index < 1·SD lower (Fear). Green color.
1·SD lower < F&G_Index < 1·SD higher (Neutral). Gray color with transparency.
F&G_Index > 1.5·SD higher (Extreme Greed). Red color.
F&G_Index < 1.5·SD lower (Extreme Fear). Lime green color.
Image of the indicator.
60-Day Accumulated Increasing RateIs this Bitcoin bull run still driven by new investors and new funds? Definitely. That’s why the 60-day accumulative increasing rate is so important and it can even determine everything. The only thing that can be trusted is the math. In history, each capital inflow uptrend bull run has ended once the 60-day accumulative increasing rate reached a high level and when the short-term euphoric investors push BTC price to rise at a fast speed and use up all kinds of leverages. At that point, there’s no time for new investors and new funds to flow in, thus the cryptocurrency market will crash from the global top.
In that sense, the crashes on 4th September, 2017 and 19th May, 2021 didn’t end the bull run, instead,they lengthened the bull run span.The last bull run cycle (2017) might have ended prematurely when BTC reached $10,000, recording 150% accumulated increase over 60 days. Then BTC won’t be pumped up to $20,000 if the course wasn’t interrupted by September 4th, 2017 incident.
Technical analysts(they are far from trustworthy, full of bollocks) call the correction of BTC: “consolidation or wipeout”, just like that diabetes is called as Liver Qi Stagnation, weight lossing, being thirsty and other symptoms. It’s quite fun to watch so many people explaining it in a false concept. Everyone knows what the maths is. That’s enough.
PS: This indicator can only be applied to Bitcoin daily chart!
CRC.lib Log FunctionsLibrary "CRCLog"
default_params() Returns default high/low intercept/slope parameter values for Bitcoin that can be adjusted and used to calculate new Regression Log lines
log_regression() Returns set of (fib) spaced lines representing log regression (default values attempt fitted to INDEX:BTCUSD genesis-2021)
A simple trading strategy for XTZ/EUR (December 2021)This is my current trading strategy for XTZ/EUR for this month of December.
It tries to avoid pumps/dumps (i.e. does not trade on big candles).
It always performs one order in each candle for the trading window of the rebalance bear/bull market indicator (check my profile for it).
It has alerts configured so that you can use it in your server/broker (just pass along the `{{strategy.order.alert_message}}` in the alert message, it will include a positive number of XTZ when to buy, or a negative number when to sell).
It does not repaint.
The amount of crypto and fiat in the portfolio can be configured in the cog.
It does not outperform buy/hold for the bull months.
Check the results in the Data Window of Trading View (please avoid the Strategy Tester, it has too many bugs and is not intended for out of the box strategies such a this one).
All code is open source.
Half-Pi Cycle CKB top indicator (insanely experimental)This is an insanely experimental script. It's a modified version of the Bitcoin pi-cycle top indicator.
It changes the Bitcoin pi-cycle top formula by halving the number of days in the two DMAs used in calculation, from 350/111 to 175/56. So I call it the half-pi cycle. It correctly picked the top of CKB (Nervos Network) vs USDT on Huobi in spring 2021 within three days.
It probably is a coincidence, and could very easily not pick the next cycle peak correctly at all. Using such a short number of days makes it a little dubious, but I had no choice since there's only so much price history for this coin. I strongly advise you to not make any trades based on this script ! I cannot be held accountable if you lose money due to this script. It hasn't been shown to be accurate multiple times like the Bitcoin pi-cycle top indicator. I simply find this interesting and want to see if it works next time.
Ehlers Directional Movement Hann Window Indicator [CC]The Directional Movement Hann Window Indicator was created by John Ehlers (Stocks and Commodities Dec 2021 pgs 17-18) and this is his updated version of the classic Directional Movement indicator created by J. Welles Wilder. Ehlers uses the Hann Window Filtering after using an exponential moving average to smooth the classic directional movement indicator. This helps significantly with the lag and lack of smoothing which are both issues with the classic indicator. I have included strong buy and sell signals in addition to the 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 you would like to see me publish!
Daily Sun Flares Class AThe classification system for solar flares uses the letters A, B, C, M or X, according to the peak flux as measured at the Earth by the GOES spacecraft.
These are daily Class A sun flares. The data was created by counting daily flares of this class based on the peak time of the flare.
2015-01-01 until 2021-08-25
Daily Sun Flares Class BThe classification system for solar flares uses the letters A, B, C, M or X, according to the peak flux as measured at the Earth by the GOES spacecraft.
These are daily Class B sun flares. The data was created by counting daily flares of this class based on the peak time of the flare.
2015-01-01 until 2021-08-25
Bitcoin Bull Runs Mid Cycle Aligned This script plots 2 lines which are the 2013 and 2016 bull run. The plots are aligned on their mid cycles to the 2021 mid cycle.
Settings:
You can move the plots on the x and y axis in the settings for the Daily, Weekly and Monthly TFs.
The plot is weird on the Monthly TF, best to use the Daily and Weekly.
If it doesn't load at first you have to zoom out fully and go back to 2013 for it to load. Then it will load.
Ripple (XRP) Model PriceAn article titled Bitcoin Stock-to-Flow Model was published in March 2019 by "PlanB" with mathematical model used to calculate Bitcoin model price during the time. We know that Ripple has a strong correlation with Bitcoin. But does this correlation have a definite rule?
In this study, we examine the relationship between bitcoin's stock-to-flow ratio and the ripple(XRP) price.
The Halving and the stock-to-flow ratio
Stock-to-flow is defined as a relationship between production and current stock that is out there.
SF = stock / flow
The term "halving" as it relates to Bitcoin has to do with how many Bitcoin tokens are found in a newly created block. Back in 2009, when Bitcoin launched, each block contained 50 BTC, but this amount was set to be reduced by 50% every 210,000 blocks (about 4 years). Today, there have been three halving events, and a block now only contains 6.25 BTC. When the next halving occurs, a block will only contain 3.125 BTC. Halving events will continue until the reward for minors reaches 0 BTC.
With each halving, the stock-to-flow ratio increased and Bitcoin experienced a huge bull market that absolutely crushed its previous all-time high. But what exactly does this affect the price of Ripple?
Price Model
I have used Bitcoin's stock-to-flow ratio and Ripple's price data from April 1, 2014 to November 3, 2021 (Daily Close-Price) as the statistical population.
Then I used linear regression to determine the relationship between the natural logarithm of the Ripple price and the natural logarithm of the Bitcoin's stock-to-flow (BSF).
You can see the results in the image below:
Basic Equation : ln(Model Price) = 3.2977 * ln(BSF) - 12.13
The high R-Squared value (R2 = 0.83) indicates a large positive linear association.
Then I "winsorized" the statistical data to limit extreme values to reduce the effect of possibly spurious outliers (This process affected less than 4.5% of the total price data).
ln(Model Price) = 3.3297 * ln(BSF) - 12.214
If we raise the both sides of the equation to the power of e, we will have:
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Final Equation:
■ Model Price = Exp(- 12.214) * BSF ^ 3.3297
Where BSF is Bitcoin's stock-to-flow
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If we put current Bitcoin's stock-to-flow value (54.2) into this equation we get value of 2.95USD. This is the price which is indicated by the model.
There is a power law relationship between the market price and Bitcoin's stock-to-flow (BSF). Power laws are interesting because they reveal an underlying regularity in the properties of seemingly random complex systems.
I plotted XRP model price (black) over time on the chart.
Estimating the range of price movements
I also used several bands to estimate the range of price movements and used the residual standard deviation to determine the equation for those bands.
Residual STDEV = 0.82188
ln(First-Upper-Band) = 3.3297 * ln(BSF) - 12.214 + Residual STDEV =>
ln(First-Upper-Band) = 3.3297 * ln(BSF) – 11.392 =>
■ First-Upper-Band = Exp(-11.392) * BSF ^ 3.3297
In the same way:
■ First-Lower-Band = Exp(-13.036) * BSF ^ 3.3297
I also used twice the residual standard deviation to define two extra bands:
■ Second-Upper-Band = Exp(-10.570) * BSF ^ 3.3297
■ Second-Lower-Band = Exp(-13.858) * BSF ^ 3.3297
These bands can be used to determine overbought and oversold levels.
Estimating of the future price movements
Because we know that every four years the stock-to-flow ratio, or current circulation relative to new supply, doubles, this metric can be plotted into the future.
At the time of the next halving event, Bitcoins will be produced at a rate of 450 BTC / day. There will be around 19,900,000 coins in circulation by August 2025
It is estimated that during first year of Bitcoin (2009) Satoshi Nakamoto (Bitcoin creator) mined around 1 million Bitcoins and did not move them until today. It can be debated if those coins might be lost or Satoshi is just waiting still to sell them but the fact is that they are not moving at all ever since. We simply decrease stock amount for 1 million BTC so stock to flow value would be:
BSF = (19,900,000 – 1.000.000) / (450 * 365) =115.07
Thus, Bitcoin's stock-to-flow will increase to around 115 until AUG 2025. If we put this number in the equation:
Model Price = Exp(- 12.214) * 114 ^ 3.3297 = 36.06$
Ripple has a fixed supply rate. In AUG 2025, the total number of coins in circulation will be about 56,000,000,000. According to the equation, Ripple's market cap will reach $2 trillion.
Note that these studies have been conducted only to better understand price movements and are not a financial advice.
Gherkinit Futures Cycle█ OVERVIEW
Presented here is code for the " NYSE:GME Futures cycle theory" originally conceived by Gherkinit (Pi-Fi) and his quantitative analysts which is still under peer review.
This theory was built upon the knowledge that many intelligent investors on Reddit accrued over the past year in regards to the Mother Of All Short Squeezes this stock has to offer.
Up until now, what happened in January 2021 was considered an anomaly brought on by FOMO and retail interest but it's starting to look like unfair market makers and similar went to cover and ran head on into retail FOMO which is why they cut off the buying at that time. In order to understand what happened and what's to come, visualizing the theory with ease is essential.
█ WHAT THE SETTINGS MEAN
- Enable Draw | Visual Clean up
(True/False) Quarterly dates : Enables or disables the quarterly dates that repeat every "cycle".
(True/False) Roll dates : Enables or disables the roll dates that repeat every "cycle".
(True/False) Expiration dates : Enables or disables the expiration dates that repeat every "cycle".
(True/False) Run dates : Enables or disables the run dates that repeat every "cycle".
- Date Colors | Making things look good
(Color) Quarterly : Color for the respective date.
(Color) Roll : Color for the respective date.
(Color) Expiration : Color for the respective date.
(Color) Run : Color for the respective date.
- Extended Cycle | Look into the future
(Integer) Extended line height multiplier : A multiplier value for the height of the lines representing the selected "future" cycle.
(Dollar Amount) Extended line height : The height value in dollars of the lines representing the selected "future" cycle.
(Integer) Extended line width : The width of the lines representing the selected "future" cycle.
(Integer) Extended cycle ID : The cycle you want to see "ahead" or in the "future". For example if you set the value to "0" you'll only see cycles from the past up until the present (already occurred). If you set the value to "1" you will see the estimated dates for the specific cycle in the future i.e. 1 cycle ahead of the last completed/visible cycle on the chart.
█ EXTRA INFO
This indicator was simply made by a bored CS student who didn't want to endlessly mark dates on a graph after learning more about the theory.
Hope this help whoever uses this. To the moon fellow apes!
- Winter ;)
P.s. Pickle 4 Life
Triple Colored Least Squares Moving Average + Crossover AlertsThis script is forked from the ‘ Double Colored Least Squares Moving Average + Crossover Alerts ‘ from @IronKnightmare.
First release & notes : 2021-11-03.
Overview:
The Least Squares Moving Average is used mainly as a crossover signal to identify bullish or bearish trends. When a shorter duration line cross a longer one a trend can be identified. When multiple lines or the price action cross a longterm trend the confirmation can be further validated. Tradingview contains already some indicators with 1 or two LSMA trendlines that can be configured and toggled.
The original script that I forked had two LSMA lines that could be plotted with other valuable functions, I added a third for further confirmation as some trading systems will use three lines or some combination of those for validation.
Usage:
In inputs
- You will see LSMA 1, LSMA 2 & LSMA 3. The default values are 40, 100 & 400 representing the number of periods plotted by that line : fast, medium and slow changing trendlines will be plotted. The offset value and source are standard for most scripts.
In Style
- You can toggle LSMA 1, 2 or 3 and any combination of those. There are much more possibilities this way.
- For each LSMA, Color 0 & Color 1 are for coloring the slope of the trendline,
- Color 0 for rising slope,
- Color 1 for descending slope.
- The script will automatically color the rise or fall of the trendline accordingly. You can also set one identical color in both slopes for one unique color.
- The ‘ Long Crossover 1 on 2 ’ is a signal for when the LSMA 1 cross over the LSMA 2, usually a shorter periods trendline, more volatile, climbing over the medium term one. A Signal will be traced on the chart at that crossing, you can configure this. The ‘Short Crossover 1 on 2’ is when the LSMA 1 cross under the LSMA 2, a signal will be traced on the chart accordingly.
- The Long Crossover 1 on 3 & Short Crossover 1 on 3 act on the same principle, although the crossing of the fast LSMA on the long / slow LSMA are used. Both can be toggled.
- The ‘ Background Coloring Line 1 : 0-Neutral, 1-Up, 2-Down ’ is an optional background coloring for the LSMA1 line. This can provide additional information at a quick glance, especially if you combine the two other lines backgrounds, the partial transparency will compound.
