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BTCCNY premiums over BTCUSD - yuan devaluationBitcoin as an alternative to capital outflows, with a market cap of just 2 billion can easily be up by multiples from the outflow of yuan from mainland China alone.
People simply do not want yuan if their purchasing power is going down over time.
Current methods of taking capital off mainland China via overseas cash withdrawals are quickly being closed out by the communist government.
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www.zerohedge.com
China has capped the amount of money Chinese holders of bank and credit cards can withdraw outside the country, in its latest effort to discourage people from moving badly needed capital offshore.
China’s foreign-exchange regulator put a new annual cap on overseas cash withdrawals using China UnionPay Co. bank cards, a UnionPay official said on Tuesday. Under the new rules, UnionPay cardholders can withdraw up to 50,000 yuan ($7,854) overseas during the last three months of this year, and the amount will be capped at 100,000 yuan for all of next year, the official said.
State-run UnionPay has a virtual monopoly on processing card transactions in China, meaning the limits extend to nearly all Chinese bank- and credit-card holders. It wasn’t clear when the new cap was issued.
The new cap is in addition to an existing 10,000 yuan daily withdrawal limit, part of China’s curbs on how much money can flow across its borders.
The move by China’s State Administration of Foreign Exchange is the latest by Beijing to scrutinize capital outflows.
The People’s Bank of China, the country’s central bank, said earlier this month that its foreign-exchange reserves fell by $93.9 billion, the biggest monthly drop ever, after it surprised the market on Aug. 11 with its decision to devalue the yuan by around 2%.
Key takeaway from the yuan devaluation and capital control:
The collapse of 2 bubbles: housing and stock market.
Weakness in commodities such as steel, copper and oil are seen which signals a weakening economy of which China are the core driver of that expansion since 2008. I suspect that China's GDP is never 8% as it is reported, but rather near 2~4% right now.
China have spent over 100 billion USD in US treasury proceeds to stabilize the yuan collapsing faster due to speculators. The amount of reserves spent at the current rate is unsustainable, it will take just 2 years for them to be used up completely.
Over time yuan will still be heading downwards.
Volume Weighted Market Mean 3.0 [Dia]150924 - v3.0
This script is a complete rewrite of the previous version here .
It gathers market data from up to 8 different Bitcoin exchange pairs (8 is maximum due to script limitations).The influence of each single exchange can be customized individually, default is 50% for 0-fee exchanges, 10% for futures and 100% for all others. For each single exchange a standard deviation from the composite mean price and a average price range are determined. To make data from exchanges with different liquidity comparable, all single exchange price ranges are normalized to the range of the composite mean price. To make data from exchanges with different fee structure (= exchanges that constantly trade at a premium / discount) comparable, all single exchange prices are corrected by their respective standard deviations. To account for trade volume, all price data is weighted by the current candles volume.
- optional Heikin Ashi candle display
- optional EMAs
- lots of switches to play around with ;-)
BTC Volume Index 1.1 beta [Dia]Indicator calculates total volume across multiple bitcoin exchanges. The influence of each exchange can be adjusted seperately.
0 fee exchanges default to 50%
0 fee futures default to 10%
Define custom lookback periods for price breakout detection.
Look for low-volume price reversals in those candles immediately following high volume peaks. The lower the volume and the greater the open/close price difference of that 2nd candle is compared to the previous one, the higher the likelihood of a local top/bottom being in.
Note:
Bar highlighting is still experimental, future versions will include better filtering options.
Use for confirmation only, not as standalone trading system.
Multi-exchange OBV V2.0This Script takes the average On Balance Volume of 4 bitcoin exchanges.
Bitfinex:BTCUSD
Okcoin:BTCUSD
BTCE:BTCUSD
HUOBI:BTCCNY
KK_Traders Dynamic Index_Bar HighlightingHey guys,
this is one of my favorite scripts as it represents a whole trading system that has given me very good results!
I have only used it on Bitcoin so far but I am sure it will also work for other instruments.
The original code to this was created by LazyBear, so all props to him for this great script!
I have linked his original post down below.
You can find the full rules to the system in this PDF (which has also been taken from LBs post):
www.forexmt4.com
Here is a short summary of the rules:
Go long when (all conditions have to be met):
The green line is above 50
The green line is above the red line
The green line is above the orange line
The close is above the upper Band of the Price Action Channel
The candles close is above its open
(The green line is below 68)
Go short when (all conditions have to be met):
The green line is below 50
The green line is below the red line
The green line is below the orange line
The close is below the lower band of the Price Action Channel
The candles close is below its open
(The green line is above 32)
Close when:
Any of these conditions aren't true anymore.
I have marked two of the rules in brackets as they seem to cut out a lot of the profits this system generates. You can choose to still use these rules by checking the box that says "Use Original Ruleset" in the options.
The system also contains rules regarding the Heiken Ashi bars. However these aren't as specific as the other rules. This is where your personal judgement comes in and this part is hard to explain. Take a look at the PDF I have linked to get a better understanding.
So far, this is just the TDI trading system and LBs script, now what have I changed?
I have incorporated the Price Action Channel to the system and changed it so that it highlights the bars whenever the system is giving a signal. As long as the bars are green the system is giving a long signal, as long as they are red the system is giving a short signal. Keep in mind that this doesn't consider the bar size of the HA bars. I recommend coloring all bars grey via the chart settings in order to be able to see the bar highlighting properly.
I have also published the Price Action Channel seperately in case some of you wish to view the Channel.
I am fairly new to creating scripts so use it with caution and let me know what you think!
LBs original post:
The seperate Price Action Channel script:
BITCOIN KILL ZONES v2Kill Zones
Kill zones are really liquidity events. Many different market participants often come together and act around these events. The activity itself may be event driven (margin calls or options exercise related activity), portfolio management driven (buy-on-close and asset allocation rebalancing orders) or institutionally driven (larger players needing liquidity to get filled in size) or a combination of any/all three. The point is, this intense cross current of activity at a very specific point in time often occurs near significant technical levels and trends established coming out of these events often persist until the next Kill Zone in approached/entered.
Specifically, there are three Kill Zones and each has its own importance/significance.
1. Asian Kill Zone (1900 - 2300 EST) Considered the "institutional" zone, this zone represents both the launch pad for new trends and also too a reloading area from the post American session. It is the start of a new day (or week) for the world and as such it makes sense this zone will often set the tone for the rest of the world's trading day. Since it is very wide (4 hours) one should pay attention to the Tokyo open (2100 EST) the Beijing open (2120 EST) and the Sydney open (0650 EST previous day).
2. London Kill Zone (0200 - 0400 EST) Considered the center of the financial universe for more than 500 years, Europe still carries a lot of influence within the banking world. Many larger players use the Euro session to establish their positions. As such, the London open often sees the most significant trend establishment activity through any given trading day. Indeed, it has been suggested 80% of all weekly trends are established through Tuesday's London Kill Zone.
3. New York Kill Zone (0830 - 1030 EST) The United States is still by far the world's largest economy and so by default New York's open carries a lot of weight and often comes with a big injection of liquidity. Indeed, most of the world's trade-able assets are priced in US dollars which gives even more significance to political and economic activity within this region. Because it comes relatively late in the globe's trading day, this Kill Zone often sees violent price swings within it's first hour leading to the time tested adage "never trust the first hour of North American trading.
Additional notes:
It has become apparent these Kill Zones are evolving over time and the course of world history. Since the end of the second world war, New York has slowly encroached on London's place as the global center for commercial banking. So much so through the later part of the 20th century New York was considered indeed, the new center of the financial universe. With the end of the cold war that leadership seems to have shifted back toward Europe and away from The United States. Additionally, Japan has slowly lost its former predominance within the global economic landscape while Beijing's has risen dramatically.
Only time will tell how these kill zones will evolve given each region's ever changing political, economic and socioeconomic influences.
Trading Notes:
If you have specific levels of interest odds are the bigger players have the same levels too. If it is indeed a solid level, look for price to trade to your level through the kill zone because the zone is a liquidity event where the bigger players can find enough size to get their big orders filled.
Try to avoid taking positions heading into Kill Zones and look for confirmation of your levels coming out of the event. For the more advanced trader, look to take positions on those level hits through the zone but understand higher time frame players often have far deeper pockets then day traders and can endure far more volatility then us little guys.
Thanks for the contribution to @CRInvestor and @ICT_MHuddleston
BAERMThe Bitcoin Auto-correlation Exchange Rate Model: A Novel Two Step Approach
THIS IS NOT FINANCIAL ADVICE. THIS ARTICLE IS FOR EDUCATIONAL AND ENTERTAINMENT PURPOSES ONLY.
If you enjoy this software and information, please consider contributing to my lightning address
Prelude
It has been previously established that the Bitcoin daily USD exchange rate series is extremely auto-correlated
In this article, we will utilise this fact to build a model for Bitcoin/USD exchange rate. But not a model for predicting the exchange rate, but rather a model to understand the fundamental reasons for the Bitcoin to have this exchange rate to begin with.
This is a model of sound money, scarcity and subjective value.
Introduction
Bitcoin, a decentralised peer to peer digital value exchange network, has experienced significant exchange rate fluctuations since its inception in 2009. In this article, we explore a two-step model that reasonably accurately captures both the fundamental drivers of Bitcoin’s value and the cyclical patterns of bull and bear markets. This model, whilst it can produce forecasts, is meant more of a way of understanding past exchange rate changes and understanding the fundamental values driving the ever increasing exchange rate. The forecasts from the model are to be considered inconclusive and speculative only.
Data preparation
To develop the BAERM, we used historical Bitcoin data from Coin Metrics, a leading provider of Bitcoin market data. The dataset includes daily USD exchange rates, block counts, and other relevant information. We pre-processed the data by performing the following steps:
Fixing date formats and setting the dataset’s time index
Generating cumulative sums for blocks and halving periods
Calculating daily rewards and total supply
Computing the log-transformed price
Step 1: Building the Base Model
To build the base model, we analysed data from the first two epochs (time periods between Bitcoin mining reward halvings) and regressed the logarithm of Bitcoin’s exchange rate on the mining reward and epoch. This base model captures the fundamental relationship between Bitcoin’s exchange rate, mining reward, and halving epoch.
where Yt represents the exchange rate at day t, Epochk is the kth epoch (for that t), and epsilont is the error term. The coefficients beta0, beta1, and beta2 are estimated using ordinary least squares regression.
Base Model Regression
We use ordinary least squares regression to estimate the coefficients for the betas in figure 2. In order to reduce the possibility of over-fitting and ensure there is sufficient out of sample for testing accuracy, the base model is only trained on the first two epochs. You will notice in the code we calculate the beta2 variable prior and call it “phaseplus”.
The code below shows the regression for the base model coefficients:
\# Run the regression
mask = df\ < 2 # we only want to use Epoch's 0 and 1 to estimate the coefficients for the base model
reg\_X = df.loc\ [mask, \ \].shift(1).iloc\
reg\_y = df.loc\ .iloc\
reg\_X = sm.add\_constant(reg\_X)
ols = sm.OLS(reg\_y, reg\_X).fit()
coefs = ols.params.values
print(coefs)
The result of this regression gives us the coefficients for the betas of the base model:
\
or in more human readable form: 0.029, 0.996869586, -0.00043. NB that for the auto-correlation/momentum beta, we did NOT round the significant figures at all. Since the momentum is so important in this model, we must use all available significant figures.
Fundamental Insights from the Base Model
Momentum effect: The term 0.997 Y suggests that the exchange rate of Bitcoin on a given day (Yi) is heavily influenced by the exchange rate on the previous day. This indicates a momentum effect, where the price of Bitcoin tends to follow its recent trend.
Momentum effect is a phenomenon observed in various financial markets, including stocks and other commodities. It implies that an asset’s price is more likely to continue moving in its current direction, either upwards or downwards, over the short term.
The momentum effect can be driven by several factors:
Behavioural biases: Investors may exhibit herding behaviour or be subject to cognitive biases such as confirmation bias, which could lead them to buy or sell assets based on recent trends, reinforcing the momentum.
Positive feedback loops: As more investors notice a trend and act on it, the trend may gain even more traction, leading to a self-reinforcing positive feedback loop. This can cause prices to continue moving in the same direction, further amplifying the momentum effect.
Technical analysis: Many traders use technical analysis to make investment decisions, which often involves studying historical exchange rate trends and chart patterns to predict future exchange rate movements. When a large number of traders follow similar strategies, their collective actions can create and reinforce exchange rate momentum.
Impact of halving events: In the Bitcoin network, new bitcoins are created as a reward to miners for validating transactions and adding new blocks to the blockchain. This reward is called the block reward, and it is halved approximately every four years, or every 210,000 blocks. This event is known as a halving.
The primary purpose of halving events is to control the supply of new bitcoins entering the market, ultimately leading to a capped supply of 21 million bitcoins. As the block reward decreases, the rate at which new bitcoins are created slows down, and this can have significant implications for the price of Bitcoin.
The term -0.0004*(50/(2^epochk) — (epochk+1)²) accounts for the impact of the halving events on the Bitcoin exchange rate. The model seems to suggest that the exchange rate of Bitcoin is influenced by a function of the number of halving events that have occurred.
Exponential decay and the decreasing impact of the halvings: The first part of this term, 50/(2^epochk), indicates that the impact of each subsequent halving event decays exponentially, implying that the influence of halving events on the Bitcoin exchange rate diminishes over time. This might be due to the decreasing marginal effect of each halving event on the overall Bitcoin supply as the block reward gets smaller and smaller.
This is antithetical to the wrong and popular stock to flow model, which suggests the opposite. Given the accuracy of the BAERM, this is yet another reason to question the S2F model, from a fundamental perspective.
The second part of the term, (epochk+1)², introduces a non-linear relationship between the halving events and the exchange rate. This non-linear aspect could reflect that the impact of halving events is not constant over time and may be influenced by various factors such as market dynamics, speculation, and changing market conditions.
The combination of these two terms is expressed by the graph of the model line (see figure 3), where it can be seen the step from each halving is decaying, and the step up from each halving event is given by a parabolic curve.
NB - The base model has been trained on the first two halving epochs and then seeded (i.e. the first lag point) with the oldest data available.
Constant term: The constant term 0.03 in the equation represents an inherent baseline level of growth in the Bitcoin exchange rate.
In any linear or linear-like model, the constant term, also known as the intercept or bias, represents the value of the dependent variable (in this case, the log-scaled Bitcoin USD exchange rate) when all the independent variables are set to zero.
The constant term indicates that even without considering the effects of the previous day’s exchange rate or halving events, there is a baseline growth in the exchange rate of Bitcoin. This baseline growth could be due to factors such as the network’s overall growth or increasing adoption, or changes in the market structure (more exchanges, changes to the regulatory environment, improved liquidity, more fiat on-ramps etc).
Base Model Regression Diagnostics
Below is a summary of the model generated by the OLS function
OLS Regression Results
\==============================================================================
Dep. Variable: logprice R-squared: 0.999
Model: OLS Adj. R-squared: 0.999
Method: Least Squares F-statistic: 2.041e+06
Date: Fri, 28 Apr 2023 Prob (F-statistic): 0.00
Time: 11:06:58 Log-Likelihood: 3001.6
No. Observations: 2182 AIC: -5997.
Df Residuals: 2179 BIC: -5980.
Df Model: 2
Covariance Type: nonrobust
\==============================================================================
coef std err t P>|t| \
\------------------------------------------------------------------------------
const 0.0292 0.009 3.081 0.002 0.011 0.048
logprice 0.9969 0.001 1012.724 0.000 0.995 0.999
phaseplus -0.0004 0.000 -2.239 0.025 -0.001 -5.3e-05
\==============================================================================
Omnibus: 674.771 Durbin-Watson: 1.901
Prob(Omnibus): 0.000 Jarque-Bera (JB): 24937.353
Skew: -0.765 Prob(JB): 0.00
Kurtosis: 19.491 Cond. No. 255.
\==============================================================================
Below we see some regression diagnostics along with the regression itself.
Diagnostics: We can see that the residuals are looking a little skewed and there is some heteroskedasticity within the residuals. The coefficient of determination, or r2 is very high, but that is to be expected given the momentum term. A better r2 is manually calculated by the sum square of the difference of the model to the untrained data. This can be achieved by the following code:
\# Calculate the out-of-sample R-squared
oos\_mask = df\ >= 2
oos\_actual = df.loc\
oos\_predicted = df.loc\
residuals\_oos = oos\_actual - oos\_predicted
SSR = np.sum(residuals\_oos \*\* 2)
SST = np.sum((oos\_actual - oos\_actual.mean()) \*\* 2)
R2\_oos = 1 - SSR/SST
print("Out-of-sample R-squared:", R2\_oos)
The result is: 0.84, which indicates a very close fit to the out of sample data for the base model, which goes some way to proving our fundamental assumption around subjective value and sound money to be accurate.
Step 2: Adding the Damping Function
Next, we incorporated a damping function to capture the cyclical nature of bull and bear markets. The optimal parameters for the damping function were determined by regressing on the residuals from the base model. The damping function enhances the model’s ability to identify and predict bull and bear cycles in the Bitcoin market. The addition of the damping function to the base model is expressed as the full model equation.
This brings me to the question — why? Why add the damping function to the base model, which is arguably already performing extremely well out of sample and providing valuable insights into the exchange rate movements of Bitcoin.
Fundamental reasoning behind the addition of a damping function:
Subjective Theory of Value: The cyclical component of the damping function, represented by the cosine function, can be thought of as capturing the periodic fluctuations in market sentiment. These fluctuations may arise from various factors, such as changes in investor risk appetite, macroeconomic conditions, or technological advancements. Mathematically, the cyclical component represents the frequency of these fluctuations, while the phase shift (α and β) allows for adjustments in the alignment of these cycles with historical data. This flexibility enables the damping function to account for the heterogeneity in market participants’ preferences and expectations, which is a key aspect of the subjective theory of value.
Time Preference and Market Cycles: The exponential decay component of the damping function, represented by the term e^(-0.0004t), can be linked to the concept of time preference and its impact on market dynamics. In financial markets, the discounting of future cash flows is a common practice, reflecting the time value of money and the inherent uncertainty of future events. The exponential decay in the damping function serves a similar purpose, diminishing the influence of past market cycles as time progresses. This decay term introduces a time-dependent weight to the cyclical component, capturing the dynamic nature of the Bitcoin market and the changing relevance of past events.
Interactions between Cyclical and Exponential Decay Components: The interplay between the cyclical and exponential decay components in the damping function captures the complex dynamics of the Bitcoin market. The damping function effectively models the attenuation of past cycles while also accounting for their periodic nature. This allows the model to adapt to changing market conditions and to provide accurate predictions even in the face of significant volatility or structural shifts.
Now we have the fundamental reasoning for the addition of the function, we can explore the actual implementation and look to other analogies for guidance —
Financial and physical analogies to the damping function:
Mathematical Aspects: The exponential decay component, e^(-0.0004t), attenuates the amplitude of the cyclical component over time. This attenuation factor is crucial in modelling the diminishing influence of past market cycles. The cyclical component, represented by the cosine function, accounts for the periodic nature of market cycles, with α determining the frequency of these cycles and β representing the phase shift. The constant term (+3) ensures that the function remains positive, which is important for practical applications, as the damping function is added to the rest of the model to obtain the final predictions.
Analogies to Existing Damping Functions: The damping function in the BAERM is similar to damped harmonic oscillators found in physics. In a damped harmonic oscillator, an object in motion experiences a restoring force proportional to its displacement from equilibrium and a damping force proportional to its velocity. The equation of motion for a damped harmonic oscillator is:
x’’(t) + 2γx’(t) + ω₀²x(t) = 0
where x(t) is the displacement, ω₀ is the natural frequency, and γ is the damping coefficient. The damping function in the BAERM shares similarities with the solution to this equation, which is typically a product of an exponential decay term and a sinusoidal term. The exponential decay term in the BAERM captures the attenuation of past market cycles, while the cosine term represents the periodic nature of these cycles.
Comparisons with Financial Models: In finance, damped oscillatory models have been applied to model interest rates, stock prices, and exchange rates. The famous Black-Scholes option pricing model, for instance, assumes that stock prices follow a geometric Brownian motion, which can exhibit oscillatory behavior under certain conditions. In fixed income markets, the Cox-Ingersoll-Ross (CIR) model for interest rates also incorporates mean reversion and stochastic volatility, leading to damped oscillatory dynamics.
By drawing on these analogies, we can better understand the technical aspects of the damping function in the BAERM and appreciate its effectiveness in modelling the complex dynamics of the Bitcoin market. The damping function captures both the periodic nature of market cycles and the attenuation of past events’ influence.
Conclusion
In this article, we explored the Bitcoin Auto-correlation Exchange Rate Model (BAERM), a novel 2-step linear regression model for understanding the Bitcoin USD exchange rate. We discussed the model’s components, their interpretations, and the fundamental insights they provide about Bitcoin exchange rate dynamics.
The BAERM’s ability to capture the fundamental properties of Bitcoin is particularly interesting. The framework underlying the model emphasises the importance of individuals’ subjective valuations and preferences in determining prices. The momentum term, which accounts for auto-correlation, is a testament to this idea, as it shows that historical price trends influence market participants’ expectations and valuations. This observation is consistent with the notion that the price of Bitcoin is determined by individuals’ preferences based on past information.
Furthermore, the BAERM incorporates the impact of Bitcoin’s supply dynamics on its price through the halving epoch terms. By acknowledging the significance of supply-side factors, the model reflects the principles of sound money. A limited supply of money, such as that of Bitcoin, maintains its value and purchasing power over time. The halving events, which reduce the block reward, play a crucial role in making Bitcoin increasingly scarce, thus reinforcing its attractiveness as a store of value and a medium of exchange.
The constant term in the model serves as the baseline for the model’s predictions and can be interpreted as an inherent value attributed to Bitcoin. This value emphasizes the significance of the underlying technology, network effects, and Bitcoin’s role as a medium of exchange, store of value, and unit of account. These aspects are all essential for a sound form of money, and the model’s ability to account for them further showcases its strength in capturing the fundamental properties of Bitcoin.
The BAERM offers a potential robust and well-founded methodology for understanding the Bitcoin USD exchange rate, taking into account the key factors that drive it from both supply and demand perspectives.
In conclusion, the Bitcoin Auto-correlation Exchange Rate Model provides a comprehensive fundamentally grounded and hopefully useful framework for understanding the Bitcoin USD exchange rate.
BTC Arcturus IndicatorBTC Arcturus Indicator: This indicator is designed to create buy and sell signals based on the market value of Bitcoin. It also predicts potential market tops with the Pi Cycle Top indicator.
How Does It Work?
1. MVRVZ (Market Value to Realized Value-Z Score) Calculation:
MC: Bitcoin's market cap (Market Cap) is pulled daily from Glassnode data.
MCR: Realized Market Cap of Bitcoin is taken daily from Coinmetrics data.
MVRVZ: It is calculated by dividing the difference between Bitcoin's market value and realized market value by one standard deviation. This value indicates whether the market is overvalued or undervalued.
2. Reception and Warning Signals:
Buy Signal: When MVRVZ falls below the -0.255 threshold value, the indicator gives a "Buy" signal. This indicates that Bitcoin is undervalued and may be a buying opportunity.
Warning Signal: A warning signal turns on when MVRVZ exceeds the threshold value of 2.765. This indicates that the market is approaching saturation and caution is warranted.
3. Tracking the Highest MVRVZ Value:
The indicator records the highest MVRVZ value in the last 10 candlesticks. This value is used to determine whether the market has reached its highest risk levels.
4. Warning Display:
If the MVRVZ value matches the highest value in the last 10 bars and this warning has not been displayed before, a "Warning" signal is displayed.
Once the warning signal is shown, no further warnings are shown for 10 candles.
5. Pi Cycle Top Indicator:
Pi Cycle Top: This indicator predicts Bitcoin tops by comparing two moving averages (350-day and 111-day). If the short-term moving average falls below the long-term moving average, this is considered a sell signal.
The indicator displays this signal with the label "Sell", indicating a potential market top.
User Guide:
Green Buy Signal: It means Bitcoin is cheap and offers a buying opportunity.
Yellow Warning Signal: Indicates that Bitcoin has reached possible profit taking points and caution should be exercised.
Red Sell Signal: Indicates that Bitcoin has reached market saturation and it may be appropriate to sell.
WAGMI LAB Trend Reversal Indicator HMA-Kahlman (m15)WAGMI HMA-Kahlman Trend Reversal Indicator
This indicator combines the Hull Moving Average (HMA) with the Kahlman filter to provide a dynamic trend reversal signal, perfect for volatile assets like Bitcoin. The strategy works particularly well on lower timeframes, making it ideal for intraday trading and fast-moving markets.
Key Features:
Trend Detection: It uses a blend of HMA and Kahlman filters to detect trend reversals, providing more accurate and timely signals.
Volatility Adaptability: Designed with volatile assets like Bitcoin in mind, this indicator adapts to rapid price movements, offering smoother trend detection during high volatility.
Easy Visualization: Buy (B) and Sell (S) signals are clearly marked with labels, helping traders spot trend shifts quickly and accurately.
Trendlines Module: The indicator plots trendlines based on pivot points, highlighting important support and resistance levels. This helps traders understand the market structure and identify potential breakout or breakdown zones.
Customizable: Adjust the HMA and Kahlman parameters to fit different assets or trading styles, making it flexible for various market conditions.
Usage Tips:
Best Timeframes: The indicator performs exceptionally well on lower timeframes (such as 15-minute to 1-hour charts), making it ideal for scalping and short-term trading strategies.
Ideal for Volatile Assets: This strategy is perfect for highly volatile assets like Bitcoin, but can also be applied to other cryptocurrencies and traditional markets with high price fluctuations.
Signal Confirmation: Use the trend signals (green for uptrend, red for downtrend) along with the buy/sell labels to help you confirm potential entries and exits. It's also recommended to combine the signals with other technical tools like volume analysis or RSI for enhanced confirmation.
Trendline Analysis: The plotted trendlines provide additional visual context to identify key market zones, supporting your trading decisions with a clear view of ongoing trends and possible reversal areas.
Risk Management: As with any strategy, always consider proper risk management techniques, such as stop-loss and take-profit levels, to protect against unforeseen market moves.
Crypto MVRV ZScore - Strategy [PresentTrading]█ Introduction and How it is Different
The "Crypto Valuation Extremes: MVRV ZScore - Strategy " represents a cutting-edge approach to cryptocurrency trading, leveraging the Market Value to Realized Value (MVRV) Z-Score. This metric is pivotal for identifying overvalued or undervalued conditions in the crypto market, particularly Bitcoin. It assesses the current market valuation against the realized capitalization, providing insights that are not apparent through conventional analysis.
BTCUSD 6h Long/Short Performance
Local
█ Strategy, How It Works: Detailed Explanation
The strategy leverages the Market Value to Realized Value (MVRV) Z-Score, specifically designed for cryptocurrencies, with a focus on Bitcoin. This metric is crucial for determining whether Bitcoin is currently undervalued or overvalued compared to its historical 'realized' price. Below is an in-depth explanation of the strategy's components and calculations.
🔶Conceptual Foundation
- Market Capitalization (MC): This represents the total dollar market value of Bitcoin's circulating supply. It is calculated as the current price of Bitcoin multiplied by the number of coins in circulation.
- Realized Capitalization (RC): Unlike MC, which values all coins at the current market price, RC is computed by valuing each coin at the price it was last moved or traded. Essentially, it is a summation of the value of all bitcoins, priced at the time they were last transacted.
- MVRV Ratio: This ratio is derived by dividing the Market Capitalization by the Realized Capitalization (The ratio of MC to RC (MVRV Ratio = MC / RC)). A ratio greater than 1 indicates that the current price is higher than the average price at which all bitcoins were purchased, suggesting potential overvaluation. Conversely, a ratio below 1 suggests undervaluation.
🔶 MVRV Z-Score Calculation
The Z-Score is a statistical measure that indicates the number of standard deviations an element is from the mean. For this strategy, the MVRV Z-Score is calculated as follows:
MVRV Z-Score = (MC - RC) / Standard Deviation of (MC - RC)
This formula quantifies Bitcoin's deviation from its 'normal' valuation range, offering insights into market sentiment and potential price reversals.
🔶 Spread Z-Score for Trading Signals
The strategy refines this approach by calculating a 'spread Z-Score', which adjusts the MVRV Z-Score over a specific period (default: 252 days). This is done to smooth out short-term market volatility and focus on longer-term valuation trends. The spread Z-Score is calculated as follows:
Spread Z-Score = (Market Z-Score - MVVR Ratio - SMA of Spread) / Standard Deviation of Spread
Where:
- SMA of Spread is the simple moving average of the spread over the specified period.
- Spread refers to the difference between the Market Z-Score and the MVRV Ratio.
🔶 Trading Signals
- Long Entry Condition: A long (buy) signal is generated when the spread Z-Score crosses above the long entry threshold, indicating that Bitcoin is potentially undervalued.
- Short Entry Condition: A short (sell) signal is triggered when the spread Z-Score falls below the short entry threshold, suggesting overvaluation.
These conditions are based on the premise that extreme deviations from the mean (as indicated by the Z-Score) are likely to revert to the mean over time, presenting opportunities for strategic entry and exit points.
█ Practical Application
Traders use these signals to make informed decisions about opening or closing positions in the Bitcoin market. By quantifying market valuation extremes, the strategy aims to capitalize on the cyclical nature of price movements, identifying high-probability entry and exit points based on historical valuation norms.
█ Trade Direction
A unique feature of this strategy is its configurable trade direction. Users can specify their preference for engaging in long positions, short positions, or both. This flexibility allows traders to tailor the strategy according to their risk tolerance, market outlook, or trading style, making it adaptable to various market conditions and trader objectives.
█ Usage
To implement this strategy, traders should first adjust the input parameters to align with their trading preferences and risk management practices. These parameters include the trade direction, Z-Score calculation period, and the thresholds for long and short entries. Once configured, the strategy automatically generates trading signals based on the calculated spread Z-Score, providing clear indications for potential entry and exit points.
It is advisable for traders to backtest the strategy under different market conditions to validate its effectiveness and adjust the settings as necessary. Continuous monitoring and adjustment are crucial, as market dynamics evolve over time.
█ Default Settings
- Trade Direction: Both (Allows for both long and short positions)
- Z-Score Calculation Period: 252 days (Approximately one trading year, capturing a comprehensive market cycle)
- Long Entry Threshold: 0.382 (Indicative of moderate undervaluation)
- Short Entry Threshold: -0.382 (Signifies moderate overvaluation)
These default settings are designed to balance sensitivity to market valuation extremes with a pragmatic approach to trade execution. They aim to filter out noise and focus on significant market movements, providing a solid foundation for both new and experienced traders looking to exploit the unique insights offered by the MVRV Z-Score in the cryptocurrency market.
BTC hash rate oscillatorOVERVIEW:
This script looks to identify entry point opportunities when moving averages over Bitcoin's hash rate are indicative of Miner capitulation. The script implements an oscillator based on Charles Capriole's "Hash Ribbons & Bitcoin Bottoms" concept. It analyses the short-term and long-term moving averages of Bitcoin's hash rate and then identifies potential entry opportunities from this.
KEY FEATURES:
Signal Generation: The script identifies entry points when the short-term moving average crosses under the long-term moving average and the rate of change falls below a specified threshold. These conditions suggest potential trading opportunities.
Historical Signals: Optionally the script displays historical signals, indicating past instances where hash rate conditions suggested favourable entry points. Users can also assess the script's historical performance.
USAGE:
The generated opportunities can be used as potential entry points for BTC. The script provides visual cues on the chart (blue labels above the miner capitulation zones) for identification of signals. Customisable moving average lengths and threshold values are supported, which allow adaptation to various strategies.
CONSIDERATIONS:
Validation: It's recommended that careful backtesting over historical data be done before acting on any identified opportunities.
User Discretion: Trading decisions should not rely solely on this script. Users should exercise their judgment and consider market conditions.
Note: This script identifies opportunities based on historical data and should be used with caution, as past performance is not indicative of future results.
RSI Season Tracker by KrisWatersSeason tracker identifies the momentum with tracking dominance and market capitalization data of Bitcoin , Altcoin and USDT .
With this indicator you can able to get know about Bitcoin, Altcoin and USDT dominance and market capitalization easily.
Backgrond Color Definations:
If the background color of indicator is ORANGE means that Bitcoin dominance is the most powerful. It is a good sign for Bitcoin.
If the background color of indicator is BLUE means that Altcoin dominance is the most powerful. It is a good sign for Altcoins.
If the background color of indicator is GREEN means that USDT dominance is the most powerful. It is a good sign for USDT.
Plus Sign (+) Meaning and Color Definations:
If ORANGE plus sign is visible on indicator means that market capitalization of Bitcoin is increasing. It is a good sign for Bitcoin.
If BLUE plus sign is visible on indicator means that market capitalization of Altcoin is increasing. It is a good sign for Altcoins.
If GREEN plus sign is visible on indicator means that market capitalization of Bitcoin and Altcoins are decreasing. It is not a good sign for both.
Light and Dark RSI Line Color Definations:
If the line of Bitcoin color is Orange means that, RSI value was increased compared with previous bar. It is one of the good sign of the momentum strength.
If the line of Bitcoin color is Yellow means that, RSI value was decreased compared with previous bar. It is not a good sign.
If the line of Altcoin color is Navy means that, RSI value was increased compared with previous bar. It is one of the good sign of the momentum strength.
If the line of Altcoin color is Blue means that, RSI value was decreased compared with previous bar. It is not a good sign.
If the line of USDT color is Green means that, RSI value was increased compared with previous bar. It is one of good the sign of the momentum strength.
If the line of USDT color is Lime means that, RSI value was decreased compared with previous bar. It is not a good sign.
Simple RSI rules are valid for this indicator.
RSI value is under 30 indicates that asset is OVERSOLD .
RSI value is upper 70 indicates that asset is OVERBOUGHT .
Important Notice: Dominance data is not correlated with price. For example; sometimes dominance is increasing but the price is not. And that's where the market capitalization comes in.
If the plus sign is not visible , you can get questioning for the movement. It is not a good sign at all.
If the plus sign is visible and RSI is increasing , It is a good sign for the strength of assets movement.
I recommended to use on DAILY timeframe . But you can use different timeframes as well.
Please leave comment below If you have any question or development idea.
2024 - Median High-Low % Change - Monthly, Weekly, DailyDescription:
This indicator provides a statistical overview of Bitcoin's volatility by displaying the median high-to-low percentage changes for monthly, weekly, and daily timeframes. It allows traders to visualize typical price fluctuations within each period, supporting range and volatility-based trading strategies.
How It Works:
Calculation of High-Low % Change: For each selected timeframe (monthly, weekly, and daily), the script calculates the percentage change from the high to the low price within the period.
Median Calculation: The median of these high-to-low changes is determined for each timeframe, offering a robust central measure that minimizes the impact of extreme price swings.
Table Display: At the end of the chart, the script displays a table in the top-right corner with the median values for each selected timeframe. This table is updated dynamically to show the latest data.
Usage Notes:
This script includes input options to toggle the visibility of each timeframe (monthly, weekly, and daily) in the table.
Designed to be used with Bitcoin on daily and higher timeframes for accurate statistical insights.
Ideal for traders looking to understand Bitcoin's typical volatility and adjust their strategies accordingly.
This indicator does not provide specific buy or sell signals but serves as an analytical tool for understanding volatility patterns.
E9 PLRRThe E9 PLRR (Power Law Residual Ratio) is a custom-built indicator designed to evaluate the overvaluation or undervaluation of an asset, specifically by utilizing logarithmic price data and a power law-based model. It leverages a dynamic regression technique to assess the deviation of the current price from its expected value, giving insights into how much the price deviates from its long-term trend.
This indicator is primarily used to detect market extremes and cycles, often used in the analysis of long-term price movements in assets like Bitcoin, where cyclical behavior and significant price deviations are common.
This chart is back from 2019 and shows (From left to right) 2018 Bear market bottom at $3.5k (Dark Blue) , following a peak at 12k (dark red) before the Covid crash back down to EUROTLX:4K (Dark blue)
Key Components
Logarithmic Price Data:
The indicator works with logarithmic price data (ohlc4), which represents the average of open, high, low, and close prices. The logarithmic transformation is crucial in financial modeling, especially when analyzing long-term price data, as it normalizes exponential price growth patterns.
Dynamic Exponent 𝑘:
The model calculates a dynamic exponent k using regression, which defines the power law relationship between time and price. This exponent is essential in determining the expected power law price return and how far the current price deviates from that expected trend.
Power Law Price Return:
The power law price return is computed using the dynamic exponent
k over a defined period, such as 365 days (1 year). It represents the theoretical price return based on a power law relationship, which is used to compare against the actual logarithmic price data.
Risk-Free Rate:
The indicator incorporates an adjustable risk-free rate, allowing users to model the opportunity cost of holding an asset compared to risk-free alternatives. By default, the risk-free rate is set to 0%, but this can be modified depending on the user's requirements.
Volatility Adjustment:
A key feature of the PLRR is its ability to adjust for price volatility. The indicator smooths out short-term price fluctuations using a moving average, helping to detect longer-term cycles and trends.
PLRR Calculation:
The core of the indicator is the calculation of the Power Law Residual Ratio (PLRR). This is derived by subtracting the expected power law price return and risk-free rate from the logarithmic price return, then multiplying the result by a user-defined multiplier.
Color Gradient:
The PLRR values are represented visually using a color gradient. This gradient helps the user quickly identify whether the asset is in an undervalued, fair value, or overvalued state:
Dark Blue to Light Blue: Indicates undervaluation, with increasing blue tones representing a higher degree of undervaluation.
Green to Yellow: Represents fair value, where the price is aligned with the expected power law return.
Orange to Dark Red: Indicates overvaluation, with increasing red tones representing a higher degree of overvaluation.
Zero Line:
A zero line is plotted on the indicator chart, serving as a reference point. Values above the zero line suggest potential overvaluation, while values below indicate potential undervaluation.
Dots Visualization:
The PLRR is plotted using dots, with each dot color-coded based on the PLRR value. This dot-based visualization makes it easier to spot significant changes or reversals in market sentiment without overwhelming the user with continuous lines.
Bar Coloring:
The chart’s bars are colored in accordance with the PLRR value at each point in time, making it visually clear when an asset is potentially overvalued or undervalued.
Indicator Functionality
Cycle Identification : The E9 PLRR is especially useful for identifying cyclical market behavior. In assets like Bitcoin, which are known for their boom-bust cycles, the PLRR can help pinpoint when the market is likely entering a peak (overvaluation) or a trough (undervaluation).
Overvaluation and Undervaluation Detection: By comparing the current price to its expected power law return, the PLRR helps traders assess whether an asset is trading above or below its fair value. This is critical for long-term investors seeking to enter the market at undervalued levels and exit during periods of overvaluation.
Trend Following: The indicator helps users identify the broader trend by smoothing out short-term volatility. This makes it useful for both momentum traders looking to ride trends and contrarian traders seeking to capitalize on market extremes.
Customization
The E9 PLRR allows users to fine-tune several parameters based on their preferences or specific market conditions:
Lookback Period:
The user can adjust the lookback period (default: 100) to modify how the moving average and regression are calculated.
Risk-Free Rate:
Adjusting the risk-free rate allows for more realistic modeling of the opportunity cost of holding the asset.
Multiplier:
The multiplier (default: 5.688) amplifies the sensitivity of the PLRR, allowing users to adjust how aggressively the indicator responds to price movements.
This indicator was inspired by the works of Ashwin & PlanG and their work around powerLaw. Thank you. I hall be working on the calculation of this indicator moving forward to make improvements and optomisations.
MVRV Z Score and MVRV Free Float Z-ScoreIMPORTANT: This script needs as much historic data as possible. Please run it on INDEX:BTCUSD , BNC:BLX or another chart of sufficient length.
MVRV
The MVRV (Market Value to Realised Value Ratio) simply divides bitcoins market cap by bitcoins realized market cap. This was previously impossible on Tradingview but has now been made possible thanks to Coinmetrics providing us with the realized market cap data.
In the free float version, the free float market cap is used instead of the regular market cap.
Z-Score
The MVRV Z-score divides the difference between Market cap and realized market cap by the historic standard deviation of the market cap.
Historically, this has been insanely accurate at detecting bitcoin tops and bottoms:
A Z-Score above 7 means bitcoin is vastly overpriced and at a local top.
A Z-Score below 0.1 means bitcoin is underpriced and at a local bottom.
In the free float version, the free float market cap is used instead of the regular market cap.
The Z-Score, also known as the standard score is hugely popular in a wide range of mathematical and statistical fields and is usually used to measure the number of standard deviations by which the value of a raw score is above or below the mean value of what is being observed or measured.
Credits
MVRV Z Score initially created by aweandwonder
MVRV initially created by Murad Mahmudov and David Puell
Blockchain Fundamentals - Active Address Sentiment Osc. [CR]Blockchain Fundamentals: Active Address Sentiment Oscillator AASO
Back with another script today, this one is a useful tool in helping to determine bitcoins value. We are looking at 2 data sources: the daily active addresses on the BTC blockchain, and the daily returns of BTC.
THIS INDICATOR WILL ONLY GIVE YOU THE CORRECT RESULTS ON THE DAILY TIMEFRAME
There is an interesting relationship that you can see by comparing the two timeseries. But for us to create a good indicator we first need to normalize the data. So we look at the percent change over the past 28 days for each metric (DAA and price).
THIS INDICATOR WILL ONLY GIVE YOU THE CORRECT RESULTS ON THE DAILY TIMEFRAME
We then calculate standard deviation bands around the DAA metric. We finalize them by averaging the bands over a 28 day period.
When the Price series (yellow line) is higher than the SD bands BTC is considered overvalued or price is overheated. A pullback could be expected soon. When the Price series is below the SD bands BTC is considered undervalued or price is oversold.
THIS INDICATOR WILL ONLY GIVE YOU THE CORRECT RESULTS ON THE DAILY TIMEFRAME
This tool doesnt give signals on the one minute chart or tell you exactly when to buy or sell. BUT what it does do is act as a convenient macro sentiment indicator that is not based completely upon price.
In an attempt to narrow down the really juicy areas, if you seen the background color highlights with white, that means its likely a top or bottom. At the very least on a local sense and many times in a cyclical macro sense as well. It also narrows down the signal to a generally more profitable area.
This indicator is not meant to be used on timeframes other than daily (did I mention that already?). I am lazy and did not code the calculations to be MTF (which is why you have to use on the daily chart). If you want to code this, please forward it on to me and I will post an update with a heartfelt credit to you.
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:
============================================
Final Equation:
■ Model Price = Exp(- 12.214) * BSF ^ 3.3297
Where BSF is Bitcoin's stock-to-flow
============================================
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.
The Insider - Hunt Bitcoin CoT DeltaThe Insider - Hunt Bitcoin CoT Delta
The gift of the Squeeze in the Largest 4 open Interest Shorts vs Longs.
Why Bother another CoT signal?
Its different & focused on the Insider's.
Performance -
This Indicator provided a
1. Signal 1 = 26th March 2019 = SUPER LONG at $4,500 that saw a near $14,000 run up
2. Signal 2 = 18th & 24th June 2019 = SHORT at the second & final level $11,700 after repeated attempts & failure in the $13K range, the mini Echo Bitcoin Bull of 2019
3. Signal 3 = 17th December 2019 = LONG $6,900, Bitcoin rallied to Mid $10,500's
4. Signal 4 = 18th Feb 2020 = SUPER SHORT from $9,700's to a final extreme Low of $3,000, calling the CV-19 collapse
5. Signal 5 = 17th March 2020 = LONG from $5,400 no closure point yet
6. Signal 6 = 29th June 2020 = SUPER LONG reiterate from $10,700 no closure sell signal yet
7. Signal 7 = 17th May 2020 = LONG another accumulate LONG with no sell signal yet generated at Post H&S's low of $33,000
Note - This indicator only commences March 2019, as Bitcoin futures were a recent introduction and needed to settle for 6 months in both use and data, no signals were meaningful prior & data was light.
What is Provided. - Please note the need to also add the Hunt Bitcoin Historical Volatility Indicator for full understanding.
We provide 3 things with the 3 indicators.
'Insider' indications from Largest players in the futures market.
1. Bitcoin Macro Buy Signals.
a) The Bitcoin Commitment of Traders results see us focus solely on Largest 4 Short Open Interest & Largest 4 Long Open Interest aspects of the CoT Release data.
When the difference - is tight, a kind of pinch, these have been great Buy signals in Bitcoin.
We call this difference the Delta & When Delta is 5% or less Bitcoin is a Buy.
2. Bitcoin Macro Sells.
a) A sell signal is Triggered in Bitcoin at any point the Largest 4 short OI > or = to 70
3. AMPLIFIER Trade signals 'Super' Longs or Shorts -
Extreme low volatility events leads to highly impulsive & volatile subsequent moves, if either of 1 or 2 above occur, combined with extreme low volatility
a 'Super Long' or 'SUPER SELL' is generated. In the case of the short side, given Bitcoins general expansive and MACRO Bull trend since inception, we seek an additional component
that is an extreme differential/Delta reading between 4 biggest Longs & Shorts OI.
Namely CoT Delta also must be > 47.5%
We also have a Cautionary level, where it is not necessarily a good idea to accumulate Bitcon, as a better opportunity lower may avail itself, see conditions below.
So the required logic explicitly stated below for all Signals.
1. Long - Hunt Bitcoin CoT Delta < or = 5
2. SUPER Long - Hunt Bitcoin CoT Delta < or = 5; and 2 Day Historical Bitcoin Volatility = or < 20
3. Short - Largest 4 Sellers OI = or > 70
4. SUPER Short - Largest 4 Sellers OI = or > 70; AND..
Hunt Bitcoin CoT Delta = or > 47.5 AND 2 Day Historical BTC Volatility = or < 20
5. Caution - Largest 4 Sellers OI = or > 67.5 AND Hunt Bitcoin CoT Delta = or > 45
WARNING SEE Notes Below
Note 1 - = Largest 4 Open Interest Shorts
Note 2 - = Largest 4 Open Interest Longs
Note 3 - = Hunt Cot Delta = (Largest 4 sellers OI) -( Largest 4 Buyers OI)
Caution = Avoid new Bitcoin Accumulation Right Now, A sell signal might follow Enter on next Long
Note 4 - The Hunt Bitcoin COT Delta signal is a Largest 'Insider' Tracking tool based on a segment of Commitment of Traders data on Bitcoin Futures, released once a week on a Friday.
It is a Macro Timeframe signal , and should not be used for Day trading and Short Timeframe analysis , Entries may be optimised after a Hunt Bitcoin CoT Signal is generated by separate shorter Timeframe analysis.
Note 5 - The Historical Bitcoin Volatility is an additional 'Amplifier' component to the 'Hunt Bitcoin Cot Delta' Insider Signal
Note 6 - The Historical Bitcoin Volatility criteria varies by timeframe, the above levels are those applying on a Two Day TF Chart, select this custom timeframe in Trading View.
if additional criteria are met for LONG & SHORT insider signals, they may become 'Super Longs/Shorts', see conditions box above.
MVRV Ratio [Alpha Extract]The MVRV Ratio Indicator provides valuable insights into Bitcoin market cycles by tracking the relationship between market value and realized value. This powerful on-chain metric helps traders identify potential market tops and bottoms, offering clear buy and sell signals based on historical patterns of Bitcoin valuation.
🔶 CALCULATION The indicator processes MVRV ratio data through several analytical methods:
Raw MVRV Data: Collects MVRV data directly from INTOTHEBLOCK for Bitcoin
Optional Smoothing: Applies simple moving average (SMA) to reduce noise
Status Classification: Categorizes market conditions into four distinct states
Signal Generation: Produces trading signals based on MVRV thresholds
Price Estimation: Calculates estimated realized price (Current price / MVRV ratio)
Historical Context: Compares current values to historical extremes
Formula:
MVRV Ratio = Market Value / Realized Value
Smoothed MVRV = SMA(MVRV Ratio, Smoothing Length)
Estimated Realized Price = Current Price / MVRV Ratio
Distance to Top = ((3.5 / MVRV Ratio) - 1) * 100
Distance to Bottom = ((MVRV Ratio / 0.8) - 1) * 100
🔶 DETAILS Visual Features:
MVRV Plot: Color-coded line showing current MVRV value (red for overvalued, orange for moderately overvalued, blue for fair value, teal for undervalued)
Reference Levels: Horizontal lines indicating key MVRV thresholds (3.5, 2.5, 1.0, 0.8)
Zone Highlighting: Background color changes to highlight extreme market conditions (red for potentially overvalued, blue for potentially undervalued)
Information Table: Comprehensive dashboard showing current MVRV value, market status, trading signal, price information, and historical context
Interpretation:
MVRV ≥ 3.5: Potential market top, strong sell signal
MVRV ≥ 2.5: Overvalued market, consider selling
MVRV 1.5-2.5: Neutral market conditions
MVRV 1.0-1.5: Fair value, consider buying
MVRV < 1.0: Potential market bottom, strong buy signal
🔶 EXAMPLES
Market Top Identification: When MVRV ratio exceeds 3.5, the indicator signals potential market tops, highlighting periods where Bitcoin may be significantly overvalued.
Example: During bull market peaks, MVRV exceeding 3.5 has historically preceded major corrections, helping traders time their exits.
Bottom Detection: MVRV values below 1.0, especially approaching 0.8, have historically marked excellent buying opportunities.
Example: During bear market bottoms, MVRV falling below 1.0 has identified the most profitable entry points for long-term Bitcoin accumulation.
Tracking Market Cycles: The indicator provides a clear visualization of Bitcoin's market cycles from undervalued to overvalued states.
Example: Following the progression of MVRV from below 1.0 through fair value and eventually to overvalued territory helps traders position themselves appropriately throughout Bitcoin's market cycle.
Realized Price Support: The estimated realized price often acts as a significant
support/resistance level during market transitions.
Example: During corrections, price often finds support near the realized price level calculated by the indicator, providing potential entry points.
🔶 SETTINGS
Customization Options:
Smoothing: Toggle smoothing option and adjust smoothing length (1-50)
Table Display: Show/hide the information table
Table Position: Choose between top right, top left, bottom right, or bottom left positions
Visual Elements: All plots, lines, and background highlights can be customized for color and style
The MVRV Ratio Indicator provides traders with a powerful on-chain metric to identify potential market tops and bottoms in Bitcoin. By tracking the relationship between market value and realized value, this indicator helps identify periods of overvaluation and undervaluation, offering clear buy and sell signals based on historical patterns. The comprehensive information table delivers valuable context about current market conditions, helping traders make more informed decisions about market positioning throughout Bitcoin's cyclical patterns.
Rolling Correlation with Bitcoin V1.1 [ADRIDEM]Overview
The Rolling Correlation with Bitcoin script is designed to offer a comprehensive view of the correlation between the selected ticker and Bitcoin. This script helps investors understand the relationship between the performance of the current ticker and Bitcoin over a rolling period, providing insights into their interconnected behavior. Below is a detailed presentation of the script and its unique features.
Unique Features of the New Script
Bitcoin Comparison : Allows users to compare the correlation of the current ticker with Bitcoin, providing an analysis of their relationship.
Customizable Rolling Window : Enables users to set the length for the rolling window, adapting to different market conditions and timeframes. The default value is 252 bars, which approximates one year of trading days, but it can be adjusted as needed.
Smoothing Option : Includes an option to apply a smoothing simple moving average (SMA) to the correlation coefficient, helping to reduce noise and highlight trends. The smoothing length is customizable, with a default value of 4 bars.
Visual Indicators : Plots the smoothed correlation coefficient between the current ticker and Bitcoin, with distinct colors for easy interpretation. Additionally, horizontal lines help identify key levels of correlation.
Dynamic Background Color : Adds dynamic background colors to highlight areas of strong positive and negative correlations, enhancing visual clarity.
Originality and Usefulness
This script uniquely combines the analysis of rolling correlation for a current ticker with Bitcoin, providing a comparative view of their relationship. The inclusion of a customizable rolling window and smoothing option enhances its adaptability and usefulness in various market conditions.
Signal Description
The script includes several features that highlight potential insights into the correlation between the assets:
Rolling Correlation with Bitcoin : Plotted as a red line, this represents the smoothed rolling correlation coefficient between the current ticker and Bitcoin.
Horizontal Lines and Background Color : Lines at -0.5, 0, and 0.5 help to quickly identify regions of strong negative, weak, and strong positive correlations.
These features assist in identifying the strength and direction of the relationship between the current ticker and Bitcoin.
Detailed Description
Input Variables
Length for Rolling Window (`length`) : Defines the range for calculating the rolling correlation coefficient. Default is 252.
Smoothing Length (`smoothing_length`) : The number of periods for the smoothing SMA. Default is 4.
Bitcoin Ticker (`bitcoin_ticker`) : The ticker symbol for Bitcoin. Default is "BINANCE:BTCUSDT".
Functionality
Correlation Calculation : The script calculates the daily returns for both Bitcoin and the current ticker and computes their rolling correlation coefficient.
```pine
bitcoin_close = request.security(bitcoin_ticker, timeframe.period, close)
bitcoin_dailyReturn = ta.change(bitcoin_close) / bitcoin_close
current_dailyReturn = ta.change(close) / close
rolling_correlation = ta.correlation(current_dailyReturn, bitcoin_dailyReturn, length)
```
Smoothing : A simple moving average is applied to the rolling correlation coefficient to smooth the data.
```pine
smoothed_correlation = ta.sma(rolling_correlation, smoothing_length)
```
Plotting : The script plots the smoothed rolling correlation coefficient and includes horizontal lines for key levels.
```pine
plot(smoothed_correlation, title="Rolling Correlation with Bitcoin", color=color.rgb(255, 82, 82, 50), linewidth=2)
h_neg1 = hline(-1, "-1 Line", color=color.gray)
h_neg05 = hline(-0.5, "-0.5 Line", color=color.red)
h0 = hline(0, "Zero Line", color=color.gray)
h_pos05 = hline(0.5, "0.5 Line", color=color.green)
h1 = hline(1, "1 Line", color=color.gray)
fill(h_neg1, h_neg05, color=color.rgb(255, 0, 0, 90), title="Strong Negative Correlation Background")
fill(h_neg05, h0, color=color.rgb(255, 165, 0, 90), title="Weak Negative Correlation Background")
fill(h0, h_pos05, color=color.rgb(255, 255, 0, 90), title="Weak Positive Correlation Background")
fill(h_pos05, h1, color=color.rgb(0, 255, 0, 90), title="Strong Positive Correlation Background")
```
How to Use
Configuring Inputs : Adjust the rolling window length and smoothing length as needed. Ensure the Bitcoin ticker is set to the desired asset for comparison.
Interpreting the Indicator : Use the plotted correlation coefficient and horizontal lines to assess the strength and direction of the relationship between the current ticker and Bitcoin.
Signal Confirmation : Look for periods of strong positive or negative correlation to identify potential co-movements or divergences. The background colors help to highlight these key levels.
This script provides a detailed comparative view of the correlation between the current ticker and Bitcoin, aiding in more informed decision-making by highlighting the strength and direction of their relationship.
Blockunity Miners Synthesis (BMS)Track the status of Bitcoin and Ethereum Miners' Netflows and their asset reserves.
The Idea
The goal is to provide a simple tool for visualizing the changes in miners' flows and reserves.
How to Use
Analysing the behaviour of miners enables you to detect long-term opportunities, in particular with the state of reserves, but also in the shorter term with the visualization of Netflows.
Elements
Miners Reserves
Miners Reserves represent the balances of addresses belonging to mining pools (in BTC or ETH).
This data can also be displayed in USD via the indicator parameters:
Miners Netflow
The Netflow is calculated by subtracting the outflows from the inflows originating from addresses associated with mining pools. When this result is negative, it indicates that more funds are exiting the miners' accounts than the funds they are receiving. Consequently, negative miner netflows suggests selling activity.
This data can also be displayed in USD via the indicator parameters. You can also choose the timeframe. For example, selecting "Yearly" will give a Netflow daily average taking into account the last 365 days:
Settings
In the settings, you can first choose which asset to view, between Bitcoin and Ethereum. Here are the reserves of Ethereum miners:
As with Bitcoin, Netflow can also be displayed in the timeframe of your choice. Here you can see the average daily netflow of Ethereum miners in USD over the last 30 days:
Here are all the parameters:
Asset Selector: Choose between Bitcoin or Ethereum miner data.
Get values in USD: Displays values in USD instead of assets.
Switch between Netflow and Reserve : If checked, displays Miners' Reserves data. If unchecked, displays Miners' Netflow data.
Display timeframe: Allows you to select the timeframe for displaying the Netflow plot.
Period Lookback (in days): Select the period to be taken into account when calculating the variation percentage of Miners' Reserves.
Lastly, you can modify all table and labels parameters.
Blockchain Fundamentals: 200 Week MA Heatmap [CR]Blockchain Fundamentals: 200 Week MA Heatmap
This is released as a thank you to all my followers who pushed me over the 600 follower mark on twitter. Thanks to all you Kingz and Queenz out there who made it happen. <3
Indicator Overview
In each of its major market cycles, Bitcoin's price historically bottoms out around the 200 week moving average.
This indicator uses a color heatmap based on the % increases of that 200 week moving average. Depending on the rolling cumulative 4 week percent delta of the 200 week moving average, a color is assigned to the price chart. This method clearly highlights the market cycles of bitcoin and can be extremely helpful to use in your forecasts.
How It Can Be Used
The long term Bitcoin investor can monitor the monthly color changes. Historically, when we see orange and red dots assigned to the price chart, this has been a good time to sell Bitcoin as the market overheats. Periods where the price dots are purple and close to the 200 week MA have historically been good times to buy.
Bitcoin Price Prediction Using This Tool
If you are looking to predict the price of Bitcoin or forecast where it may go in the future, the 200WMA heatmap can be a useful tool as it shows on a historical basis whether the current price is overextending (red dots) and may need to cool down. It can also show when Bitcoin price may be good value on a historical basis. This can be when the dots on the chart are purple or blue.
Over more than ten years, $BTC has spent very little time below the 200 week moving average which is also worth noting when thinking about price predictions for Bitcoin or a Bitcoin price forecast.
Notes
1.) If you do not want to view the legend do the following: Indicator options > Style tab > Uncheck "Tables"
2.) I use my custom function to get around the limited historical data for bitcoin. You can check out the explanation of it here:
