OBV 1min Volume SqueezeIn the vast realm of trading strategies, few terms evoke as much intrigue as the word "squeeze." It conjures images of pent-up energy, ready to burst forth in a sudden and decisive move. In this blog post, we'll delve into a new trading idea titled the "OBV 1-Minute Volume Squeeze" which aims to catch bigger market movements by fetching 1 minute OBV data on higher time charts.
The Essence of Squeeze
In trading parlance, a "squeeze" typically denotes a scenario where volatility contracts, and prices consolidate within a narrow range. Translating this concept to volume dynamics, a "volume squeeze" suggests a period of compressed volume activity. It is unclear if the Bulls or the Bears are at winning hand and price is thus consolidating. The script calculates buying and selling pressure by fetching 1 min data. The total volume presure is the sum of absolute values of the buying and selling pressure added up. By deviding the Buying volume by the total volume we know the Buying Pressure.
The trading theory suggest that when the buying pressure exceeds a certain value eg. 50% (default value in the script is 55%) it is likely the trend will continue to go up for a longer period of time. Vice Versa when selling pressure is higher, the trend is likely to continue down. In the script you can adjust the sensitivity in such way a higher "Volume Pressure %" result in less trading signals.
Fetching 1 min data
The OBV is a wonderful indicator to measure the buying and selling pressure. A disadvantage of the script is that the total volume pressure is presented as a positive (buying) or negative value (selling) value in the Oscillator. It does not offset the Bulls power against the Bears power at given time. The script aims to do measure the directional volume power by defining a volume pressure % (oulier value) by fetching 1 min OBV data on higher time frame charts comparing the Bulls power against the Bears Power. The code is included below:
// Fetch Lower Timeframe Data in an array
// nV = ZeroValue, sV = Selling Volume, bV = Buying Volume, tV = Total Volume
= request.security_lower_tf(syminfo.tickerid, '1', )
sum_bV_Lengthbars = array.sum(bV)
sum_sV_Lengthbars = array.sum(sV)
sum_tV_Lengthbars = sum_bV_Lengthbars + sum_sV_Lengthbars // Combine buying and selling volumes to get total volume
// Calculate buying and selling volume as percentage of the total volume, but ensure the denominator isn't zero.
buying_percentage = sum_tV_Lengthbars != 0 ? sum_bV_Lengthbars / sum_tV_Lengthbars * 100 : na
selling_percentage = sum_tV_Lengthbars != 0 ? -(sum_sV_Lengthbars / sum_tV_Lengthbars * 100) : na
OBV Oscillator Explanation
The On Balance Volume (OBV) indicator is a technical analysis tool used to measure buying and selling pressure in the market. It does this by keeping a running total of volume flows. OBV is typically calculated by adding the volume on a candle when the price closes higher than the previous candle's close and subtracting the volume on candles when the price closes lower than the previous candles close. If the price closes unchanged from the previous candle, the volume is not added to or subtracted from the OBV. The OBV can be presented as an oscillator. Positve value is the buying pressure and negative values is the selling pressure. In the settings the OBV is calculated based on 1 min data and comes with the following input options for visualization on the chart:
Higher Time Frame Settings (make sure the HTF is higher than the chart you have open)
Type of MA being: EMA, DEMA, TEMA, SMA, WMA, HMA, McGinley
Volume Pressure % (outlier value)
Length of number of bars (of the choosen HTF settings)
Smoothing of number candles of hte opened timechart. Note that higher number of bars to smoothen the indicator results in less signals, but lag of the indicator increases.
The Oscilator contains 3 main lines which are used to determin the entry signals:
Orange Line = the Outlier value in settings described as "Volume Pressure %"
Green Line = Total Buying Pressure OBV
Red Line = Total Selling Pressure OBV
If the Green or Red line is in between the zero line and the orange line the volume is squeezed and waiting for a directional break out.
If the Green line crosses over the orange line the buying pressure is > 55% and triggers a long entry position (green dot). If the Red line crosses under the orange line the selling pressure is > 55% and triggers an short entry (red dot). In the strategy settings this option is called: "Wait for total volume to increase?".
Alternative Strategy Options
In order to play around with different settings users can opt for two more strategy entry settings, called:
"Wait for total volume to deacrease?" --> Only gives a signal when total volume is declining, but buying or selling pressure maintains and crosses % threshold.
"Wait for Pull Back?" --> After a pullback occured and opposite buy/sell pressure gets lower than threshold (direction is shifting)
Turning on all options will logically result into more signals. Note these strategy ideas are experimental and can best be used in confirmation with other indicators.
Moving Average Filter (HTF)
The Oscillator has a horizontal line at the bottom. The line is green when the moving average is in a uptrend and red when the moving average is in a downtrend. The MA Filter comes with the following settings:
Higher Time Frame Setting
Type of MA being: EMA, DEMA, TEMA, SMA, WMA, HMA, McGinley
Length of number of bars (of the choosen HTF settings)
At last I hope you like this volume trading idea and if you have any comments let me know!
Cerca negli script per "bear"
3 Bar PlayThe "3 Bar Play" is a simple yet powerful pattern that traders look for as a signal of potential market movement. The pattern is defined by a sequence of three bars (or candlesticks) on the chart:
I saw Rake Trades post about this pattern. It not a new concept just wanted it to automatically be plotted on my chart rather then looking out for it.
Up 3 Bar Play: This pattern signals a potential upward movement.
The first bar (two bars ago from the current bar) must close higher than it opened, indicating a bullish bar.
The second bar (the previous bar) must close lower than it opened, indicating a bearish bar, but its low should be higher than the low of the first bar, showing that bears couldn't push the price much lower.
The third bar (the current bar) must open and close higher than the previous bar, closing above the high of the second bar, confirming the bullish sentiment.
Down 3 Bar Play: This pattern signals a potential downward movement.
The first bar (two bars ago from the current bar) must close lower than it opened, indicating a bearish bar.
The second bar (the previous bar) must close higher than it opened, indicating a bullish bar, but its high should be lower than the high of the first bar, showing that bulls couldn't push the price much higher.
The third bar (the current bar) must open and close lower than the previous bar, closing below the low of the second bar, confirming the bearish sentiment.
Plotting the Patterns
plotshape(): This function is used to plot shapes on the chart to visually highlight where the patterns occur.
For an "Up 3 Bar Play", a green triangle pointing upwards is plotted below the bullish pattern to indicate a potential buy signal.
For a "Down 3 Bar Play", a red triangle pointing downwards is plotted above the bearish pattern to indicate a potential sell signal.
Key Points
This script helps traders quickly identify potential entry points based on the 3 Bar Play pattern without manually scanning the charts.
It's important to remember that no single pattern guarantees market movements, and it's often used in conjunction with other indicators and analysis methods.
This script is a practical tool for those looking to incorporate the 3 Bar Play pattern into their trading strategy, offering a clear visual cue on the chart whenever the pattern is identified.
Please understand the 3 bar play and where you should set your stop loss
Inversion Fair Value Gap Screener | Flux Charts💎 GENERAL OVERVIEW
Introducing our new Inverse Fair Value Gap Screener! This screener can provide information about the latest Inverse Fair Value Gaps in up to 5 tickers. You can also customize the algorithm that finds the Inverse Fair Value Gaps and the styling of the screener.
Features of the new Inverse Fair Value Gap (IFVG) Screener :
Find Latest Inverse Fair Value Gaps Across 5 Tickers
Shows Their Information Of :
Latest Status
Number Of Retests
Consumption Percent
Volume
Customizable Algorithm / Styling
📌 HOW DOES IT WORK ?
A Fair Value Gap generally occur when there is an imbalance in the market. They can be detected by specific formations within the chart. An Inverse Fair Value Gap is when a FVG becomes invalidated, thus reversing the direction of the FVG.
IFVGs get consumed when a Close / Wick enters the IFVG zone. Check this example:
This screener then finds Fair Value Gaps across 5 different tickers, and shows the latest information about them.
Status ->
Far -> The current price is far away from the IFVG.
Approaching ⬆️/⬇️ -> The current price is approaching the IFVG, and the direction it's approaching from.
Inside -> The price is currently inside the IFVG.
Retests -> Retest means the price tried to invalidate the IFVG, but failed to do so. Here you can see how many times the price retested the IFVG.
Consumed -> IFVGs get consumed when a Close / Wick enters the IFVG zone. For example, if the price hits the middle of the IFVG zone, the zone is considered 50% consumed.
Volume -> Volume of a IFVG is essentially the volume of the bar that broke the original FVG that formed it.
🚩UNIQUENESS
This screener can detect latest Inverse Fair Value Gaps and give information about them for up to 5 tickers. This saves the user time by showing them all in a dashboard at the same time. The screener also uniquely shows information about the number of retests and the consumed percent of the IFVG, as well as it's volume. We believe that this extra information will help you spot reliable IFVGs easier.
⚙️SETTINGS
1. Tickers
You can set up to 5 tickers for the screener to scan Fair Value Gaps here. You can also enable / disable them and set their individual timeframes.
2. General Configuration
FVG Zone Invalidation -> Select between Wick & Close price for FVG Zone Invalidation.
IFVG Zone Invalidation -> Select between Wick & Close price for IFVG Zone Invalidation. This setting also switches the type for IFVG consumption.
Zone Filtering -> With "Average Range" selected, algorithm will find FVG zones in comparison with average range of last bars in the chart. With the "Volume Threshold" option, you may select a Volume Threshold % to spot FVGs with a larger total volume than average.
FVG Detection -> With the "Same Type" option, all 3 bars that formed the FVG should be the same type. (Bullish / Bearish). If the "All" option is selected, bar types may vary between Bullish / Bearish.
Detection Sensitivity -> You may select between Low, Normal or High FVG detection sensitivity. This will essentially determine the size of the spotted FVGs, with lower sensitivities resulting in spotting bigger FVGs, and higher sensitivities resulting in spotting all sizes of FVGs.
Bitcoin Momentum StrategyThis is a very simple long-only strategy I've used since December 2022 to manage my Bitcoin position.
I'm sharing it as an open-source script for other traders to learn from the code and adapt it to their liking if they find the system concept interesting.
General Overview
Always do your own research and backtesting - this script is not intended to be traded blindly (no script should be) and I've done limited testing on other markets beyond Ethereum and BTC, it's just a template to tweak and play with and make into one's own.
The results shown in the strategy tester are from Bitcoin's inception so as to get a large sample size of trades, and potential returns have diminished significantly as BTC has grown to become a mega cap asset, but the script includes a date filter for backtesting and it has still performed solidly in recent years (speaking from personal experience using it myself - DYOR with the date filter).
The main advantage of this system in my opinion is in limiting the max drawdown significantly versus buy & hodl. Theoretically much better returns can be made by just holding, but that's also a good way to lose 70%+ of your capital in the inevitable bear markets (also speaking from experience).
In saying all of that, the future is fundamentally unknowable and past results in no way guarantee future performance.
System Concept:
Capture as much Bitcoin upside volatility as possible while side-stepping downside volatility as quickly as possible.
The system uses a simple but clever momentum-style trailing stop technique I learned from one of my trading mentors who uses this approach on momentum/trend-following stock market systems.
Basically, the system "ratchets" up the stop-loss to be much tighter during high bearish volatility to protect open profits from downside moves, but loosens the stop loss during sustained bullish momentum to let the position ride.
It is invested most of the time, unless BTC is trading below its 20-week EMA in which case it stays in cash/USDT to avoid holding through bear markets. It only trades one position (no pyramiding) and does not trade short, but can easily be tweaked to do whatever you like if you know what you're doing in Pine.
Default parameters:
HTF: Weekly Chart
EMA: 20-Period
ATR: 5-period
Bar Lookback: 7
Entry Rule #1:
Bitcoin's current price must be trading above its higher-timeframe EMA (Weekly 20 EMA).
Entry Rule #2:
Bitcoin must not be in 'caution' condition (no large bearish volatility swings recently).
Enter at next bar's open if conditions are met and we are not already involved in a trade.
"Caution" Condition:
Defined as true if BTC's recent 7-bar swing high minus current bar's low is > 1.5x ATR, or Daily close < Daily 20-EMA.
Trailing Stop:
Stop is trailed 1 ATR from recent swing high, or 20% of ATR if in caution condition (ie. 0.2 ATR).
Exit on next bar open upon a close below stop loss.
I typically use a limit order to open & exit trades as close to the open price as possible to reduce slippage, but the strategy script uses market orders.
I've never had any issues getting filled on limit orders close to the market price with BTC on the Daily timeframe, but if the exchange has relatively low slippage I've found market orders work fine too without much impact on the results particularly since BTC has consistently remained above $20k and highly liquid.
Cost of Trading:
The script uses no leverage and a default total round-trip commission of 0.3% which is what I pay on my exchange based on their tier structure, but this can vary widely from exchange to exchange and higher commission fees will have a significantly negative impact on realized gains so make sure to always input the correct theoretical commission cost when backtesting any script.
Static slippage is difficult to estimate in the strategy tester given the wide range of prices & liquidity BTC has experienced over the years and it largely depends on position size, I set it to 150 points per buy or sell as BTC is currently very liquid on the exchange I trade and I use limit orders where possible to enter/exit positions as close as possible to the market's open price as it significantly limits my slippage.
But again, this can vary a lot from exchange to exchange (for better or worse) and if BTC volatility is high at the time of execution this can have a negative impact on slippage and therefore real performance, so make sure to adjust it according to your exchange's tendencies.
Tax considerations should also be made based on short-term trade frequency if crypto profits are treated as a CGT event in your region.
Summary:
A simple, but effective and fairly robust system that achieves the goals I set for it.
From my preliminary testing it appears it may also work on altcoins but it might need a bit of tweaking/loosening with the trailing stop distance as the default parameters are designed to work with Bitcoin which obviously behaves very differently to smaller cap assets.
Good luck out there!
Wick %Heyo Fellas,
thanks for checking out my new indicator.
Introduction
Wick % is a simple indicator to compare wick size with body size (mode 1) and to compare wick size with candle size (mode 2).
Upper wicks are bullish when close is higher than open pricen.
Lower wicks are bearish when close is lower than open price.
Wick Theory
In general, big wick and small bodie on a bar means that bull and bears are fighting heavily.
A big wick below the body means the bulls are leading in that fight,
and a big wick above the body means the bears are leading in that fight.
Calculation Formula
Mode 1 – Percentual Increase Wick/Body:
upperWickPercentage = (upperWick / body) * 100 - 100
lowerWickPercentage = (lowerWick / body) * 100 - 100
Mode 2 – Percent Wick/Candlestick:
upperWickPercentage = (upperWick / (high - low)) * 100
lowerWickPercentage = (lowerWick / (high - low)) * 100
Usage
You can use it on every symbol and every timeframe.
The indicator repaints by default, but you can disable it in the settings.
When you disable repaint, it moves the label one bar to the right.
If you want to use the indicator for signals, you must disable repainting.
Best regards,
simwai
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.
MACD by Take and TradeImproved version of MACD with asymmetrical BUY and SELL approaches.
This indicator is based on popular MACD one, but with some "tricks" designed to make it more applicable to the rapidly changing crypto market.
Key benefits:
Dynamic auto-adjusted threshold to filter out weak signals
Highlighted BUY/SELL signals with divergence (if a signal is accompanied by divergence, for example, price makes a new high while macd has a second high below the first, this signal is considered stronger and will be highlighted in a darker color)
Boost BUY signals on very slow market in accumulation phase
Not symmetric! It uses 2 different signal lines, which allows to obtain SELL signals earlier comparing to classic MACD approach
Classic concept of MACD
Classic MACD, in its simplest case, consists of two lines - macd line and signal line. Macd line is a difference between so-called "fast" and "slow" EMA lines (there are just a Exponential Moving Average lines with different windows: "12" for fast and "26" for slow). Signal line is just a smoothed "macd" line.
When macd line crosses signal line from bottom to up and intersection point < 0, this is "BUY" signal. And vise versa, when macd line crosses signal line from top to bottom, and intersection point > 0, this is "SELL" signal.
Parameters used in default configuration of classic MACD indicator:
Fast line: EMA-12
Slow line: EMA-26
Signal line: EMA-9
Problem of classic concept
Classic MACD indicator usually gives not bad "BUY" signals, especially if using them not for operational trading but for "investment" strategy. But "SELL" signalls usually generated too late. Simply because the market tends to fall much faster than it rises.
Possible solution (the main feature of our version of MACD)
To make indicator react faster on SELL condition, while still keeping it reliable for BUY signals, we decided to use two signal lines . Faster than default signal line (with window=6) for BUY signals and much faster than default (with window=2) for SELL signals.
This approach allowed us to receive sell signals earlier and exit deals on more favorable prices. Trade off of this change - is the number of SELL signals - there were more of them. However, this does not matter, since we receive the very first sell signal with a "very fast signal line" much earlier than with classic indicator settings.
Parameters we use in our improved MACD indicator:
Fast line: EMA-12
Slow line: EMA-24
Faster signal line: EMA-6
Much faster signal line: EMA-2
Removing noise (false triggerings)
Other drawback of classic MACD - it generates a lot of "weak" (false) signals. This signals are generated when macd crosses signal line much close to zero-line. And usually there are a lot of such intersections.
To remove this kind of noise, we added a trigger threshold, which by default is equal to 2.5% of the average asset price over a long period of time. Due to the link to the average price, this threshold automatically takes a specific value for each trading pair. Threshold 2.5% works perfect for all trading pairs for 1D timeframe. For other timeframes user can (and maybe will want) change it.
Boost weak BUY signals in a prolonged bear market
Signals on bearish stage are usually very weak, because there is no volatility, and no price impulse. And such signals will be filtered out as "noise" - see above. But this time is perfect time to buy! Therefore, we further boost the buy signals in a prolonged bear market so that they can pass through the filter and appear on the chart. Bearish period is the best time to invest!
Developed by Take and Trade. Enjoy using it!
Grucha Percentage Index (GPI) V2Grucha Percentage Index originally created by Polish coder named Grzegorz Antosiewicz in 2011 as mql code. This code is adapted by his original code to tradingview's pinescript.
What Does it Do
GPI is an oscillator that finds the lowest/highest prices with certain depth and generates signals by comparing the bull and bear bars. It use two lines, one is the original GPI calculation, the other is the smoothed version of the original line.
How to Use
GPI can catch quick volatility based movements and can be used as a confirmation indicator along with your existing trading system. When GDI (default color yellow) crosses above the GDI MA (default colored blue) it can be considered as a bullish movement and reverse can be considered as bearish movement.
How does it Work
The main calculation is done via the code below:
for i=0 to length
if candleC < 0
minus += candleC
if candleC >= 0
plus -= candleC
Simply we are adding green and red bars seperately and then getting their percentage to the bullish movement to reflect correctly in a 0-100 z-score enviroment via the code below:
res = (math.abs(minus)/sum)*100
Rest is all about plotting the results and adding seperate line with smoothing.
Note
These kind of oscillators are not designed to be used alone for signal generation but rather should be used in combination with different indicators to increase reliability of your signals.
Happy Trading.
CBO (Candle Bias Oscillator)The Candle Bias Oscillator (CBO) with volume and ATR scaling is a unique technical analysis tool designed to capture market sentiment through the analysis of candlestick patterns, volume momentum, and market volatility. This indicator is built on the foundation of assessing the bias within a candlestick's body and wicks, adjusted for market volatility using the Average True Range (ATR), and further refined by comparing the Rate of Change (ROC) in volume and the adjusted bias. The culmination of these calculations results in the CBO, a smoothed oscillator that highlights potential market turning points through divergence analysis.
Key Features:
Bias Calculations: Utilizes the relationship between the candle's body and wicks to determine the market's immediate bias, offering a nuanced view beyond simple price action. Have you ever wanted to quantify exactly how bullish or bearish a particular candle or candlestick pattern is? Whether it's dojis, hammers, engulfing, gravestones, evening morning star, three soldiers etc. you don't have to memorize 50 candlestick patterns anymore.
Volatility Adjustment: Employs the ATR to adjust the bias calculation, ensuring the oscillator remains relevant across varying market conditions by accounting for volatility.
Momentum and Divergence: Measures the momentum in volume and bias through ROC calculations, identifying divergence that may signal reversals or significant price movements.
Signal Line: A smoothed version of the CBO, derived from its own values, serving as a benchmark for identifying potential crossovers and divergences.
Utility and Application:
The CBO with Divergence Scaling is developed for traders who seek a deeper understanding of market dynamics beyond price movements alone. It is particularly useful for identifying potential reversals or continuation patterns early, by highlighting divergence between market sentiment (as expressed through candlestick bias) and actual volume movements. In this way, it aligns us retail traders with institutional traders and smart money. This indicator is versatile and can be applied across various time frames and market instruments, offering value to both short-term traders and long-term investors.
How to Use:
Trend Identification: The direction and value of the CBO provide insights into the prevailing market trend. A positive oscillator value may indicate bullish sentiment, while a negative value suggests bearish sentiment.
Signal Line Crossovers: Crossovers between the CBO and its signal line can be used as potential buy or sell signals. A crossover above the signal line might indicate a buying opportunity, whereas a crossover below could suggest a selling point.
Divergence: Discrepancies between the CBO and price action (especially when confirmed by volume ROC) can highlight potential reversals.
Customization and Parameters: This script allows users to adjust several parameters, including oscillator periods, signal line periods, ATR periods, and ROC periods for divergence, to best fit their trading strategy and the characteristics of the market they are analyzing.
Conclusion:
The Custom Bias Oscillator with Divergence Scaling is a comprehensive tool designed to offer traders a multi-faceted view of market conditions, combining elements of price action, volatility, and momentum. By integrating these aspects into a single indicator, it aims to provide a more rounded and actionable insight into market trends and potential turning points.
To comply with best practices and ensure clarity regarding the informational nature of the Custom Bias Oscillator (CBO) tool, it's crucial to include a disclaimer about the non-advisory nature of the script. Here's a suitable disclaimer that you can add to the end of your script description or publication:
Disclaimer:
The Custom Bias Oscillator (CBO) with Divergence Scaling and its accompanying analysis are provided as tools for educational and informational purposes only and should not be construed as financial advice. The creator of this indicator does not guarantee any specific outcomes or profit, and all users should be aware of the risks involved in trading and investing. Users should conduct their own research and consult with a professional financial advisor before making any investment decisions. The use of this indicator is at the user's own risk, and the creator bears no responsibility for any direct or consequential loss arising from any use of this tool or the information provided herein.
GG - LevelsThe GG Levels indicator is a tool designed for day trading U.S. equity futures. It highlights key levels intraday, overnight, intermediate-swing levels that are relevant for intraday futures trading.
Terminology
RTH (Regular Trading Hours): Represents the New York session from 09:30 to 17:00 EST.
ON Session (Overnight Session): Represents the trading activity from 17:00 to 09:29 EST.
IB (Initial Balance): The first hour of the New York session, from 09:30 to 10:30 EST.
Open: The opening price of the RTH session.
YH (Yesterday's High): The highest price during the RTH session of the previous day.
YL (Yesterday's Low): The lowest price during the RTH session of the previous day.
YC (Yesterday's Close): The daily bar close which for futures gets updated to settlement.
IBH (Initial Balance High): The highest price during the IB session.
IBL (Initial Balance Low): The lowest price during the IB session.
ONH (Overnight High): The highest price during the ON session.
ONL (Overnight Low): The lowest price during the ON session.
VWAP (Volume-Weighted Average Price): The volume-weighted average price that resets each day.
Why is RTH Important?
Tracking the RTH session is important because often times the overnight session can be filled with "lies". It is thought that because the overnight session is lower volume price can be pushed or "manipulated" to extremes that would not happen during higher volume times.
Why is the ON Session Important Then?
Just because the ON session can be thought as a "lie" doesn't mean it is relevant to know. For example, if price is stuck inside the ON range then you can think of the market as rotational or range-bound. If price is above the ON range then it can be thought of as bullish. If price is below the ON range then it can be thought as bearish.
What is IB?
IB or initial balance is the first hour of the New York Session. Typically the market sets the tone for the day in the first hour. This tone is similarly a map like the ON session. If we are above the IBH then it is bullish and likely a trend day to the upside. If we are below the IBL then it is bearish and likely a trend day to the downside. If we are in IB then we want to avoid conducting business in the middle of IBH and IBL to avoid getting chopped up in a range bound market.
These levels are not a holy grail
You should use this indicator as guide or map for context about the instrument you are trading. You need to combine your own technical analysis with this indicator. You want as much context confirming your trade thesis in order to enter a trade. Simply buying or selling because we are above or below a level is not recommended in any circumstance. If it were that easy I would not publish this indicator.
Adjustments
In the indicator settings you can adjust the RTH, ON, and IB session-time settings. All of the times entered must be in EST (Eastern Standard Time). You may want to do this to apply the levels to a foreign market.
Examples
Crypto Stablecoin Supply - Indicator [presentTrading]█ Introduction and How it is Different
The "Stablecoin Supply - Indicator" differentiates itself by focusing on the aggregate supply of major stablecoins—USDT, USDC, and DAI—rather than traditional price-based metrics. Its premise is that fluctuations in the total supply of these stablecoins can serve as leading indicators for broader market movements, offering traders a unique vantage point to anticipate shifts in market sentiment.
BTCUSD 6h for recent bull market
BTCUSD 8h
█ Strategy, How it Works: Detailed Explanation
🔶 Data Collection
The strategy begins with the collection of the closing supply for USDT, USDC, and DAI stablecoins. This data is fetched using a specified timeframe (**`tfInput`**), allowing for flexibility in analysis periods.
🔶 Supply Calculation
The individual supplies of USDT, USDC, and DAI are then aggregated to determine the total stablecoin supply within the market at any given time. This combined figure serves as the foundation for the subsequent statistical analysis.
🔶 Z-Score Computation
The heart of the indicator's strategy lies in the computation of the Z-Score, which is a statistical measure used to identify how far a data point is from the mean, relative to the standard deviation. The formula for the Z-Score is:
Z = (X - μ) / σ
Where:
- Z is the Z-Score
- X is the current total stablecoin supply (TotalStablecoinClose)
- μ (mu) is the mean of the total stablecoin supply over a specified length (len)
- σ (sigma) is the standard deviation of the total stablecoin supply over the same length
A moving average of the Z-Score (**`zScore_ma`**) is calculated over a short period (defaulted to 3) to smooth out the volatility and provide a clearer signal.
🔶 Signal Interpretation
The Z-Score itself is plotted, with its color indicating its relation to a defined threshold (0.382), serving as a direct visual cue for market sentiment. Zones are also highlighted to show when the Z-Score is within certain extreme ranges, suggesting overbought or oversold conditions.
Bull -> Bear
█ Trade Direction
- **Entry Threshold**: A Z-Score crossing above 0.382 suggests an increase in stablecoin supply relative to its historical average, potentially indicating bullish market sentiment or incoming capital flow into cryptocurrencies.
- **Exit Threshold**: Conversely, a Z-Score dropping below -0.382 may signal a reduction in stablecoin supply, hinting at bearish sentiment or capital withdrawal.
█ Usage
Traders can leverage the "Stablecoin Supply - Indicator" to gain insights into the underlying market dynamics that are not immediately apparent through price analysis alone. It is particularly useful for identifying potential shifts in market sentiment before they are reflected in price movements. By integrating this indicator with other technical analysis tools, traders can develop a more rounded and informed trading strategy.
█ Default Settings
- Timeframe Input (`tfInput`): Allows users to specify the timeframe for data collection, adding flexibility to the analysis.
- Z-Score Length (`len`): Set to 252 by default, representing the period over which the mean and standard deviation of the stablecoin supply are calculated.
- Color Coding: Uses distinct colors (green for bullish, red for bearish) to indicate the Z-Score's position relative to its thresholds, enhancing visual clarity.
- Extreme Range Fill: Highlights areas between defined high and low Z-Score thresholds with distinct colors to indicate potential overbought or oversold conditions.
By integrating considerations of stablecoin supply into the analytical framework, the "Stablecoin Supply - Indicator" offers a novel perspective on cryptocurrency market dynamics, enabling traders to make more nuanced and informed decisions.
Pattern indicatorRules are pretty simple for this indicator .we are searching candlestick pattern on current day high and low ..
*** Candlestick we are looking for ***
1) Bullish/Bearish Engulfing 2) Bearish/Bullish Harami 3)Hammer/Inverted Hammer
Rule for searching bullish candlestick ====>
1) searching for current day high and day low
2) looking for candlestick as Bullish Engulfing or Bullish Harami or Hammer
3) if we got both rule 1 and rule 2 we are getting label ex- bullish engulfing
4) we can Enable/Disable Candlestick we don't want to search
Rule for bearish candles ====>
1) searching for current day high and day low
2) looking for candlestick as Bearish Engulfing or Bearish Harami or inverted hammer
3) if we got both rule 1 and rule 2 we are getting label ex- bullish engulfing
4) we can Enable/Disable Candlestick we don't want to search
Note -- i have created all indicator calculation ....
Disclaimer: market involves significant risks, including complete possible loss of funds. Consequently trading is not suitable for all investors and traders. By increasing leverage risk increases as well.With the demo account you can test any trading strategies you wish in a risk-free environment. Please bear in mind that the results of the transactions of the practice account are virtual, and do not reflect any real profit or loss or a real trading environment, whereas market conditions may affect both the quotation and execution
Weekly BiasWeekly Bias
For H1 time frame and below.
Horizontal Line Plots every week.
Condition for line is 12am EST on Monday.
Price above, line is green~ potential bullishness.
Price below, line is red~ potential bearishness.
This line gives us potential sentiment for any given week on any given forex market.
If you have any questions, or want access to other indicators, please message me.
Golden Cross and Death Cross with ProbabilityThe Advanced Golden and Death Crossover Indicator offers traders a powerful tool for identifying potential buy and sell signals through the classic technical analysis method of moving average crossovers. This script enhances decision-making by dynamically changing the chart background color in response to Golden (bullish) and Death (bearish) crossovers, providing a visual representation of the market's momentum.
Features:
Golden and Death Crossover Detection: Utilizes a 50-period SMA and a 200-period SMA to identify potential buy (golden cross) and sell (death cross) points.
Continuous Background Coloring: Changes the chart's background color to green for golden crosses and red for death crosses, offering an intuitive grasp of market trends.
Customizable Lookback Period: Allows users to adjust the lookback period for calculating the success rate of each crossover, making the indicator adaptable to various trading strategies.
Success Rate Calculation: Provides an additional layer of analysis by calculating the historical success rate of crossovers within the specified lookback period.
Instructions:
Adding the Indicator: Search for "Advanced Golden and Death Crossover Indicator" in the TradingView Indicators & Strategies library and add it to your chart.
Customization: Access the indicator settings to adjust the lookback period according to your trading preferences.
Interpretation: Use the continuous background color as a guide to market conditions, with green indicating bullish momentum and red indicating bearish momentum. The success rate of past crossovers can help assess the reliability of the signals.
How the Script Works:
The Advanced Golden and Death Crossover Indicator operates by continuously monitoring two key moving averages (MAs) on your chart: a short-term (50-period) SMA and a long-term (200-period) SMA. Here's a step-by-step breakdown of its functionality:
Crossover Detection:
Golden Cross: When the short-term MA crosses above the long-term MA, indicating potential bullish momentum, the script identifies this as a Golden Cross signal.
Death Cross: Conversely, when the short-term MA crosses below the long-term MA, suggesting potential bearish momentum, the script flags this as a Death Cross signal.
Background Coloring:
Upon detecting a Golden Cross, the script changes the chart background to green, visually representing a bullish market condition.
Upon detecting a Death Cross, the chart background turns red, indicating bearish market conditions.
This color change remains in effect until the next crossover event, providing a continuous visual cue of the market's trend direction.
Success Rate Calculation:
The script calculates the historical success rate of these crossovers within a user-defined lookback period. This metric helps assess the reliability of the signals based on past performance.
Customization:
Users have the flexibility to adjust the lookback period for the success rate calculation, allowing for customization according to individual trading strategies and risk preferences.
Application in Trading Analysis:
Traders can use this indicator as part of their technical analysis toolkit to make informed decisions about entry and exit points. The visual cues from the continuous background coloring, combined with the success rate of past signals, provide a comprehensive overview of market trends and crossover reliability. It’s important for traders to combine this indicator with other analysis tools and consider broader market conditions to optimize their trading strategy.
Disclaimer:
This script is provided for educational and informational purposes only and should not be construed as investment advice. Trading involves risk, and you should conduct your own research or consult a financial advisor before making investment decisions. The author or distributor of this script bears no responsibility for any trading losses incurred by users.
Advanced Engulfing CandlesThere are a plenty of Engulfing candle detecting indicators but every single of them detect engulfing candles engulfed by only single candle but sometime it take more then one candle to engulf the previous opposite candle, which is also considered as engulfing candle.
So this script show both type of candles.
Type of Engulfing Candles
Normal Engulfing Candles
Candle engulfed by more then one continuous candle
I hope you will like it.
If you find any bugs or have any suggestions for any possible addition feel free to comment or DM me.
Pivot Percentile Trend - Strategy [presentTrading]
█ Introduction and How it is Different
The "Pivot Percentile Trend - Strategy" from PresentTrading represents a paradigm shift in technical trading strategies. What sets this strategy apart is its innovative use of pivot percentiles, a method that goes beyond traditional indicator-based analyses. Unlike standard strategies that might depend on single-dimensional signals, this approach takes a multi-layered view of market movements, blending percentile calculations with SuperTrend indicators for a more nuanced and dynamic market analysis.
This strategy stands out for its ability to process multiple data points across various timeframes and pivot lengths, thereby capturing a broader and more detailed picture of market trends. It's not just about following the price; it's about understanding its position in the context of recent historical highs and lows, offering a more profound insight into potential market movements.
BTC 6h L/S
Where traditional methods might react to market changes, the Pivot Percentile Trend strategy anticipates them, using a calculated approach to identify trend strengths and weaknesses. This foresight gives traders a significant advantage, allowing for more strategic decision-making and potentially increasing the chances of successful trades.
In essence, this strategy introduces a more comprehensive and proactive approach to trading, harnessing the power of advanced percentile calculations combined with the robustness of SuperTrend indicators. It's a strategy designed for traders who seek a deeper understanding of market dynamics and a more calculated approach to their trading decisions.
Local picture
█ Strategy, How It Works: Detailed Explanation
🔶 Percentile Calculations
- The strategy employs percentile calculations to assess the relative position of current market prices against historical data.
- For a set of lengths (e.g., `length * 1`, `length * 2`, up to `length * 7`), it calculates the 75th percentile for high values (`percentilesHigh`) and the 25th percentile for low values (`percentilesLow`).
- These percentiles provide a sense of where the current price stands compared to recent price ranges.
Length - 10
Length - 15
🔶 SuperTrend Indicator
- The SuperTrend indicator is a key component, providing trend direction signals.
- It uses the `currentTrendValue`, derived from the difference between bull and bear strengths calculated from the percentile data.
* used the Supertrend toolkit by @EliCobra
🔶 Trend Strength Counts
- The strategy calculates counts of bullish and bearish indicators based on comparisons between the current high and low against high and low percentiles.
- `countBull` and `countBear` track the number of times the current high is above the high percentiles and the current low is below the low percentiles, respectively.
- Weak bullish (`weakBullCount`) and bearish (`weakBearCount`) counts are also determined by how often the current lows and highs fall within the percentile range.
*The idea of this strength counts mainly comes from 'Trend Strength Over Time' @federalTacos5392b
🔶 Trend Value Calculation
- The `currentTrendValue` is a crucial metric, computed as `bullStrength - bearStrength`.
- It indicates the market's trend direction, where a positive value suggests a bullish trend and a negative value indicates a bearish trend.
🔶 Trade Entry and Exit Logic
- The entry points for trades are determined by the combination of the trend value and the direction indicated by the SuperTrend indicator.
- For a long entry (`shouldEnterLong`), the `currentTrendValue` must be positive and the SuperTrend indicator should show a downtrend.
- Conversely, for a short entry (`shouldEnterShort`), the `currentTrendValue` should be negative with the SuperTrend indicating an uptrend.
- The strategy closes positions when these conditions reverse.
█ Trade Direction
The strategy is versatile, allowing traders to choose their preferred trading direction: long, short, or both. This flexibility enables traders to tailor their strategies to their market outlook and risk appetite.
█ Default Settings and Customization
1. Trade Direction: Selectable as Long, Short, or Both, affecting the type of trades executed.
2. Indicator Source: Pivot Percentile Calculations, key for identifying market trends and reversals.
3. Lengths for Percentile Calculation: Various configurable lengths, influencing the scope of trend analysis.
4. SuperTrend Settings: ATR Length 20, Multiplier 18, affecting indicator sensitivity and trend detection.
5. Style Options: Custom colors for bullish (green) and bearish (red) trends, aiding visual interpretation.
6. Additional Settings: Includes contrarian signals and UI enhancements, offering strategic and visual flexibility.
Crypto Candlestick Patterns - CN VersionIntroduction:
The candlestick chart has been used for centuries since the Japanese applications. Based on the candlestick charting, people developed candle pattern analysis. Now we have tons of books or articles illustrating the usage of reversal patterns and continuation patterns, and computers provide a faster and preciser way to recognize these pattern.
Originally we have a common *All Candlestick Patterns* indicator to use. This indicator works well for most of the markets or commodities including stocks and futures. However, for cryptocurrency market, quite a few patterns are not suitable anymore. For example, crypto markets are continuously running 7x24hrs and the big coins with good volume tend to have almost continuous price in commonly used time periods. Hence, original patterns with "window" or "jump" concepts are usually not applied to crypto.
For these issues, I modified the original *All Candlestick Patterns* indicator and introduced the Chinese version for people speaking such language.
Like most of the other indicators, I personally do not recommend anyone to simply follow the patterns it shows to enter the market. You may take these recognized patterns as a reference, and further actions on trading should be done with several other tools, such as MACD, RSI, Stochastic and etc.
Usage:
The application of this indicator is basically the same as the original *All Candlestick Patterns* and you will get an automatically generated pattern recognition by your computer system.
There are a few parameters to adjust for the indicator:
Trending Detection Settings: Here you can choose SMA-Fast, SMA-Fast/Slow or None detecting options to recognize the current market trend. This is a minor improvement from the original indicator and you can choose your preferred trending detecting settings by changing the length of SMA.
Candlestick Settings: You may adjust the rules to recognize the properties of candlesticks. I add a "perturbation" parameter here, which actually is an error tolerance for pattern recognition. Some seemingly pattern may not fulfill the strict rules of classic candlestick patterns, but we may recognize them by watch the charting on our own. Hence this error tolerance may show more potential patterns from the charting.
Plot Settings: It is the usually colour choice and providing options for bullish/bearish.
Pattern Settings: Here you can select the patterns that you would like to see from the charting. You can pick the preferred reversal patterns or choose to show all the patterns. It's all up to you!
Features:
Language Translation: Since this is a Chinese language version. I have replaced all the English explanation of patterns to Chinese ones. Move your mouse to the label, you will find a brief intro of the pattern and a notice about bullish or bearish signals it indicates.
Alerts: As the same as the original one, we will have the alert options from this indicator. All the alerts and their messages are Chinese. You can activate alerts based on this indicator from the alert management section, as the same as many other indicators you have used before.
Future Improvements:
For now I am satisfied with the work I have done, and I may apply it to several charts. It's welcome for any users to take a look at the codes and put modifications or improvements towards it. Currently most of the comments in the code are in Chinese language, since basically it's for Chinese speaking users, while the code itself and the parameter names should be pretty easy to understand in English. (I have been using English for writing in the past 8 years, hence this introduction is in English as well.)
VWAP, MFI, RSI with S/R StrategyBest for 0dte/intraday trading on AMEX:SPY with 1 minute chart
Strategy Concept
This strategy aims to identify potential reversal points in a price trend by combining momentum indicators (RSI and MFI), volume-weighted price (VWAP), and recent price action trends. It looks for conditions where the price is poised to change direction, either bouncing off a support level in a potential uptrend or falling from a resistance level in a potential downtrend.
By incorporating both price level analysis (support/resistance) and momentum indicators, the strategy seeks to increase the likelihood of identifying significant trend reversals, taking into consideration both recent price movements and the current price's position relative to historical highs and lows.
VWAP (Volume-Weighted Average Price)
VWAP acts as a benchmark to determine the general market trend. It's an average price weighted by volume.
A price above VWAP is often considered bullish, and a price below VWAP is seen as bearish.
MFI (Money Flow Index) and RSI (Relative Strength Index) Parameters
MFI is a volume-weighted RSI, used to identify overbought (above 70) or oversold (below 30) conditions.
RSI is a momentum indicator that measures the magnitude of recent price changes to identify overbought or oversold conditions, similar to MFI.
The script uses standard overbought (70) and oversold (30) thresholds for both MFI and RSI.
Trend Check Function
The function trendCheck analyzes the past pastBars candles to count how many were bullish (closing price higher than the opening price) and bearish.
This function is used to assess the recent trend direction.
Support and Resistance Detection
The script calculates the highest high (highestHigh) and lowest low (lowestLow) over the last lookbackSR (50) periods to identify potential support and resistance levels.
isNearSupport and isNearResistance are conditions to check if the current price is within 0.08% of these identified levels, indicating proximity to support or resistance.
Buy and Sell Logic
Buy Signal:
The RSI crosses over the oversold threshold (30).
The MFI is also below its oversold level (30).
The current price is above the VWAP.
The recent trend (past 20 bars) has been predominantly bearish.
The price is near the identified support level.
Sell Signal:
The RSI crosses under the overbought threshold (70).
The MFI is above its overbought level (70).
The current price is below the VWAP.
The recent trend has been predominantly bullish.
The price is near the identified resistance level.
Aleem Trend Supertrend EMA Title: "Supertrend and 200 EMA Crossover Strategy"
Description:
This script is designed to provide traders with a robust and original trading strategy by combining the Supertrend indicator with a 200-period Exponential Moving Average (EMA). The core concept is to utilize the strengths of both indicators to determine optimal entry and exit points.
The Supertrend indicator is well-regarded for its precision in signaling trend reversals by considering the volatility of the market, as measured by the Average True Range (ATR). It is particularly useful for identifying ongoing trends and potential reversals.
The 200 EMA is a widely-used indicator that many traders look to as a determinant of the long-term trend. When the price is above the 200 EMA, the overall market sentiment is considered bullish, and when below, bearish.
By combining these two, the script generates a Buy signal under the following conditions:
When the Supertrend turns bullish (color changes from red to green) with the closing price above the 200 EMA, or
When the price crosses above the 200 EMA while the Supertrend is already green.
A Sell signal is generated when:
The Supertrend turns bearish (color changes from green to red) with the closing price below the 200 EMA, or
The price crosses below the 200 EMA while the Supertrend is already red.
To avoid repetitive signals and to maintain clarity, the script has been enhanced with a feature to prevent multiple consecutive Buy or Sell signals. Once a Buy or Sell signal is generated, the script will not produce another identical signal until an opposing signal or an exit condition is met.
Exit signals for both Buy and Sell positions are provided to indicate when the trend is weakening or reversing, based on the Supertrend's color change in relation to the 200 EMA.
This strategy is flexible and can be utilized across various time frames and asset classes. It aims to aid traders in making more informed decisions by highlighting potential reversals and continuations in the market trend.
Usage:
To use this script, traders should observe the Buy and Sell signals as potential entry points. Exit signals should be taken as prompts to close positions or to protect profits with stop-loss adjustments. As with all strategies, it's recommended to use this in conjunction with other analysis methods and to backtest thoroughly before live implementation.
RSI Graphique and Dashboard MTFMTF RSI Indicator - User Guide
Introduction:
The MTF RSI (Multi-Timeframe Relative Strength Index) Pine Script is designed to provide traders with a comprehensive view of the RSI (Relative Strength Index) across multiple timeframes. The script includes a primary chart displaying RSI values and a dashboard summarizing RSI trends for different time intervals.
Installation:
Copy the provided Pine Script.
Open the TradingView platform.
Create a new script.
Paste the copied code into the script editor.
Save and apply the script to your chart.
Primary Chart:
The primary chart displays RSI values for the selected timeframe (5, 15, 60, 240, 1440 minutes).
different color lines represent RSI values for different timeframes.
Overbought and Oversold Levels:
Overbought levels (70) are marked in red, while oversold levels (30) are marked in blue for different timeframes.
Dashboard:
The dashboard is a quick reference for RSI trends across multiple timeframes.
Each row represents a timeframe with corresponding RSI trend information.
Arrows (▲ for bullish, ▼ for bearish) indicate the current RSI trend.
Arrow colors represent the trend: blue for bullish, red for bearish.
Settings:
Users can customize the RSI length, background color, and other parameters.
The background color of the dashboard can be adjusted for light or dark themes.
Interpretation:
Bullish Trend: ▲ arrow and blue color.
Bearish Trend: ▼ arrow and red color.
RSI values above 70 may indicate overbought conditions, while values below 30 may indicate oversold conditions.
Practical Tips:
Timeframe Selection: Consider the trend alignment across different timeframes for comprehensive market analysis.
Confirmation: Use additional indicators or technical analysis to confirm RSI signals.
Backtesting: Before applying in live trading, conduct thorough backtesting to evaluate the script's performance.
Adjustment: Modify settings according to your trading preferences and market conditions.
Disclaimer:
This script is a tool for technical analysis and should be used in conjunction with other indicators. It is not financial advice, and users should conduct their own research before making trading decisions. Adjust settings based on personal preferences and risk tolerance. Use the script responsibly and at your own risk.
Session Breakout Scalper Trading BotHi Traders !
Introduction:
I have recently been exploring the world of automated algorithmic trading (as I prefer more objective trading strategies over subjective technical analysis (TA)) and would like to share one of my automation compatible (PineConnecter compatible) scripts “Session Breakout Scalper”.
The strategy is really simple and is based on time conditional breakouts although has more ”relatively” advanced optional features such as the regime indicators (Regime Filters) that attempt to filter out noise by adding more confluence states and the ATR multiple SL that takes into account volatility to mitigate the down side risk of the trade.
What is Algorthmic Trading:
Firstly what is algorithmic trading? Algorithmic trading also known as algo-trading, is a method of using computer programs (in this case pine script) to execute trades based on predetermined rules and instructions (this trading strategy). It's like having a robot trader who follows a strict set of commands to buy and sell assets automatically, without any human intervention.
Important Note:
For Algorithmic trading the strategy will require you having an essential TV subscription at the minimum (so that you can set alerts) plus a PineConnecter subscription (scroll down to the .”How does the strategy send signals” headings to read more)
The Strategy Explained:
Is the Time input true ? (this can be changed by toggling times under the “TRADE MEDIAN TIMES” group for user inputs).
Given the above is true the strategy waits x bars after the session and then calculates the highest high (HH) to lowest low (LL) range. For this box to form, the user defined amount of bars must print after the session. The box is symmetrical meaning the HH and LL are calculated over a lookback that is equal to the sum of user defined bars before and after the session (+ 1).
The Strategy then simultaneously defines the HH as the buy level (green line) and the LL as the sell level (red line). note the strategy will set stop orders at these levels respectively.
Enter a buy if price action crosses above the HH, and then cancel the sell order type (The opposite is true for a stop order).
If the momentum based regime filters are true the strategy will check for the regime / regimes to be true, if the regime if false the strategy will exit the current trade, as the regime filter has predicted a slowing / reversal of momentum.
The image below shows the strategy executing these trading rules ( Regime filters, "Trades on chart", "Signal & Label" and "Quantity" have been omitted. "Strategy label plots" has been switched to true)
Other Strategy Rules:
If a new session (time session which is user defined) is true (blue vertical line) and the strategy is currently still in a trade it will exit that trade immediately.
It is possible to also set a range of percentage gain per day that the strategy will try to acquire, if at any point the strategy’s profit is within the percentage range then the position / trade will be exited immediately (This can be changed in the “PERCENT DAY GAIN” group for user inputs)
Stops and Targets:
The strategy has either static (fixed) or variable SL options. TP however is only static. The “STRAT TP & TP” group of user inputs is responsible for the SL and TP values (quoted in pips). Note once the ATR stop is set to true the SL values in the above group no longer have any affect on the SL as expected.
What are the Regime Filters:
The Larry Williams Large Trade Index (LWLTI): The Larry Williams Large Trade Index (LWTI) is a momentum-based technical indicator developed by iconic trader Larry Williams. It identifies potential entries and exits for trades by gauging market sentiment, particularly the buying and selling pressure from large market players. Here's a breakdown of the LWTI:
LWLTI components and their interpretation:
Oscillator: It oscillates between 0 and 100, with 50 acting as the neutral line.
Sentiment Meter: Values above 75 suggest a bearish market dominated by large selling, while readings below 25 indicate a bullish market with strong buying from large players.
Trend Confirmation: Crossing above 75 during an uptrend and below 25 during a downtrend confirms the trend's continuation.
The Andean Oscillator (AO) : The Andean Oscillator is a trend and momentum based indicator designed to measure the degree of variations within individual uptrends and downtrends in the prices.
Regime Filter States:
In trading, a regime filter is a tool used to identify the current state or "regime" of the market.
These Regime filters are integrated within the trading strategy to attempt to lower risk (equity volatility and/or draw down). The regime filters have different states for each market order type (buy and sell). When the regime filters are set to true, if these regime states fail to be true the trade is exited immediately.
For Buy Trades:
LWLTI positive momentum state: Quotient of the lagged trailing difference and the ATR > 50
AO positive momentum state: Bull line > Bear line (signal line is omitted)
For Sell Trades:
LWLTI negative momentum stat: Quotient of the lagged trailing difference and the ATR < 50
AO negative momentum state: Bull line < Bear line (signal line is omitted)
How does the Strategy Send Signals:
The strategy triggers a TV alert (you will neet to set a alert first), TV then sends a HTTP request to the automation software (PineConnecter) which receives the request and then communicates to an MT4/5 EA to automate the trading strategy.
For the strategy to send signals you must have the following
At least a TV essential subscription
This Script added to your chart
A PineConnecter account, which is paid and not free. This will provide you with the expert advisor that executes trades based on these strategies signals.
For more detailed information on the automation process I would recommend you read the PineConnecter documentation and FAQ page.
Dashboard:
This Dashboard (top right by defualt) lists some simple trading statistics and also shows when a trade is live.
Important Notice:
- USE THIS STRATEGY AT YOUR OWN RISK AND ALWAYS DO YOUR OWN RESEARCH & MANUAL BACKTESTING !
- THE STRATEGY WILL NOT EXHIBIT THE BACKTEST PERFORMANCE SEEN BELOW IN ALL MARKETS !
PB wTF50What kind of traders/investors are we?
We are trend followers, always on the lookout for the next big move in the market. Our scripts are meticulously crafted for higher timeframes (daily, weekly, monthly) aiming to capture the large market trends.
What does this script do?
The Pb wTF50 script simplifies the complex world of investing by colour-coding bars to indicate the trend direction. Green bars signify a bullish trend, red indicates a bearish trend, and a combination of both signifies a sideways market. This visual representation ensures investors can quickly gauge the market's direction and act accordingly.
How is the PB wTF50 produced?
The PB wTF50 script employs the simple moving averages (SMAs) as its backbone. Bars positioned above both the SMAs turn green, indicating a bullish trend. Conversely, bars below these SMAs turn red, signalling a bearish trend.
What is the best timeframe to use the script?
The PB wTF50 script is designed for the weekly timeframe. This ensures that traders and investors are aligned with the long-term market trend, filtering out the noise of shorter timeframes.
What makes this script unique?
The challenges of identifying the onset, progression, and culmination of trends are well-known in the investing community. The PbF script addresses these challenges head-on.
The PB wTF50 is not a lagging indicator. It is aligned with price movement, which helps investors and traders focus on what the asset’s price is doing. The asset’s price is the primary indicator of its direction.
Lagging indicators can be used alongside the PB wTF50 to confirm the asset’s direction.
The PBwTF50 continues to remain green during extended periods of bullish pullbacks and red during extended periods of bearish pullbacks. This helps investors and traders hold positions during corrections in the market.
When interacting with OB/OS zones, investors and traders are positioned to align with the trend and ignore short-term fluctuations against the trend.
The PB wTF50 can be used to enter additional positions, also known as compounding, when an asset’s price has pulled back into an OS zone, but the trend filter has remained green in a bull trend/OB zone, but the trend filter has remained red in a bear trend.
In essence, the PB wTF50 script is a trend filter that gives investors and traders the ability to apply discretion with the start and end of long-term trends as they develop.
CBC FlipThis is an indicator for the Candle By Candle (CBC) Flip strategy as created by @MapleStax
It’s useful to traders because it’s a simple approach to gauge if bulls or bears are in control for any particular candle. The logic is as follows:
If the most recent candle close is above the previous candle high, then bulls are in control.
If the most recent candle close is below the previous candle low, then bears are in control.
If neither of these 2 conditions are met, then whoever was already in control remains in force until one of the 2 conditions is met and the sentiment is flipped, hence the name CBC Flip.
My script is original because there are no other CBC Flip scripts available on TV. This is the first, which is why I created it, to help other traders benefit from the power of CBC Flips.
The indicator output is simply interpreted as follows:
Triangle up = bulls in control
Triangle down = bears in control
In my experience this script is best used on the 5 or 10 minute time frames, as it helps to keep you in the trade for the bigger moves once a trend is established, while not getting shaken out from the “noisy” up/down candle price action of lower time frames like the 1 minute.
I’ve also had more success with this indicator when only taking long trades once the green triangle appears and price is above VWAP, and only taking short trades once the red triangle appears and price is below VWAP.
