SPX Excess CAPE YieldHere we are looking at the Excess CAPE yield for the SPX500 over the last 100+ years
"A higher CAPE meant a lower subsequent 10-year return, and vice versa. The R-squared was a phenomenally high 0.9 — the CAPE on its own was enough to explain 90% of stocks’ subsequent performance over a decade. The standard deviation was 1.37% — in other words, two-thirds of the time the prediction was within 1.37 percentage points of the eventual outcome: this over a quarter-century that included an equity bubble, a credit bubble, two epic bear markets, and a decade-long bull market."
assets.bwbx.io
In December of 2020 Dr. Robert Shiller the Yale Nobel Laurate suggested that an improvement on CAPE could be made by taking its inverse (the CAPE earnings yield) and subtracting the us10 year treasury yield.
"His model plainly suggests that stocks will do badly over the next 10 years, and that bonds will do even worse. This was the way Shiller put it in a research piece for Barclays Plc in October, (which can be found on SSRN Below):
In summary, investors expect a certain return in equities as compensation for investing in a riskier asset class, and as interest rates have declined, the relative expected return for equities has increased dramatically. We believe this may quantitatively help to explain investors current preference for equities over bonds, and as such the quick recoveries we are observing (with the exception of the UK), whilst still in the midst of a pandemic. In the US in particular, we are once again observing stretched valuations and high CAPE ratios compared to history."
Sources:
papers.ssrn.com
www.bloomberg.com
The standard trading view disclaimer applies to this post -- please consult your own investment advisor before making investment decisions. This post is for observation only and has no warranty etc. www.tradingview.com
Best,
JM
Search in scripts for "A股半导体公司+并购欧洲光学企业+2020年股价大涨+传感器"
TSI HMA CCIHi!
This strategy has TSI and CCI indicators with the CCI being based on a HMA instead of the Price.
There is a number of conditions that must combine to create buy or sell signals, but it is basically a couple of MA crossovers.
The strategy opens new orders on each candle if the conditions are met, Either direction, so it is hedging.
It wont open new orders if there is a floating loss, and so is constantly attempting to hold a floating profit (drawup instead of drawdown)
But It has a StopLoss (set by user) for closing of losing orders, and it closes all orders in basket style when account is in profit to users set amount target profit.
Low commission set to simulate swap but Forex pairs generally dont have commission like the crypto exchanges do. So if you use this on cryptos, remember to increase the commission to your brokers amount.
Crypto users will likely find that because this opens so many orders the commission could erase its profits.
So i recommend this for Forex only, and perhaps, only NZDUSD 4H chart. other pairs, change settings for.
The strategy has settings for testing on target time spans, so you could test it on just Jan-Feb 2020 for example, if you want, or from Jan 2020 to present day.
Have Fun! Open Script for copy/paste/edit/publish your own version :)
HTF High/Low Repaint StrategyHere is an another attempt to demonstrate repainting and how to avoid them. It happened few times to me that I develop a strategy which is giving immense returns - only to realize after few forward testing that it is repainting. Sometimes, it is well disguised even during forward testing.
In this simple strategy, conditions are as below:
Buy : When a 3M bar produces high and low higher than it's previous 3M bar high, low
Sell : When a 3M bar produces high and low lower than its previous 3M bar high, low.
Default setting is : lookahead = on and offset = 0
This means current 3M bar high low is plotted for all the daily bars within this month. Which means, strategy looks ahead of time to see this 3M bar high is higher than previous 3M bar high during the start of the first daily bar. Hence, this combination leads to massive repaint.
For example, trade made on October 2nd 2018 already knows well ahead of time that price is going to go down in next 3 months:
Similarly, after 2 years on October 2nd 2020 - the strategy already knows that last 3M high is going to be breached on 7th December 2020
Solution: If you are using security for higher timeframes, safer option is always to use offset 1. Further details in the trading view script:
BUT
It may still repaint if we are passing function to security.
For example:
f_secureSecurity(_symbol, _res, _src) => security(_symbol, _res, _src , lookahead = barmerge.lookahead
This function will likely avoid any repainting with Higher timeframe if we are passing in built variables such as high, low, close, open etc. But, if we try to pass supertrend, this will not produce right results. This is because supertrend calculation in turn uses high/low/close values which do not consider the offset while calculating. Hence, even with offset 1, this will still produce issues.
Hence, the call:
= f_secureSecurity(syminfo.tickerid, derivedResolution, supertrend(3,10), offset) will again lead to massive repainting. Solution to this is to implement supertrend function and use high, low, close values derived from secureSecurity.
Quick tips to identify or be suspicious about repainting
Unbelievable results on all timeframes and all instruments with both long and short trades
Lower timeframes giving significantly higher returns on backtest when compared to higher timeframe
If these things happen, be wary about repainting and do a through check of all security function usage in your strategy.
All the best :)
PS: Apply 3-5 days resolution and see the fun. Also, WHC is one hell of a Christmas tree. Could have made immense profit in the same strategy even without repainting.
Bitlinc MARSI Study AST w/ Take Profit & Stop loss - beta 0.1This script is beta 0.1 - will update as soon as the script is tradable
This script is based on AST on a 10 minute timeframe. You can change the asset and the timeframe for any asset you want to trade, but for it to work correct ALL settings have to be testes in the Strategy section of the TradingView. Each assets and timeframe require a different mixture of settings. This is NOT a one settings fits all trading for all assets on any timeframe. Below are the settings and explanation on how it works.
How it fires a buy / sell:
The script will plot an RSI with upper and lower bands in a separate indicator window. The idea behind this script is to fire a LONG when MA crosses OVER lower band and fire a SHORT when the MA crosses under the lower band. Each order that fires is an OCO (Order Cancels Order) for pyramiding.
Settings:
You have full control of these settings as mentioned above, you must configure every part of this script for each asset and timeframe you trade.
- Length of MA
- Length
- Upper bands of RSI
- Lower bands of RSI
- Take profit percentage
- Stop loss percentage
- Month to start and end the strategy (within 2020)
- Day to start and end the strategy (within 2020)
- Quantity type
- Slippage
- Pyramiding
***Remember that after the signal to enter or exit a trade is fired, the alert will trigger AFTER the close of the candle that caused the tigger to fire
Haemil-ri Moving Average Line/Created by user dunsan2000 updated 2020/8/20
//It is a moving average that is easy to use in Haemil-ri.
//둔산2000 만듬, 2020/8/20 수정됨
//해밀리에서 사용하기에 편한 이동평균선 입니다.
LB Squeeze Momentum DivergencesThis study tries to highlight LazyBear Squeeze Momentum divergences
as they are defined by
TradingLatino TradingView user
Squeeze momentum green peaks are connected by a line
Associated prices to these green peaks are also connected
If both lines have a different slope orientation
then there is a divergence.
It only shows two last divergence lines and angles.
The original chart screenshot shows some divergence lines
on the top or main chart
these were drawn manually
because you cannot write to two different charts
from the same pine script study (Well, not in August 2020 anyways)
It's aimed at BTCUSDT pair and 4h timeframe.
HOW IT WORKS
Simple geometric mathematics are used
to calculate the two lines degrees
Then both degrees are compared
to show if both lines agree ( // or \\ )
or if they disagree ( /\ or \/ )
SETTINGS
(SQZDiver) Show degrees : Show degrees of each Squeeze Momentum Divergence
lines to the x-axis.
(SQZDiver) Show desviation labels : Whether to show
or not desviation labels for the Squeeze Momentum Divergences.
(SQZDiver) Show desviation lines : Whether to show
or not desviation lines for the Squeeze Momentum Divergences.
(ADX) Smoothing
(ADX) DI Length
(ADX) key level
(ADX) Print : Whether to show
or not scaled ADX line
(SQZMOM) BB Length
(SQZMOM) BB MultFactor
(SQZMOM) KC Length
(SQZMOM) KC MultFactor
(SQZMOM) Use TrueRange (KC)
(SQZMOM) Print : Whether to show
or not Squeeze Momentum indicator.
WARNING
Some securities and timeframes might output degrees
too next to zero.
The code might need to be tweaked to meet your needs.
USAGE
One strategy is to sell when you are in a long entry
when you find out that the price slope is upwards ( / )
while the lb smilb slope is downwards: ( \ )
E.g. You will see:
/
\
on the indicator.
Why?
Because it might signal you that the price is
going to correct downwards soon.
FEEDBACK 1
Please let me know if there is any
other strategy based on the red side of
LB Squeeze Momentum
so that I might add support for it in the future.
FEEDBACK 2
Calculating degrees in a chart
with a different x-axis scale
is a nightmare
that's why I did not a range settings
so that values next to zero are
converted into zero
and thus showing an horizontal line.
Feedback is welcome on this matter.
EXTRA 1
If you turn off showing the divergence lines
and if you turn off showing the divergence labels
you almost get what TradingLatino user uses
as its default momentum indicator.
EXTRA 2
Optionally this indicator can show you
a rescaled ADX (it only works properly on 2020 Bitcoin charts)
ABOUT COLOURS
TradingLatino user has both dark green and light green
inverted compared to this LB SQZMOM chart.
CREDITS
I have reused and adapted some code from
'Squeeze Momentum Indicator' study
which it's from TradingView LazyBear user.
I have reused and adapted some code from
'Directional Movement Index + ADX & Keylevel Support' study
which it's from TradingView console user.
Weis Pip Wave jayyWhat you see here is the Weis pip wave. The Weis pip wave shows how far in price a Weis wave has traveled through the duration of a Weis wave. The Weis pip wave is used in combination with the Weis cumulative volume wave. The two waves must be set to the same "wave size" and using the same method as described by Weis.
Using the traditional Weis method simply enter the desired wave size in the box "Select Weis Wave Size". In the example shown, it is set to 5 points. Each wave for each security and each timeframe requires its own wave size. Although not the traditional method a more automatic way to set wave size would be to use ATR. This is not the true Weis method but it does give you similar waves and, importantly, without the hassle of selecting a wave size for every chart. Once the Weis wave size is set then the pip wave will be shown.
I have put a zigzag of a 5 point Weis wave on the above bar chart. I have added it to allow your eye to get a better appreciation for Weis wave pivot points. You will notice that the wave is not in straight lines connecting wave tops to bottoms this is a function of the limitations of Pinescript version 1. This script would need to be in version 4 to allow straight lines. I will elaborate on the Weis pip zigzag script.
What is a Weis wave? David Weis has been recognized as a Wyckoff method analyst he has written two books one of which, Trades About to Happen, describes the evolution of the now popular Weis wave. The method employed by Weis is to identify waves of price action and to compare the strength of the waves on characteristics of wave strength. Chief among the characteristics of strength is the cumulative volume of the wave. There are other markers that Weis uses as well for example how the actual price difference between the start of the Weis wave from start to finish. Weis also uses time, particularly when using a Renko chart. Weis specifically uses candle/bar closes to define all wave action.
David Weis did a futures.io video which is a popular source of information about his method.
Cheers jayy
PS This script was published a day ago, however, I had included some links to the website of a person that uses Weis pip waves and also a dropbox link that contains the Weis wave chart for May 27, 2020, published by David Weis. Providing those links is against TV policy and so the script was hidden by TV. This is the identical script with the identical settings but without the offending links. If you want to see the pip Weis method in practice then search Weis pip wave. I have absolutely no affiliation. If you want to see Weis chart in pdf then message me and I will give a link or the Weis pdf. Why would you want to see the Weis chart for May 27, 2020? Merely to confirm the veracity of my algorithm. You could compare my chart () from the same period to the Weis chart. Both waves are for the ES!1 4 hour chart and both for a wave size of 5.
ApopheniaPays Crossing detector & 2-field date/time entryYou specify a horizontal line by value, start date/time, and end date/time, and choose a data source (bar close is the default) and it will label count how many times that source crosses that line between those dates/times.
Enter the start and end dates for your horizontal line as MMDDYY and HHMM (24 hour time).
: Jan 17, 2020 would be 11720 (properly it would be 011720, but Pine inputs delete leading 0s).
: November 17, 2020 would be 110720.
: 8:30 AM would be 0830.
: 8:30 PM would be 2030.
Remember to enter the right time zone.
I believe nobody else has published a 2-input date/time picker on TV, at least the last time I checked they hadn't, they all make you input M,D,Y,H,M as separate fields. Ugh!
If you use any parts of this code, please credit me. If somehow you happen to make a lot of money using this code, please think about what a fair share would be to pay me for my help, then give that amount to a worthwhile charity.
Stochastic Pop and Drop by Jake Bernstein v1 [Bitduke]I found a simple strategy by Jake Bernstein, modified it a little and created a strategy with Risk Management System (SL+TP); After that I test it on the different cryptocurrency pairs.
About the Indicator
Basically it's the strategy of 2 indicators: Stochastic Oscillator to define the bias and Average Directional Index to confirm it.
One again, It uses Stochastic Oscillator to define the trading bias. In particular, the trading bias was deemed bullish when the weekly 14-period Stochastic Oscillator was above some default value (in him paper - 50) and rising and vice versa.
Once the trading bias is established, Steckler used the Average Directional Index (ADX) to define a slowdown in the trend. ADX measures the strength of the trend and a move below 20 signals a weak trend.
Modifications
I didn't implement Average Directional Index (ADX) and test just different sources for data, oscillator periods and different levels in relation to the crypto market.
So, it shows good results with two tight thresholds at 55 and 45 level.
The bar chart below the defining the bullish and bearish periods (green and red) and gives a signal to enter the trade (purple bars).
Backtesting
Backtested on XBTUSD , BTCPERP (FTX) pairs. You may notice it shows good results on 3h timeframe.
Relatively low drawdown
~ 10% (from 2019 to date) FTX
~ 22% (4 years from 2016) Bitmex
I backtested on the different altcoin pairs as well, but the results were just not good.
Relatively good results were shown by some index pairs from the FTX exchange ( FTX:SHITPERP ), but I think there is a few data for backtesting to be asure in them.
Bitmex 3h (2017 - 2020) :
i.imgur.com
FTX 3h (2019 - 2020):
i.imgur.com
Possible Improvements
- Regarding trading algorithm it would be good to check with strategy with ADX somehow. Maybe for the better entries
- As for Risk Management system, it can be improved by adding trailing stop to the strategy.
Link: school.stockcharts.com
Double MA CCI"What is the Commodity Channel Index (CCI)?
Developed by Donald Lambert, the Commodity Channel Index (CCI) is a momentum-based oscillator used to help determine when an investment vehicle is reaching a condition of being overbought or oversold. It is also used to assess price trend direction and strength. This information allows traders to determine if they want to enter or exit a trade, refrain from taking a trade, or add to an existing position. In this way, the indicator can be used to provide trade signals when it acts in a certain way.
KEY TAKEAWAYS
• The CCI measures the difference between the current price and the historical average price.
• When the CCI is above zero it indicates the price is above the historic average. When CCI is below zero, the price is below the hsitoric average.
• High readings of 100 or above, for example, indicate the price is well above the historic average and the trend has been strong to the upside.
• Low readings below -100, for example, indicate the price is well below the historic average and the trend has been strong to the downside.
• Going from negative or near-zero readings to +100 can be used as a signal to watch for an emerging uptrend.
• Going from positive or near-zero readings to -100 may indicate an emerging downtrend.
• CCI is an unbounded indicator meaning it can go higher or lower indefinitely. For this reason, overbought and oversold levels are typically determined for each individual asset by looking at historical extreme CCI levels where the price reversed from." ----> 1
SOURCE
1: (SINCE IM NOT A "PRO" MEMBER I C'ANT POST THE SOUCRE URL..., webpage consulted at : 8:50 GMT -5 ; the 2020-01-18)
I- Added a 2nd MA length and changed the default values of the source type and switched the SMA to a MA.
II- In process to add analytic MACD histogram correlation and if possible, ploting a relative histogram between the CCI upper and lower band.
P.S.:
Don't set your moving averages lengths to far from each other... This could result in fewer convergence and divergence, also in fewer crossing MA's.
Have a good year 2020 !!
//----CODER----//
R.V.
Reflex Oscillator - Dr. John EhlersHot off the press, I present this NEW "Reflex Oscillator" employing PSv4.0, originally formulated by Dr. John Ehlers for TASC - February 2020 Traders Tips. John Ehlers might describe it's novel characteristics as being a reversal sensitive near zero-lag averaging indicator retaining the CYCLE component. Also, I would add that irregardless of the sampling interval, this indicator has a bound range between +/-2.0 on "1 second" candles all the way up to "1 month" candle durations. This indicator also has a companion indicator entitled "TrendFlex Oscillator". I have published it in tandem with this one in my scripts profile.
One notable difference between this and the original formulation is that I have added an independent control for the Super Smoother. This "tweak" is enabled by applying the override and adjusting it's period. There is a "Post Smooth" input() that "tweaks" the internal Reflex EMA too. Keep in mind that my intention of adding tweaks is solely for experimentation with the original formulation.
I also added adjustable levels for those of you that may wish to employ alertcondition()s to this indicator somehow. Providing a more utilitarian approach, I created this with an easy to use reusable function named reflex(). As always, I have included advanced Pine programming techniques that conform to proper "Pine Etiquette". Being this is one of John Ehlers' first two simultaneously released indicators for 2020, I felt a few more bells and whistles were appropriate as a proper contribution to the Tradingview community.
Features List Includes:
Dark Background - Easily disabled in indicator Settings->Style for "Light" charts or with Pine commenting
AND much, much more... You have the source!
The comments section below is solely just for commenting and other remarks, ideas, compliments, etc... regarding only this indicator, not others. When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members, I may implement more ideas when they present themselves as worthy additions. As always, "Like" it if you simply just like it with a proper thumbs up, and also return to my scripts list occasionally for additional postings. Have a profitable future everyone!
TrendFlex Oscillator - Dr. John EhlersHot off the press, I present this NEW "TrendFlex Oscillator" employing PSv4.0, originally formulated by Dr. John Ehlers for TASC - February 2020 Traders Tips. John Ehlers might describe it's novel characteristics as being a reversal sensitive near zero-lag averaging indicator retaining the TREND component. Also, I would add that irregardless of the sampling interval, this indicator has a bound range between +/-2.0 on "1 second" candles all the way up to "1 month" candle durations. This indicator also has a companion indicator entitled "Reflex Oscillator". I have published it in tandem with this one in my scripts profile.
One notable difference between this and the original formulation is that I have added an independent control for the Super Smoother. This "tweak" is enabled by applying the override and adjusting it's period. There is a "Post Smooth" input() that "tweaks" the internal TrendFlex EMA too. Keep in mind that my intention of adding tweaks is solely for experimentation with the original formulation.
I also added adjustable levels for those of you that may wish to employ alertcondition()s to this indicator somehow. Providing a more utilitarian approach, I created this with an easy to use reusable function named trendflex(). As always, I have included advanced Pine programming techniques that conform to proper "Pine Ettiquette". Being this is one of John Ehlers' first two simultaneously released indicators for 2020, I felt a few more bells and whistles were appropriate as a proper contribution to the Tradingview community.
Features List Includes:
Dark Background - Easily disabled in indicator Settings->Style for "Light" charts or with Pine commenting
AND much, much more... You have the source!
The comments section below is solely just for commenting and other remarks, ideas, compliments, etc... regarding only this indicator, not others. When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members, I may implement more ideas when they present themselves as worthy additions. As always, "Like" it if you simply just like it with a proper thumbs up, and also return to my scripts list occasionally for additional postings. Have a profitable future everyone!
Ichimoku Kinkō hyō Keizen 改善
The script is not finnished yet and show's an other interpretation of how it could be scripted
Step -1 is complete... Basic Ichimoku with asjutable length and editable lines colors and visibilities.
Step -2 in progress... Adding ability to une multiple Spans, sens and Kumo on higher and lower timeframe.
Your Step : Like and Share ;) have a good year 2020 !
2020-01-06 /--------/ -R.V.
Credit Spread RegimeThe Credit Market as Economic Barometer
Credit spreads are among the most reliable leading indicators of economic stress. When corporations borrow money by issuing bonds, investors demand a premium above the risk-free Treasury rate to compensate for the possibility of default. This premium, known as the credit spread, fluctuates based on perceptions of economic health, corporate profitability, and systemic risk.
The relationship between credit spreads and economic activity has been studied extensively. Two papers form the foundation of this indicator. Pierre Collin-Dufresne, Robert Goldstein, and Spencer Martin published their influential 2001 paper in the Journal of Finance, documenting that credit spread changes are driven by factors beyond firm-specific credit quality. They found that a substantial portion of spread variation is explained by market-wide factors, suggesting credit spreads contain information about aggregate economic conditions.
Simon Gilchrist and Egon Zakrajsek extended this research in their 2012 American Economic Review paper, introducing the concept of the Excess Bond Premium. They demonstrated that the component of credit spreads not explained by default risk alone is a powerful predictor of future economic activity. Elevated excess spreads precede recessions with remarkable consistency.
What Credit Spreads Reveal
Credit spreads measure the difference in yield between corporate bonds and Treasury securities of similar maturity. High yield bonds, also called junk bonds, carry ratings below investment grade and offer higher yields to compensate for greater default risk. Investment grade bonds have lower yields because the probability of default is smaller.
The spread between high yield and investment grade bonds is particularly informative. When this spread widens, investors are demanding significantly more compensation for taking on credit risk. This typically indicates deteriorating economic expectations, tighter financial conditions, or increasing risk aversion. When the spread narrows, investors are comfortable accepting lower premiums, signaling confidence in corporate health.
The Gilchrist-Zakrajsek research showed that credit spreads contain two distinct components. The first is the expected default component, which reflects the probability-weighted cost of potential defaults based on corporate fundamentals. The second is the excess bond premium, which captures additional compensation demanded beyond expected defaults. This excess premium rises when investor risk appetite declines and financial conditions tighten.
The Implementation Approach
This indicator uses actual option-adjusted spread data from the Federal Reserve Economic Database (FRED), available directly in TradingView. The ICE BofA indices represent the industry standard for measuring corporate bond spreads.
The primary data sources are FRED:BAMLH0A0HYM2, the ICE BofA US High Yield Index Option-Adjusted Spread, and FRED:BAMLC0A0CM, the ICE BofA US Corporate Index Option-Adjusted Spread for investment grade bonds. These indices measure the spread of corporate bonds over Treasury securities of similar duration, expressed in basis points.
Option-adjusted spreads account for embedded options in corporate bonds, providing a cleaner measure of credit risk than simple yield spreads. The methodology developed by ICE BofA is widely used by institutional investors and central banks for monitoring credit conditions.
The indicator offers two modes. The HY-IG excess spread mode calculates the difference between high yield and investment grade spreads, isolating the pure compensation for below-investment-grade credit risk. This measure is less affected by broad interest rate movements. The HY-only mode tracks the absolute high yield spread, capturing both credit risk and the overall level of risk premiums in the market.
Interpreting the Regimes
Credit conditions are classified into four regimes based on Z-scores calculated from the spread proxy.
The Stress regime occurs when spreads reach extreme levels, typically above a Z-score of 2.0. At this point, credit markets are pricing in significant default risk and economic deterioration. Historically, stress regimes have coincided with recessions, financial crises, and major market dislocations. The 2008 financial crisis, the 2011 European debt crisis, the 2016 commodity collapse, and the 2020 pandemic all triggered credit stress regimes.
The Elevated regime, between Z-scores of 1.0 and 2.0, indicates above-normal risk premiums. Credit conditions are tightening. This often occurs in the build-up to stress events or during periods of uncertainty. Risk management should be heightened, and exposure to credit-sensitive assets may be reduced.
The Normal regime covers Z-scores between -1.0 and 1.0. This represents typical credit conditions where spreads fluctuate around historical averages. Standard investment approaches are appropriate.
The Low regime occurs when spreads are compressed below a Z-score of -1.0. Investors are accepting below-average compensation for credit risk. This can indicate complacency, strong economic confidence, or excessive risk-taking. While often associated with favorable conditions, extremely tight spreads sometimes precede sudden reversals.
Credit Cycle Dynamics
Beyond static regime classification, the indicator tracks the direction and acceleration of spread movements. This reveals where credit markets stand in the credit cycle.
The Deteriorating phase occurs when spreads are elevated and continuing to widen. Credit conditions are actively worsening. This phase often precedes or coincides with economic downturns.
The Recovering phase occurs when spreads are elevated but beginning to narrow. The worst may be over. Credit conditions are improving from stressed levels. This phase often accompanies the early stages of economic recovery.
The Tightening phase occurs when spreads are low and continuing to compress. Credit conditions are very favorable and improving further. This typically occurs during strong economic expansions but may signal building complacency.
The Loosening phase occurs when spreads are low but beginning to widen from compressed levels. The extremely favorable conditions may be normalizing. This can be an early warning of changing sentiment.
Relationship to Economic Activity
The predictive power of credit spreads for economic activity is well-documented. Gilchrist and Zakrajsek found that the excess bond premium predicts GDP growth, industrial production, and unemployment rates over horizons of one to four quarters.
When credit spreads spike, the cost of corporate borrowing increases. Companies may delay or cancel investment projects. Reduced investment leads to slower growth and eventually higher unemployment. The transmission mechanism runs from financial conditions to real economic activity.
Conversely, tight credit spreads lower borrowing costs and encourage investment. Easy credit conditions support economic expansion. However, excessively tight spreads may encourage over-leveraging, planting seeds for future stress.
Practical Application
For equity investors, credit spreads provide context for market risk. Equities and credit often move together because both reflect corporate health. Rising credit spreads typically accompany falling stock prices. Extremely wide spreads historically have coincided with equity market bottoms, though timing the reversal remains challenging.
For fixed income investors, spread regimes guide sector allocation decisions. During stress regimes, flight to quality favors Treasuries over corporates. During low regimes, spread compression may offer limited additional return for credit risk, suggesting caution on high yield.
For macro traders, credit spreads complement other indicators of financial conditions. Credit stress often leads equity volatility, providing an early warning signal. Cross-asset strategies may use credit regime as a filter for position sizing.
Limitations and Considerations
FRED data updates with a lag, typically one business day for the ICE BofA indices. For intraday trading decisions, more current proxies may be necessary. The data is most reliable on daily timeframes.
Credit spreads can remain at extreme levels for extended periods. Mean reversion signals indicate elevated probability of normalization but do not guarantee timing. The 2008 crisis saw spreads remain elevated for many months before normalizing.
The indicator is calibrated for US credit markets. Application to other regions would require different data sources such as European or Asian credit indices. The relationship between spreads and subsequent economic activity may vary across market cycles and structural regimes.
References
Collin-Dufresne, P., Goldstein, R.S., and Martin, J.S. (2001). The Determinants of Credit Spread Changes. Journal of Finance, 56(6), 2177-2207.
Gilchrist, S., and Zakrajsek, E. (2012). Credit Spreads and Business Cycle Fluctuations. American Economic Review, 102(4), 1692-1720.
Krishnamurthy, A., and Muir, T. (2017). How Credit Cycles across a Financial Crisis. Working Paper, Stanford University.
Absorption RatioThe Hidden Connections Between Markets
Financial markets are not isolated islands. When panic spreads, seemingly unrelated assets suddenly begin moving in lockstep. Stocks, bonds, commodities, and currencies that normally provide diversification benefits start falling together. This phenomenon, where correlations spike during crises, has devastated portfolios throughout history. The Absorption Ratio provides a quantitative measure of this hidden fragility.
The concept emerged from research at State Street Associates, where Mark Kritzman, Yuanzhen Li, Sebastien Page, and Roberto Rigobon developed a novel application of principal component analysis to measure systemic risk. Their 2011 paper in the Journal of Portfolio Management demonstrated that when markets become tightly coupled, the variance explained by the first few principal components increases dramatically. This concentration of variance signals elevated systemic risk.
What the Absorption Ratio Measures
Principal component analysis, or PCA, is a statistical technique that identifies the underlying factors driving a set of variables. When applied to asset returns, the first principal component typically captures broad market movements. The second might capture sector rotations or risk-on/risk-off dynamics. Additional components capture increasingly idiosyncratic patterns.
The Absorption Ratio measures the fraction of total variance absorbed or explained by a fixed number of principal components. In the original research, Kritzman and colleagues used the first fifth of the eigenvectors. When this fraction is high, it means a small number of factors are driving most of the market movements. Assets are moving together, and diversification provides less protection than usual.
Consider an analogy: imagine a room full of people having independent conversations. Each person speaks at different times about different topics. The total "variance" of sound in the room comes from many independent sources. Now imagine a fire alarm goes off. Suddenly everyone is talking about the same thing, moving in the same direction. The variance is now dominated by a single factor. The Absorption Ratio captures this transition from diverse, independent behavior to unified, correlated movement.
The Implementation Approach
TradingView does not support matrix algebra required for true principal component analysis. This implementation uses a closely related proxy: the average absolute correlation across a universe of major asset classes. This approach captures the same underlying phenomenon because when assets are highly correlated, the first principal component explains more variance by mathematical necessity.
The asset universe includes eight ETFs representing major investable categories: SPY and QQQ for large cap US equities, IWM for small caps, EFA for developed international markets, EEM for emerging markets, TLT for long-term treasuries, GLD for gold, and USO for oil. This selection provides exposure to equities across geographies and market caps, plus traditional diversifying assets.
From eight assets, there are twenty-eight unique pairwise correlations. The indicator calculates each using a rolling window, takes the absolute value to measure coupling strength regardless of direction, and averages across all pairs. This average correlation is then transformed to match the typical range of published Absorption Ratio values.
The transformation maps zero average correlation to an AR of 0.50 and perfect correlation to an AR of 1.00. This scaling aligns with empirical observations that the AR typically fluctuates between 0.60 and 0.95 in practice.
Interpreting the Regimes
The indicator classifies systemic risk into four regimes based on AR levels.
The Extreme regime occurs when the AR exceeds 0.90. At this level, nearly all asset classes are moving together. Diversification has largely failed. Historically, this regime has coincided with major market dislocations: the 2008 financial crisis, the 2020 COVID crash, and significant correction periods. Portfolios constructed under normal correlation assumptions will experience larger drawdowns than expected.
The High regime, between 0.80 and 0.90, indicates elevated systemic risk. Correlations across asset classes are above normal. This often occurs during the build-up to stress events or during volatile periods where fear is spreading but has not reached panic levels. Risk management should be more conservative.
The Normal regime covers AR values between 0.60 and 0.80. This represents typical market conditions where some correlation exists between assets but diversification still provides meaningful benefits. Standard portfolio construction assumptions are reasonable.
The Low regime, below 0.60, indicates that assets are behaving relatively independently. Diversification is working well. Idiosyncratic factors dominate returns rather than systematic risk. This environment is favorable for active management and security selection strategies.
The Relationship to Portfolio Construction
The implications for portfolio management are significant. Modern portfolio theory assumes correlations are stable and uses historical estimates to construct efficient portfolios. The Absorption Ratio reveals that this assumption is violated precisely when it matters most.
When AR is elevated, the effective number of independent bets in a diversified portfolio shrinks. A portfolio holding stocks, bonds, commodities, and real estate might behave as if it holds only one or two positions during high AR periods. Position sizing based on normal correlation estimates will underestimate portfolio risk.
Conversely, when AR is low, true diversification opportunities expand. The same nominal portfolio provides more independent return streams. Risk can be deployed more aggressively while maintaining the same effective exposure.
Component Analysis
The indicator separately tracks equity correlations and cross-asset correlations. These components tell different stories about market structure.
Equity correlations measure coupling within the stock market. High equity correlation indicates broad risk-on or risk-off behavior where all stocks move together. This is common during both rallies and selloffs driven by macroeconomic factors. Stock pickers face headwinds when equity correlations are elevated because individual company fundamentals matter less than market beta.
Cross-asset correlations measure coupling between different asset classes. When stocks, bonds, and commodities start moving together, traditional hedges fail. The classic 60/40 stock/bond portfolio, for example, assumes negative or low correlation between equities and treasuries. When cross-asset correlation spikes, this assumption breaks down.
During the 2022 market environment, for instance, both stocks and bonds fell significantly as inflation and rate hikes affected all assets simultaneously. High cross-asset correlation warned that the usual defensive allocations would not provide their expected protection.
Mean Reversion Characteristics
Like most risk metrics, the Absorption Ratio tends to mean-revert over time. Extremely high AR readings eventually normalize as panic subsides and assets return to more independent behavior. Extremely low readings tend to rise as some level of systematic risk always reasserts itself.
The indicator tracks AR in statistical terms by calculating its Z-score relative to the trailing distribution. When AR reaches extreme Z-scores, the probability of normalization increases. This creates potential opportunities for strategies that bet on mean reversion in systemic risk.
A buy signal triggers when AR recovers from extremely elevated levels, suggesting the worst of the correlation spike may be over. A sell signal triggers when AR rises from unusually low levels, warning that complacency about diversification benefits may be excessive.
Momentum and Trend
The rate of change in AR carries information beyond the absolute level. Rapidly rising AR suggests correlations are increasing and systemic risk is building. Even if AR has not yet reached the high regime, acceleration in coupling should prompt increased vigilance.
Falling AR momentum indicates normalizing conditions. Correlations are decreasing and assets are returning to more independent behavior. This often occurs in the recovery phase following stress events.
Practical Application
For asset allocators, the AR provides guidance on how much diversification benefit to expect from a given allocation. During high AR periods, reducing overall portfolio risk makes sense because the usual diversifiers provide less protection. During low AR periods, standard or even aggressive allocations are more appropriate.
For risk managers, the AR serves as an early warning indicator. Rising AR often precedes large market moves and volatility spikes. Tightening risk limits before correlations reach extreme levels can protect capital.
For systematic traders, the AR provides a regime filter. Mean reversion strategies may work better during high AR periods when panics create overshooting. Momentum strategies may work better during low AR periods when trends can develop independently across assets.
Limitations and Considerations
The proxy methodology introduces some approximation error relative to true PCA-based AR calculations. The asset universe, while representative, does not include all possible diversifiers. Correlation estimates are inherently backward-looking and can change rapidly.
The transformation from average correlation to AR scale is calibrated to match typical published ranges but is not mathematically equivalent to the eigenvalue ratio. Users should interpret levels directionally rather than as precise measurements.
Correlation regimes can persist longer than expected. Mean reversion signals indicate elevated probability of normalization but do not guarantee timing. High AR can remain elevated throughout extended crisis periods.
References
Kritzman, M., Li, Y., Page, S., and Rigobon, R. (2011). Principal Components as a Measure of Systemic Risk. Journal of Portfolio Management, 37(4), 112-126.
Kritzman, M., and Li, Y. (2010). Skulls, Financial Turbulence, and Risk Management. Financial Analysts Journal, 66(5), 30-41.
Billio, M., Getmansky, M., Lo, A., and Pelizzon, L. (2012). Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors. Journal of Financial Economics, 104(3), 535-559.
Self-Organized Criticality - Avalanche DistributionHere's all you need to know: This indicator applies Self-Organized Criticality (SOC) theory to financial markets, measuring the power-law exponent (alpha) of price drawdown distributions. It identifies whether markets are in stable Gaussian regimes or critical states where large cascading moves become more probable.
Self-Organized Criticality
SOC theory, introduced by Per Bak, Tang, and Wiesenfeld (1987), describes how complex systems naturally evolve toward critical (fragile) states. An example is a sand pile: adding grains creates avalanches whose sizes follow a power-law distribution rather than a normal distribution.
Financial markets exhibit similar behavior. Price movements aren't purely random walks—they display:
Fat-tailed distributions (more extreme events than Gaussian models predict)
Scale invariance (no characteristic avalanche size)
Intermittent dynamics (periods of calm punctuated by large cascades)
Power-Law Distributions
When a system is in a critical state, the probability of an avalanche of size s follows:
P(s) ∝ s^(-α)
Where:
α (alpha) is the power-law exponent
Higher α → distribution resembles Gaussian (large events rare)
Lower α → heavy tails dominate (large events common)
This indicator estimates α from the empirical distribution of price drawdowns.
Mathematical Method
1. Avalanche Detection
The indicator identifies local price peaks (highest point in a lookback window), then measures the percentage drawdown to the next trough. A dynamic ATR-based threshold filters out noise—small drops in calm markets count, but the bar rises in volatile periods.
2. Logarithmic Binning
Avalanche sizes are sorted into logarithmically-spaced bins (e.g., 1-2%, 2-4%, 4-8%) rather than linear bins. This captures power-law behavior across multiple scales - a 2% drop and 20% crash both matter. The indicator creates 12 adaptive bins spanning from your smallest to largest observed avalanche.
3. Bin-to-Bin Ratio Estimation
For each pair of adjacent bins, we calculate:
α ≈ log(N₁/N₂) / log(s₂/s₁)
Where N₁ and N₂ are avalanche counts, s₁ and s₂ are bin sizes.
Example: If 2% drops happen 4× more often than 4% drops, then α ≈ log(4)/log(2) ≈ 2.0.
We get 8-11 independent estimates and average them. This is more robust than fitting one line through all points—outliers can't dominate.
4. Rolling Window Analysis
Alpha recalculates using only recent avalanches (default: last 500 bars). Old data drops out as new avalanches occur, so the indicator tracks regime shifts in real-time.
Regime Classification
🟢 Gaussian α ≥ 2.8 Normal distribution behavior; large moves are rare outliers
🟡 Transitional 1.8 ≤ α < 2.8 Moderate fat tails; system approaching criticality
🟠 Critical 1.0 ≤ α < 1.8 Heavy tails; large avalanches increasingly common
🔴 Super-Critical α < 1.0 Extreme tail risk; system prone to cascading failures
What Alpha Tells You
Declining alpha → Market moving toward criticality; tail risk increasing
Rising alpha → Market stabilizing; returns to normal distribution
Persistent low alpha → Sustained fragility; heightened crash probability
Supporting Metrics
Heavy Tail %: Concentration of total drawdown in largest 10% of events
Populated Bins: Data coverage quality (11-12 out of 12 is ideal)
Avalanche Count: Sample size for statistical reliability
Limitations
This is a distributional measure, not a timing indicator. Low alpha indicates increased systemic risk but doesn't predict when a cascade will occur. Only that the probability distribution has shifted toward larger events.
How This Differs from the Per Bak Fragility Index
The SOC Avalanche Distribution calculates the power-law exponent (alpha) directly from price drawdown distributions - a pure mathematical analysis requiring only price data. The Per Bak Fragility Index aggregates external stress indicators (VIX, SKEW, credit spreads, put/call ratios) into a weighted composite score.
Technical Notes
Default settings optimized for daily and weekly timeframes on major indices
Requires minimum 200 bars of history for stable estimates
ATR-based dynamic sizing prevents scale-dependent bias
Alerts available for regime transitions and super-critical entry
References
Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of the 1/f noise. Physical Review Letters.
Sornette, D. (2003). Why Stock Markets Crash: Critical Events in Complex Financial Systems. Princeton University Press.
BTC – LEVR: Leverage Efficiency & Volume RatioLEVR: Leverage Efficiency & Volume Ratio
Observation-only. Data: IntoTheBlock.
Overview
The Leverage Efficiency & Volume Ratio (LEVR) is a market structure oscillator designed to detect "Paper Bubbles" and "Organic Bottoms" by separating speculative greed from network utility. While most indicators analyze price action, LEVR analyzes market fragility. It operates on the thesis that Sustainable Rallies are driven by Spot/Network Activity, while Fragile Rallies are driven by Derivatives Leverage.
Synergy
How it works with VERI
LEVR is designed to be the tactical counterpart to the fundamental VERI Indicator (Valuation & Entity Ratio Index).
Use VERI for Strategy: To identify Value. (Is Bitcoin cheap? Are Whales buying?)
Use LEVR for Risk: To identify Structure. (Is the current price move real, or is it a leverage bubble about to pop?)
The "Perfect Setup"
The strongest buy signals occur when VERI is in the Accumulation Zone (Whales buying) AND LEVR is in the Organic Zone (Leverage is flushed out) (as it was the case in the Dec 2022 Bear Market Bottom).
Why LEVR is Unique
Standard indicators often fail to contextualize Open Interest:
vs. Raw Open Interest: Raw OI always trends up over time as the market grows. LEVR solves this by normalizing OI against Active Addresses. This reveals when leverage is outpacing actual adoption.
vs. ELR (Estimated Leverage Ratio): Classic ELR divides Open Interest by Exchange Reserves. However, Exchange Reserves are notoriously difficult to track accurately. LEVR uses Active Addresses (Network Utility) as a cleaner, more reliable denominator for network health.
Methodology
The Mathematics: The indicator calculates a normalized Z-Score ratio between two IntoTheBlock datasets:
The Numerator (Greed): Perpetual Open Interest. The total dollar value of all open futures contracts. This represents the "Gambling" capital.
The Denominator (Utility): Active Addresses. The number of unique addresses transacting on-chain. This represents the "Real" user base.
The Formula : LEVR = Z-Score ( Perpetual Open Interest / Active Addresses )
How to Interpret the Visuals
The line color changes dynamically to reflect the current risk regime:
🟥 Speculative Premium (Red Line > 2.0) :
Signal: "Leverage Bubble."
Context: Open Interest is rising significantly faster than User Growth. The rally is fueled by debt.
Risk: High probability of a "Long Squeeze" or liquidation cascade.
🟦 Organic Base (Blue Line < -1.5) :
Signal: "Spot Driven Market."
Context: Speculators have been flushed out, but active network usage remains high. The line turns Blue to signal a healthy opportunity zone.
Risk: Low. Historically marks robust bottoms where hands are strong.
🟧 Neutral (Orange Line) :
The market is in a transition phase between organic growth and speculation.
Settings & Inputs
Users can customize the sensitivity of the Z-Score to fit their trading style (in brackets their current standard value):
Lookback Period (365) : The rolling window used to establish the "Baseline." A 365-day window captures the yearly trend.
Signal Smoothing (7) : A short moving average to reduce daily data noise.
Bubble Zone Top/Bottom (3.0 / 2.0) : The thresholds for the Red Zone. Raising the "Top" value will only show the most extreme, generational leverage bubbles.
Organic Zone Top/Bottom (-1.5 / -2.5) : The thresholds for the Green Zone. Lowering these values requires a deeper "flush" to trigger a signal.
Optimization
This indicator is mathematically optimized for the Daily (1D) timeframe. Using it on lower timeframes may result in noise due to the daily resolution of on-chain data.
Important Note on Historical Data
Please be aware that aggregated global Perpetual Open Interest data only becomes reliable and widely available starting around 2020-2021.
Pre-2021: The indicator will show a flat line or empty values. This is not a bug; it reflects the lack of historical derivatives market data for that period.
2021-Present: The indicator functions fully as intended.
Credits
Concept inspired by the "Estimated Leverage Ratio" (ELR) popularised by CryptoQuant and analysts like Willy Woo. LEVR adapts this concept for TradingView by substituting Exchange Reserves with Network Activity for better reliability.
Disclaimer
This tool is for research purposes only. It visualizes market structure data and does not constitute financial advice.
Tags
bitcoin, btc, open interest, leverage, on-chain, intotheblock, risk, derivatives, levr, veri
Stochastic Hash Strat [Hash Capital Research]# Stochastic Hash Strategy by Hash Capital Research
## 🎯 What Is This Strategy?
The **Stochastic Slow Strategy** is a momentum-based trading system that identifies oversold and overbought market conditions to capture mean-reversion opportunities. Think of it as a "buy low, sell high" approach with smart mathematical filters that remove emotion from your trading decisions.
Unlike fast-moving indicators that generate excessive noise, this strategy uses **smoothed stochastic oscillators** to identify only the highest-probability setups when momentum truly shifts.
---
## 💡 Why This Strategy Works
Most traders fail because they:
- **Chase prices** after big moves (buying high, selling low)
- **Overtrade** in choppy, directionless markets
- **Exit too early** or hold losses too long
This strategy solves all three problems:
1. **Entry Discipline**: Only trades when the stochastic oscillator crosses in extreme zones (oversold for longs, overbought for shorts)
2. **Cooldown Filter**: Prevents revenge trading by forcing a waiting period after each trade
3. **Fixed Risk/Reward**: Pre-defined stop-loss and take-profit levels ensure consistent risk management
**The Math Behind It**: The stochastic oscillator measures where the current price sits relative to its recent high-low range. When it's below 25, the market is oversold (time to buy). When above 70, it's overbought (time to sell). The crossover with its moving average confirms momentum is shifting.
---
## 📊 Best Markets & Timeframes
### ⭐ OPTIMAL PERFORMANCE:
**Crude Oil (WTI) - 12H Timeframe**
- **Why it works**: Oil markets have predictable volatility patterns and respect technical levels
**AAVE/USD - 4H to 12H Timeframe**
- **Why it works**: DeFi tokens exhibit strong momentum cycles with clear extremes
### ✅ Also Works Well On:
- **BTC/USD** (12H, Daily) - Lower frequency but high win rate
- **ETH/USD** (8H, 12H) - Balanced volatility and liquidity
- **Gold (XAU/USD)** (Daily) - Classic mean-reversion asset
- **EUR/USD** (4H, 8H) - Lower volatility, requires patience
### ❌ Avoid Using On:
- Timeframes below 4H (too much noise)
- Low-liquidity altcoins (wide spreads kill performance)
- Strongly trending markets without pullbacks (Bitcoin in 2021)
- News-driven instruments during major events
---
## 🎛️ Understanding The Settings
### Core Stochastic Parameters
**Stochastic Length (Default: 16)**
- Controls the lookback period for price comparison
- Lower = faster reactions, more signals (10-14 for volatile markets)
- Higher = smoother signals, fewer trades (16-21 for stable markets)
- **Pro tip**: Use 10 for crypto 4H, 16 for commodities 12H
**Overbought Level (Default: 70)**
- Threshold for short entries
- Lower values (65-70) = more trades, earlier entries
- Higher values (75-80) = fewer but higher-conviction trades
- **Sweet spot**: 70 works for most assets
**Oversold Level (Default: 25)**
- Threshold for long entries
- Higher values (25-30) = more trades, earlier entries
- Lower values (15-20) = fewer but stronger bounce setups
- **Sweet spot**: 20-25 depending on market conditions
**Smooth K & Smooth D (Default: 7 & 3)**
- Additional smoothing to filter out whipsaws
- K=7 makes the indicator slower and more reliable
- D=3 is the signal line that confirms the trend
- **Don't change these unless you know what you're doing**
---
### Risk Management
**Stop Loss % (Default: 2.2%)**
- Automatically exits losing trades
- Should be 1.5x to 2x your average market volatility
- Too tight = death by a thousand cuts
- Too wide = uncontrolled losses
- **Calibration**: Check ATR indicator and set SL slightly above it
**Take Profit % (Default: 7%)**
- Automatically exits winning trades
- Should be 2.5x to 3x your stop loss (reward-to-risk ratio)
- This default gives 7% / 2.2% = 3.18:1 R:R
- **The golden rule**: Never have R:R below 2:1
---
### Trade Filters
**Bar Cooldown Filter (Default: ON, 3 bars)**
- **What it does**: Forces you to wait X bars after closing a trade before entering a new one
- **Why it matters**: Prevents emotional revenge trading and overtrading in choppy markets
- **Settings guide**:
- 3 bars = Standard (good for most cases)
- 5-7 bars = Conservative (oil, slow-moving assets)
- 1-2 bars = Aggressive (only for experienced traders)
**Exit on Opposite Extreme (Default: ON)**
- Closes your long when stochastic hits overbought (and vice versa)
- Acts as an early profit-taking mechanism
- **Leave this ON** unless you're testing other exit strategies
**Divergence Filter (Default: OFF)**
- Looks for price/momentum divergences for additional confirmation
- **When to enable**: Trending markets where you want fewer but higher-quality trades
- **Keep OFF for**: Mean-reverting markets (oil, forex, most of the time)
---
## 🚀 Quick Start Guide
### Step 1: Set Up in TradingView
1. Open TradingView and navigate to your chart
2. Click "Pine Editor" at the bottom
3. Copy and paste the strategy code
4. Click "Add to Chart"
5. The strategy will appear in a separate pane below your price chart
### Step 2: Choose Your Market
**If you're trading Crude Oil:**
- Timeframe: 12H
- Keep all default settings
- Watch for signals during London/NY overlap (8am-11am EST)
**If you're trading AAVE or crypto:**
- Timeframe: 4H or 12H
- Consider these adjustments:
- Stochastic Length: 10-14 (faster)
- Oversold: 20 (more aggressive)
- Take Profit: 8-10% (higher targets)
### Step 3: Wait for Your First Signal
**LONG Entry** (Green circle appears):
- Stochastic crosses up below oversold level (25)
- Price likely near recent lows
- System places limit order at take profit and stop loss
**SHORT Entry** (Red circle appears):
- Stochastic crosses down above overbought level (70)
- Price likely near recent highs
- System places limit order at take profit and stop loss
**EXIT** (Orange circle):
- Position closes either at stop, target, or opposite extreme
- Cooldown period begins
### Step 4: Let It Run
The biggest mistake? **Interfering with the system.**
- Don't close trades early because you're scared
- Don't skip signals because you "have a feeling"
- Don't increase position size after a big win
- Don't revenge trade after a loss
**Follow the system or don't use it at all.**
---
### Important Risks:
1. **Drawdown Pain**: You WILL experience losing streaks of 5-7 trades. This is mathematically normal.
2. **Whipsaw Markets**: Choppy, range-bound conditions can trigger multiple small losses.
3. **Gap Risk**: Overnight gaps can cause your actual fill to be worse than the stop loss.
4. **Slippage**: Real execution prices differ from backtested prices (factor in 0.1-0.2% slippage).
---
## 🔧 Optimization Guide
### When to Adjust Settings:
**Market Volatility Increased?**
- Widen stop loss by 0.5-1%
- Increase take profit proportionally
- Consider increasing cooldown to 5-7 bars
**Getting Too Few Signals?**
- Decrease stochastic length to 10-12
- Increase oversold to 30, decrease overbought to 65
- Reduce cooldown to 2 bars
**Getting Too Many Losses?**
- Increase stochastic length to 18-21 (slower, smoother)
- Enable divergence filter
- Increase cooldown to 5+ bars
- Verify you're on the right timeframe
### A/B Testing Method:
1. **Run default settings for 50 trades** on your chosen market
2. Document: Win rate, profit factor, max drawdown, emotional tolerance
3. **Change ONE variable** (e.g., oversold from 25 to 20)
4. Run another 50 trades
5. Compare results
6. Keep the better version
**Never change multiple settings at once** or you won't know what worked.
---
## 📚 Educational Resources
### Key Concepts to Learn:
**Stochastic Oscillator**
- Developed by George Lane in the 1950s
- Measures momentum by comparing closing price to price range
- Formula: %K = (Close - Low) / (High - Low) × 100
- Similar to RSI but more sensitive to price movements
**Mean Reversion vs. Trend Following**
- This is a **mean reversion** strategy (price returns to average)
- Works best in ranging markets with defined support/resistance
- Fails in strong trending markets (2017 Bitcoin, 2020 Tech stocks)
- Complement with trend filters for better results
**Risk:Reward Ratio**
- The cornerstone of profitable trading
- Winning 40% of trades with 3:1 R:R = profitable
- Winning 60% of trades with 1:1 R:R = breakeven (after fees)
- **This strategy aims for 45% win rate with 2.5-3:1 R:R**
### Recommended Reading:
- *"Trading Systems and Methods"* by Perry Kaufman (Chapter on Oscillators)
- *"Mean Reversion Trading Systems"* by Howard Bandy
- *"The New Trading for a Living"* by Dr. Alexander Elder
---
## 🛠️ Troubleshooting
### "I'm not seeing any signals!"
**Check:**
- Is your timeframe 4H or higher?
- Is the stochastic actually reaching extreme levels (check if your asset is stuck in middle range)?
- Is cooldown still active from a previous trade?
- Are you on a low-liquidity pair?
**Solution**: Switch to a more volatile asset or lower the overbought/oversold thresholds.
---
### "The strategy keeps losing money!"
**Check:**
- What's your win rate? (Below 35% is concerning)
- What's your profit factor? (Below 0.8 means serious issues)
- Are you trading during major news events?
- Is the market in a strong trend?
**Solution**:
1. Verify you're using recommended markets/timeframes
2. Increase cooldown period to avoid choppy markets
3. Reduce position size to 5% while you diagnose
4. Consider switching to daily timeframe for less noise
---
### "My stop losses keep getting hit!"
**Check:**
- Is your stop loss tighter than the average ATR?
- Are you trading during high-volatility sessions?
- Is slippage eating into your buffer?
**Solution**:
1. Calculate the 14-period ATR
2. Set stop loss to 1.5x the ATR value
3. Avoid trading right after market open or major news
4. Factor in 0.2% slippage for crypto, 0.1% for oil
---
## 💪 Pro Tips from the Trenches
### Psychological Discipline
**The Three Deadly Sins:**
1. **Skipping signals** - "This one doesn't feel right"
2. **Early exits** - "I'll just take profit here to be safe"
3. **Revenge trading** - "I need to make back that loss NOW"
**The Solution:** Treat your strategy like a business system. Would McDonald's skip making fries because the cashier "doesn't feel like it today"? No. Systems work because of consistency.
---
### Position Management
**Scaling In/Out** (Advanced)
- Enter 50% position at signal
- Add 50% if stochastic reaches 10 (oversold) or 90 (overbought)
- Exit 50% at 1.5x take profit, let the rest run
**This is NOT for beginners.** Master the basic system first.
---
### Market Awareness
**Oil Traders:**
- OPEC meetings = volatility spikes (avoid or widen stops)
- US inventory reports (Wed 10:30am EST) = avoid trading 2 hours before/after
- Summer driving season = different patterns than winter
**Crypto Traders:**
- Monday-Tuesday = typically lower volatility (fewer signals)
- Thursday-Sunday = higher volatility (more signals)
- Avoid trading during exchange maintenance windows
---
## ⚖️ Legal Disclaimer
This trading strategy is provided for **educational purposes only**.
- Past performance does not guarantee future results
- Trading involves substantial risk of loss
- Only trade with capital you can afford to lose
- No one associated with this strategy is a licensed financial advisor
- You are solely responsible for your trading decisions
**By using this strategy, you acknowledge that you understand and accept these risks.**
---
## 🙏 Acknowledgments
Strategy development inspired by:
- George Lane's original Stochastic Oscillator work
- Modern quantitative trading research
- Community feedback from hundreds of backtests
Built with ❤️ for retail traders who want systematic, disciplined approaches to the markets.
---
**Good luck, stay disciplined, and trade the system, not your emotions.**
Hash Supertrend [Hash Capital Research]Hash Supertrend Strategy by Hash Capital Research
Overview
Hash Supertrend is a professional-grade trend-following strategy that combines the proven Supertrend indicator with institutional visual design and flexible time filtering.
The strategy uses ATR-based volatility bands to identify trend direction and executes position reversals when the trend flips.This implementation features a distinctive fluorescent color system with customizable glow effects, making trend changes immediately visible while maintaining the clean, professional aesthetic expected in quantitative trading environments.
Entry Signals:
Long Entry: Price crosses above the Supertrend line (trend flips bullish)
Short Entry: Price crosses below the Supertrend line (trend flips bearish)
Controls the lookback period for volatility calculation
Lower values (7-10): More sensitive to price changes, generates more signals
Higher values (12-14): Smoother response, fewer signals but potentially delayed entries
Recommended range: 7-14 depending on market volatility
Factor (Default: 3.0)
Restricts trading to specific hours
Useful for avoiding low-liquidity sessions, overnight gaps, or known choppy periods
When disabled, strategy trades 24/7
Start Hour (Default: 9) & Start Minute (Default: 30)
Define when the trading session begins
Uses exchange timezone in 24-hour format
Example: 9:30 = 9:30 AM
End Hour (Default: 16) & End Minute (Default: 0)
Controls the vibrancy of the fluorescent color system
1-3: Subtle, muted colors
4-6: Balanced, moderate saturation
7-10: Bright, highly saturated fluorescent appearance
Affects both the Supertrend line and trend zones
Glow Effect (Default: On)
Adds luminous halo around the Supertrend line
Creates a multi-layered visual with depth
Particularly effective during strong trends
Glow Intensity (Default: 5.0)
Displays tiny fluorescent dots at entry points
Green dot below bar: Long entry
Red dot above bar: Short entry
Provides clear visual confirmation of executed trades
Show Trend Zone (Default: On)
Strong trending markets (2020-style bull runs, sustained bear markets)
Markets with clear directional bias
Instruments with consistent volatility patterns
Timeframes: 15m to Daily (optimal on 1H-4H)
Challenging Conditions:
Choppy, range-bound markets
Low volatility consolidation periods
Highly news-driven instruments with frequent gaps
Very low timeframes (1m-5m) prone to noise
Recommended AssetsCryptocurrency:
BTC Halving Cycle SignalsBTC Halving Cycle Signals
What signals does this script give in real history (2011-2025):
2015 → BUY (bear market bottom)
2019 → BUY (post-2018 bottom)
October 2020 → BUY
November 2023 → BUY
And right now (Nov 2025) → green bottom + price above weekly EMA200 → about to give a buy signal if it breaks $72k strongly.
BUY signal: ~500 days pre-halving + price > weekly EMA200 + monthly RSI <60 (accumulation).
SELL signal: ~1064 days post-halving + RSI >75 or close < SuperTrend (distribution).
Hardcoded halving dates (can be edited). Works on BTCUSD weekly/monthly, gives 1-2 signals per cycle.
200SMA Distance OscillatorThe oscillator measures the percentage deviation of closing price x from SMA200.
The idea behind the oscillator was preceded by an analysis of how often MAs in the index hold/bounce or are broken through.
Basically, the idea was about index analysis, i.e., the macro picture of a market.
Who wants to buy individual stocks when the overall market is plummeting ;-)
Or in other words: How long are you long in a market? When is it time to take profits?
After the analysis of the stability of SMAs in the index was rather modest (ratio of just under 6:4 for bounce to breakout – overall in 20, 50, 100, and 200 frames from 2020 to 2025), it was noticeable that the percentage over- or underperformance was scalable, especially in indices.
And since indices generally move upwards, there were fixed limits for over- and underestimations – especially in the longer term (SMA200) – unlike with individual stocks.
It is therefore more a question of macro trends and less of short-term movements, e.g., in day trading.
It was now interesting to see at what percentage range counter-movements were likely – particularly in the positive range for profit-taking, but of course also in the negative range for entry into sold-off markets.
If, for example, closing prices around +25% above SMA200 were reached in the NDX, the probability is very high that the market has overreacted and an interim correction will follow – so the theory goes.
On the other hand, continuous levels of +5 to +10% are a product of healthy positive development in a bull market and do not necessarily require action.
The oscillator was specifically designed for the NDX, but can also be used for the SPX and others.
The style was based on the RSI, so that the color level rises from 10% to 20% (overbought/oversold principle).
Based on manually examined movements, the criteria were set as follows:
+/-10% = flow / no color background
> +/-10% = border areas / color background
The center line represents the 252 average of the percentage deviations and could also be used as a trigger, provided it has been historically examined and is valid.
The oscillator is very interesting because it behaves completely differently from one financial instrument to another and, as a result, also in the timeframes (4h, D, W).
It would probably make sense to change the flow and border levels in the code when using it outside of indices.
The fact is that the oscillator must be “adjusted” to each instrument in order to achieve its goal of providing the best possible prediction. “Adjusting” refers to the analysis of the levels at which an instrument/asset usually reacts.
As with all indicators and oscillators, it is advisable to take other indicators and, in particular, macro news into account when analyzing this development.
If I find any substantial correlations with other indicators, I will be happy to provide an update.
The idea came from me, the code from Grok.
The code is not 100% perfect, but the data (percentage deviation, color background) is correct according to initial analysis.
In the settings, you can make the lines of the plots invisible. This makes the oscillator clearer. You can also adjust the settings for the average line.
Aspects of Mars-Saturn by BTThis script displays the most commonly used aspects between Mars and Saturn. It uses a +/-2 degree orb (deviation), meaning the script shows the dates when the calculated distance between Mars and Saturn is within a 2 degree deviation of a major aspect.
Most of the astrological applications uses 3 degree or more for orb however this will cause chart overload. So please keep in mind to consider a couple of dates before or after if you want to use bigger orb.
The script includes an option to plot only the start date of sequential aspect events to reduce visual clutter and improve chart clarity. It currently covers dates from 2020 to 2030, but more will be added soon.
Currently available aspects:
Conjunction - 0 Degree
Opposition - 180 Degree
Trine - 120 Degree
Square - 90 Degree
Sextile - 60 Degree
Inconjunction - 150 Degree
Semi-Sextile - 30 Degree
Semi-Square - 45 Degree
Sesquiquadrate - 135 Degree
Global M2 Money Supply Growth (GDP-Weighted)📊 Global M2 Money Supply Growth (GDP-Weighted)
This indicator tracks the weighted aggregate M2 money supply growth across the world's four largest economies: United States, China, Eurozone, and Japan. These economies represent approximately 69.3 trillion USD in combined GDP and account for the majority of global liquidity, making this a comprehensive macro indicator for analyzing worldwide monetary conditions.
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🔧 KEY FEATURES:
📈 GDP-Weighted Aggregation
Each economy is weighted proportionally by its nominal GDP using 2025 IMF World Economic Outlook data:
• United States: 44.2% (30.62 trillion USD)
• China: 28.0% (19.40 trillion USD)
• Eurozone: 21.6% (15.0 trillion USD)
• Japan: 6.2% (4.28 trillion USD)
The weights are fully adjustable through the indicator settings, allowing you to update them annually as new IMF forecasts are released (typically April and October).
⏱️ Multiple Time Period Options
Choose between three calculation methods to analyze different timeframes:
• YoY (Year-over-Year): 12-month growth rate for identifying long-term liquidity trends and cycles
• MoM (Month-over-Month): 1-month growth rate for detecting short-term monetary policy shifts
• QoQ (Quarter-over-Quarter): 3-month growth rate for medium-term trend analysis
🔄 Advanced Offset Function
Shift the entire indicator forward by 0-365 days to test lead/lag relationships between global liquidity and asset prices. Research suggests a 56-70 day lag between M2 changes and Bitcoin price movements, but you can experiment with different offsets for various assets (equities, gold, commodities, etc.).
🌍 Individual Country Breakdown
Real-time display of each economy's M2 growth rate with:
• Current percentage change (YoY/MoM/QoQ)
• GDP weight contribution
• Color-coded values (green = monetary expansion, red = contraction)
📊 Smart Overlay Capability
Displays directly on your main price chart with an independent left-side scale, allowing you to visually correlate global liquidity trends with any asset's price action without cluttering the chart.
🔧 Customizable GDP Weights
All GDP values can be adjusted through the indicator settings without editing code, making annual updates simple and accessible for all users.
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📡 DATA SOURCES:
All M2 money supply data is sourced from ECONOMICS (Trading Economics) for consistency and reliability:
• ECONOMICS:USM2 (United States)
• ECONOMICS:CNM2 (China)
• ECONOMICS:EUM2 (Eurozone)
• ECONOMICS:JPM2 (Japan)
All values are normalized to USD using current daily exchange rates (USDCNY, EURUSD, USDJPY) before GDP-weighted aggregation, ensuring accurate cross-country comparisons.
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💡 USE CASES & APPLICATIONS:
🔹 Liquidity Cycle Analysis
Track global monetary expansion/contraction cycles to identify when central banks are coordinating loose or tight monetary policies.
🔹 Market Timing & Risk Assessment
High M2 growth (>10%) historically correlates with risk-on environments and rising asset prices across crypto, equities, and commodities. Negative M2 growth signals monetary tightening and potential market corrections.
🔹 Bitcoin & Crypto Correlation
Compare with Bitcoin price using the offset feature to identify the optimal lag period. Many traders use 60-70 day offsets to predict crypto market movements based on liquidity changes.
🔹 Macro Portfolio Allocation
Use as a regime filter to adjust portfolio exposure: increase risk assets during liquidity expansion, reduce during contraction.
🔹 Central Bank Policy Divergence
Monitor individual country metrics to identify when major central banks are pursuing divergent policies (e.g., Fed tightening while China eases).
🔹 Inflation & Economic Forecasting
Rapid M2 growth often leads inflation by 12-18 months, making this a leading indicator for future inflation trends.
🔹 Recession Early Warning
Negative M2 growth is extremely rare and has preceded major recessions, making this a valuable risk management tool.
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📊 INTERPRETATION GUIDE:
🟢 +10% or Higher
Aggressive monetary expansion, typically during crises (2001, 2008, 2020). The COVID-19 period saw M2 growth reach 20-27%, which preceded significant inflation and asset price surges. Strong bullish signal for risk assets.
🟢 +6% to +10%
Above-average liquidity growth. Central banks are providing stimulus beyond normal levels. Generally favorable for equities, crypto, and commodities.
🟡 +3% to +6%
Normal/healthy growth rate, roughly in line with GDP growth plus 2% inflation targets. Neutral environment with moderate support for risk assets.
🟠 0% to +3%
Slowing liquidity, potential tightening phase beginning. Central banks may be raising rates or reducing balance sheets. Caution warranted for high-beta assets.
🔴 Negative Growth
Monetary contraction - extremely rare. Only occurred during aggressive Fed tightening in 2022-2023. Strong warning signal for risk assets, often precedes recessions or major market corrections.
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🎯 OPTIMAL USAGE:
📅 Recommended Timeframes:
• Daily or Weekly charts for macro analysis
• Monthly charts for very long-term trends
💹 Compatible Asset Classes:
• Cryptocurrencies (especially Bitcoin, Ethereum)
• Equity indices (S&P 500, NASDAQ, global markets)
• Commodities (Gold, Silver, Oil)
• Forex majors (DXY correlation analysis)
⚙️ Suggested Settings:
• Default: YoY calculation with 0 offset for current liquidity conditions
• Bitcoin traders: YoY with 60-70 day offset for predictive analysis
• Short-term traders: MoM with 0 offset for recent policy changes
• Quarterly rebalancers: QoQ with 0 offset for medium-term trends
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📋 VISUAL DISPLAY:
The indicator plots a blue line showing the selected growth metric (YoY/MoM/QoQ), with a dashed reference line at 0% to clearly identify expansion vs. contraction regimes.
A comprehensive table in the top-right corner displays:
• Current global M2 growth rate (large, prominent display)
• Individual country breakdowns with their GDP weights
• Color-coded growth rates (green for positive, red for negative)
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🔄 MAINTENANCE & UPDATES:
GDP weights should be updated annually (ideally in April or October) when the IMF releases new World Economic Outlook forecasts. Simply adjust the four GDP input parameters in the indicator settings - no code editing required.
The relative GDP proportions between the Big 4 economies change very gradually (typically <1-2% per year), so even if you update weights once every 1-2 years, the impact on the indicator's accuracy is minimal.
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💭 TRADING PHILOSOPHY:
This indicator embodies the principle that "liquidity drives markets." By tracking the combined M2 money supply of the world's largest economies, weighted by their economic size, you gain insight into the fundamental liquidity conditions that underpin all asset prices.
Unlike single-country M2 indicators, this GDP-weighted approach captures the true global picture, accounting for the fact that US monetary policy has 2x the impact of Japanese policy due to economic size differences.
Perfect for macro-focused traders, long-term investors, and anyone seeking to understand the "tide that lifts all boats" in financial markets.
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Created for traders and investors who incorporate global liquidity trends into their decision-making process. Best used alongside other technical and fundamental analysis tools for comprehensive market assessment.
⚠️ Disclaimer: M2 money supply is a lagging macroeconomic indicator. Past correlations do not guarantee future results. Always use proper risk management and combine with other analysis methods.
