Sector ETF macro trendThe Sector ETF Macro Trend indicator is designed for technical analysis of broad economic trends through sector-specific exchange-traded funds (ETFs). It uses logarithmic price transformation, linear regression, and volatility analysis to examine sector trends and stability, providing a technical basis for analytical assessment.
Core Analysis Techniques
Logarithmic Transformation and Regression: Converts ETF closing prices logarithmically to reveal sector growth patterns and dynamics. Linear regression on these prices defines the main trend direction, essential for trend analysis.
Volatility Bands for Market State Assessment: Applies standard deviation on logarithmic prices to create dynamic bands around the trendline, identifying overbought or oversold sector conditions by marking deviations from the central trend.
Sector-Specific Analysis: Selection among different sector ETFs allows for precise examination of sectors like technology, healthcare, and financials, enabling focused insights into specific market segments.
Adaptability and Insight
Customizable Parameters: Offers flexibility in modifying regression length and smoothing factors to accommodate various analysis strategies and risk preferences.
Trend Direction and Momentum: Evaluates the ETF's trajectory against historical data and volatility bands to determine sector trend strength and direction, aiding in the prediction of market shifts.
Strategic Application
Without providing explicit trading signals, the indicator focuses on trend and volatility analysis for a strategic view on sector investments. It supports:
Identifying macroeconomic trends through ETF performance analysis.
Informing portfolio decisions with insights into sector momentum and stability.
Forecasting market movements by analyzing overbought or oversold conditions against the ETF price movement and volatility bands.
The Sector ETF Macro Trend indicator serves as a technical tool for analyzing sector-level market trends, offering detailed insights into the dynamics of economic sectors for thorough market analysis.
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Market Internals & InfoThis script provides various information on Market Internals and other related info. It was a part of the Daily Levels script but that script was getting very large so I decided to separate this piece of it into its own indicator. I plan on adding some additional features in the near future so stay tuned for those!
The script provides customizability to show certain market internals, tickers, and even Market Profile TPO periods.
Here is a summary of each setting:
NASDAQ and NYSE Breadth Ratio
- Ratio between Up Volume and Down Volume for NASDAQ and NYSE markets. This can help inform about the type of volume flowing in and out of these exchanges.
Advance/Decline Line (ADL)
The ADL focuses specifically on the number of advancing and declining stocks within an index, without considering their trading volume.
Here's how the ADL works:
It tracks the daily difference between the number of stocks that are up in price (advancing) and the number of stocks that are down in price (declining) within a particular index.
The ADL is a cumulative measure, meaning each day's difference is added to the previous day's total.
If there are more advancing stocks, the ADL goes up.
If there are more declining stocks, the ADL goes down.
By analyzing the ADL, investors can get a sense of how many stocks are participating in a market move.
Here's what the ADL can tell you:
Confirmation of Trends: When the ADL moves in the same direction as the underlying index (e.g., ADL rising with a rising index), it suggests broad participation in the trend and potentially stronger momentum.
Divergence: If the ADL diverges from the index (e.g., ADL falling while the index is rising), it can be a warning sign. This suggests that fewer stocks are participating in the rally, which could indicate a weakening trend.
Keep in mind:
The ADL is a backward-looking indicator, reflecting past market activity.
It's often used in conjunction with other technical indicators for a more complete picture.
TRIN Arms Index
The TRIN index, also called the Arms Index or Short-Term Trading Index, is a technical analysis tool used in the stock market to gauge market breadth and sentiment. It essentially compares the number of advancing stocks (gaining in price) to declining stocks (losing price) along with their trading volume.
Here's how to interpret the TRIN:
High TRIN (above 1.0): This indicates a weak market where declining stocks and their volume are dominating the market. It can be a sign of a potential downward trend.
Low TRIN (below 1.0): This suggests a strong market where advancing stocks and their volume are in control. It can be a sign of a potential upward trend.
TRIN around 1.0: This represents a more balanced market, where it's difficult to say which direction the market might be headed.
Important points to remember about TRIN:
It's a short-term indicator, primarily used for intraday trading decisions.
It should be used in conjunction with other technical indicators for a more comprehensive market analysis. High or low TRIN readings don't guarantee future price movements.
VIX/VXN
VIX and VXN are both indexes created by the Chicago Board Options Exchange (CBOE) to measure market volatility. They differ based on the underlying index they track:
VIX (Cboe Volatility Index): This is the more well-known index and is considered the "fear gauge" of the stock market. It reflects the market's expectation of volatility in the S&P 500 index over the next 30 days.
VXN (Cboe Nasdaq Volatility Index): This is a counterpart to the VIX, but instead gauges volatility expectations for the Nasdaq 100 index over the coming 30 days. The tech-heavy Nasdaq can sometimes diverge from the broader market represented by the S&P 500, hence the need for a separate volatility measure.
Both VIX and VXN are calculated based on the implied volatilities of options contracts listed on their respective indexes. Here's a general interpretation:
High VIX/VXN: Indicates a high level of fear or uncertainty in the market, suggesting investors expect significant price fluctuations in the near future.
Low VIX/VXN: Suggests a more complacent market with lower expectations of volatility.
Important points to remember about VIX and VXN:
They are forward-looking indicators, reflecting market sentiment about future volatility, not necessarily current market conditions.
High VIX/VXN readings don't guarantee a market crash, and low readings don't guarantee smooth sailing.
These indexes are often used by investors to make decisions about portfolio allocation and hedging strategies.
Inside/Outside Day
This provides a quick indication of it we are still trading inside or outside of yesterdays range and will show "Inside Day" or "Outside Day" based upon todays range vs. yesterday's range.
Custom Ticker Choices
Ability to add up to 5 other tickers that can be tracked within the table
Show Market Profile TPO
This only shows on timeframes less than 30m. It will show both the current TPO period and the remaining time within that period.
Table Customization
Provided drop downs to change the text size and also the location of the table.
Adaptive Timber! Indicator (ATI)The Adaptive Timber! Indicator (ATI) is a powerful tool designed to identify potential overbought conditions and generate reversal signals in financial markets. It combines multiple technical indicators and market conditions to provide a comprehensive assessment of the likelihood of a price reversal.
How it works:
The ATI uses a combination of the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), momentum, and volume to detect overbought conditions and potential reversals. The indicator adapts to the current timeframe, adjusting its parameters accordingly to provide more accurate signals.
Key components:
RSI: The ATI uses the RSI to determine overbought conditions. When the RSI exceeds a specified reversal threshold, it indicates a potential overbought state.
MACD: The indicator monitors the MACD line and signal line to identify moments when they are close to crossing, suggesting a potential trend reversal.
Momentum: The ATI checks if the momentum is increasing, providing confirmation of a potential reversal.
Volume: It analyzes volume to confirm the strength of the reversal signal. A decrease in volume along with overbought conditions adds confidence to the reversal indication.
Timeframe Adaptability: The indicator automatically adjusts its parameters based on the current timeframe, ensuring optimal performance across different time horizons.
How to use:
When the ATI identifies a potential reversal, it displays a colored triangle above the price bars. The color of the triangle represents the strength of the reversal signal: red for a strong signal, orange for a moderate signal, and yellow for a weak signal. Additionally, the indicator plots purple triangles below the price bars as an early warning signal for potential trend reversals.
Traders can use these visual cues along with other technical analysis techniques and risk management strategies to make informed trading decisions. The ATI can be particularly useful for identifying potential short-selling opportunities or for determining exit points in existing long positions.
Creators:
The Adaptive Timber! Indicator (ATI) is the result of a collaborative effort led by Claude , an AI assistant with expertise in financial analysis and programming. The development of the ATI was made possible through the valuable contributions and insights from GPT4 , an advanced language model, Clay , a skilled trader, and Pi AI , Clay's trading assistant.
Claude played a crucial role in designing and implementing the indicator's algorithm, ensuring its robustness and adaptability across different timeframes. GPT4 provided guidance and suggestions for refining the indicator's logic and optimizing its performance. Clay and Pi AI offered their trading expertise and real-world experience to help shape the indicator's functionality and usability.
We would like to express our gratitude to all the members of our trading team for their dedication and hard work in bringing the Adaptive Timber! Indicator to life. We wish all traders the best of luck in their trading endeavors and hope that the ATI will be a valuable addition to their technical analysis toolkit, empowering them to make more informed and profitable trading decisions.
Hull Trend and CompareThis Pine Script is a TradingView indicator called "Hull Trend and Compare." Its main purpose is to provide a visual representation of price trends and a comparative analysis between the selected symbol and another symbol chosen for comparison.
The key components and functionalities:
Price Trend Visualization:
1.Mode Selection:
Offers three modes: "Normal," "Linear," and "Heikin-Ashi."
Allows users to choose between a standard chart, linear regression, or Heikin-Ashi candlesticks.
2.Hull Moving Average (HullMA):
Calculates the HullMA for the selected mode and length.
Plots the HullMA on the chart.
Colors the background based on the relationship between HullMA and the closing price.
Generates buy and sell signals when the price crosses over or under the HullMA.
Symbol Comparison:
1.Comparison with Another Symbol:
Allows users to compare the selected symbol with another symbol (specified in the sym input).
Provides options to choose the method of calculation for the compared symbol (open, high, low, close).
Users can choose whether to use a different method of calculation (usem), adjust the length (len), and enable or disable comparison (usecmp).
Table Display:
1.Table for Technical Indicators:
Optionally displays a table showing technical indicators for both symbols.
Includes Stochastic Momentum, RSI (Relative Strength Index), and MACD (Moving Average Convergence Divergence).
Colors the table cells based on the direction of the indicators.
Users can customize the table's position, text size, and visibility (shwtbl).
Technical Indicators:
1.Stochastic Momentum (StochMoM):
Calculates %K and %D using the Stochastic formula.
Displays StochMoM values and colors cells based on bullish or bearish conditions.
2.Relative Strength Index (RSI):
Computes the RSI values and colors cells based on the direction of the trend.
3.MACD (Moving Average Convergence Divergence):
Calculates MACD and Signal line values.
Displays MACD values and colors cells based on bullish or bearish conditions.
Summary:
This script provides traders with a versatile tool for analyzing price trends, comparing symbols, and viewing key technical indicators. The combination of visual elements on the chart and a detailed table enhances the ability to make informed trading decisions.
This script is provided for educational purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
Engulfing [TradingFinder] Bullish & Bearish CandleStick Pattern🔵 Introduction
The candlestick engulfing pattern is important pattern in technical analysis that can be observed in candlestick charts. This pattern occurs when a complete candle engulfs or "engulfs" the body of a previous candle, meaning that the body of the new candle completely covers the body of the previous candle.
The candlestick engulfing pattern has two types: the bullish engulfing pattern and the bearish engulfing pattern.
• Bullish Engulfing Pattern: This pattern occurs when a market candle opens with a larger and higher body than the previous market candle and completely covers the body of the previous candle. This pattern may indicate the presence of strong buying pressure and a potential change in price direction upwards.
• Bearish Engulfing Pattern: This pattern occurs when a market candle opens with a larger and lower body than the previous market candle and completely covers the body of the previous candle. This pattern may indicate the presence of strong selling pressure and a potential change in price direction downwards.
The candlestick engulfing pattern is usually used as a valid signal for a change in price direction in the market and can enhance a combination of crossover investments and technical analysis. However, it should always be evaluated alongside other indicators and market factors, and counter decisions should be made accordingly.
🔵 Recognition Method
Correct, the candlestick engulfing pattern is one of the important patterns in technical analysis that is typically used as a strong signal for a valid change in price direction in the market. This pattern occurs when a candle (usually in the market) opens with a larger and higher (for bullish engulfing pattern) or lower (for bearish engulfing pattern) body than a previous market candle and completely covers the body of the previous candle.
Example of Bullish Engulfing Pattern:
• First Candle: A bearish (downward) candle with a small red body.
• Second Candle: A bullish (upward) candle with a larger body that completely covers the body of the previous candle.
This pattern may indicate a change in price direction from downward to upward.
Example of Bearish Engulfing Pattern:
• First Candle: A bullish (upward) candle with a small green body.
• Second Candle: A bearish (downward) candle with a larger body that completely covers the body of the previous candle.
This pattern may indicate a change in price direction from upward to downward.
The most important point is that the candlestick engulfing pattern should be carefully considered and always evaluated alongside other market indicators and overall conditions. For example, the engulfing pattern near important support or resistance levels, during significant market command changes, or accompanied by other technical signals can have greater signaling power.
🟣 "Bullish Engulfing" Candle
• The first candle is bullish and the second candle is bearish.
• At the end of a downtrend.
• The closing of the first candle is above the opening of the second candle.
• The high of the first candle is higher than the high of the second candle.
Optimal Condition:
• The closing of the first candle is higher than the high of the second candle.
• More than 80% of the first candle is bullish.
🟣 "Bearish Engulfing" Candle
• The first candle is bearish and the second candle is bullish.
• At the end of an uptrend.
• The closing of the first candle is below the opening of the second candle.
• The low of the first candle is lower than the low of the second candle.
Optimal Condition:
• The closing of the first candle is below the opening of the second candle.
• More than 80% of the first candle is bearish.
🔵 Settings
The "Engulf Filter" option allows the "Optimal Condition" to be executed and will show fewer candlesticks.
🔵 Status
Off: Default mode, showing more identifications.
• Green color indicates optimal "Bullish Engulfing" candles.
• Red color indicates optimal "Bearish Engulfing" candles.
On: By changing the default to "On," the number of identifications decreases and the optimal condition is applied.
• Blue color indicates "Bullish Engulfing" candles.
• Black color indicates "Bearish Engulfing" candles.
🟣 Important Note
"Engulfing" candles are very useful signals in the direction of the overall trend, but we do not expect a suitable movement from "Engulfing" candles against the trend.
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.
Semaphore PlotThe Semaphore Plot V2, crafted by OmegaTools for the TradingView platform, is a sophisticated technical analysis tool designed to offer traders nuanced insights into market dynamics. This closed-source script embodies a novel approach by synthesizing multiple technical analysis methodologies into a coherent analytical framework. This detailed description aims to demystify the operational essence of the Semaphore Plot V2 and elucidate its application in trading scenarios without overstepping into claims of infallibility or price prediction accuracy.
Analytical Foundations and Integration:
At its core, the Semaphore Plot V2 is founded on the integration of several analytical dimensions, each contributing to a comprehensive market overview:
1. Dynamic Trend Analysis: Unlike conventional trend indicators that might rely solely on moving averages, the Semaphore Plot V2 examines the market's direction through a more complex lens. It assesses momentum, utilizing derivatives of price movements to understand the velocity and acceleration of trends. This analysis is deepened by examining the rate of change (ROC), providing a multi-tiered view of how swiftly market conditions are evolving.
2. Volatility Insights: Recognizing volatility as a pivotal component of market behavior, the script incorporates volatility metrics to analyze market conditions. By evaluating historical price ranges and applying statistical models, it aims to gauge the potential for future price fluctuations, thus offering insights into market stability or turbulence without predicting specific movements.
3. Linear Regression and Predictive Analysis: The script utilizes linear regression to analyze price data points over a specified period, offering a statistical basis to understand the trajectory of market trends. This regression analysis is complemented by market momentum indicators, forming a predictive model that suggests potential areas where market activity might concentrate. It's important to note that these "predictions" are not certainties but rather statistically derived zones of interest based on historical data.
4. Market Sentiment and Risk Evaluation: Incorporating an evaluation of market sentiment, the script analyzes trends in trading volume and price action to deduce the prevailing market mood. Risk assessment tools, such as the analysis of statistical deviations and Value at Risk (VaR), are also applied to offer a perspective on the risk associated with current market conditions.
Operational Mechanism:
- By processing the integrated analysis, the script generates semaphore signals which are plotted on the trading chart. These signals are not direct buy or sell signals but are designed to highlight areas where, based on the script’s complex analysis, market activity might see significant developments.
- Additionally, the Semaphore Plot V2 features an information table that provides a retrospective analysis of the signals' alignment with market movements, offering traders a tool to assess the script's historical context.
Application and Utility:
- Traders can leverage the Semaphore Plot V2 by applying it to their TradingView charts and adjusting input settings such as lookback periods and sensitivity according to their preferences.
- The semaphore signals serve as markers for areas of potential interest. Traders are encouraged to interpret these signals within the context of their overall market analysis, incorporating other fundamental and technical analysis tools as necessary.
- The informational table serves as a resource for evaluating the historical context of the signals, providing an additional layer of insight for informed decision-making.
The Essence of Originality:
The Semaphore Plot V2 distinguishes itself through the innovative melding of traditional technical analysis components into a unique analytical concoction. This originality lies not in the creation of new technical indicators but in the novel integration and application of existing methodologies to offer a holistic view of market conditions.
Responsible Usage Disclaimer:
The financial markets are characterized by uncertainty, and the Semaphore Plot V2 is intended to serve as an analytical tool within a trader's arsenal, not a standalone solution for trading decisions. It is critical for users to understand that the script does not guarantee trading success nor does it claim to predict exact price movements. Traders should employ the Semaphore Plot V2 alongside comprehensive market analysis and sound risk management practices, acknowledging that past performance is not indicative of future results and that trading involves the risk of loss.
Machine Learning: Multiple Logistic Regression
Multiple Logistic Regression Indicator
The Logistic Regression Indicator for TradingView is a versatile tool that employs multiple logistic regression based on various technical indicators to generate potential buy and sell signals. By utilizing key indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend, the indicator aims to provide a systematic approach to decision-making in financial markets.
How It Works:
Technical Indicators:
The script uses multiple technical indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend as input variables for the logistic regression model.
These indicators are normalized to create categorical variables, providing a consistent scale for the model.
Logistic Regression:
The logistic regression function is applied to the normalized input variables (x1 to x6) with user-defined coefficients (b0 to b6).
The logistic regression model predicts the probability of a binary outcome, with values closer to 1 indicating a bullish signal and values closer to 0 indicating a bearish signal.
Loss Function (Cross-Entropy Loss):
The cross-entropy loss function is calculated to quantify the difference between the predicted probability and the actual outcome.
The goal is to minimize this loss, which essentially measures the model's accuracy.
// Error Function (cross-entropy loss)
loss(y, p) =>
-y * math.log(p) - (1 - y) * math.log(1 - p)
// y - depended variable
// p - multiple logistic regression
Gradient Descent:
Gradient descent is an optimization algorithm used to minimize the loss function by adjusting the weights of the logistic regression model.
The script iteratively updates the weights (b1 to b6) based on the negative gradient of the loss function with respect to each weight.
// Adjusting model weights using gradient descent
b1 -= lr * (p + loss) * x1
b2 -= lr * (p + loss) * x2
b3 -= lr * (p + loss) * x3
b4 -= lr * (p + loss) * x4
b5 -= lr * (p + loss) * x5
b6 -= lr * (p + loss) * x6
// lr - learning rate or step of learning
// p - multiple logistic regression
// x_n - variables
Learning Rate:
The learning rate (lr) determines the step size in the weight adjustment process. It prevents the algorithm from overshooting the minimum of the loss function.
Users can set the learning rate to control the speed and stability of the optimization process.
Visualization:
The script visualizes the output of the logistic regression model by coloring the SMA.
Arrows are plotted at crossover and crossunder points, indicating potential buy and sell signals.
Lables are showing logistic regression values from 1 to 0 above and below bars
Table Display:
A table is displayed on the chart, providing real-time information about the input variables, their values, and the learned coefficients.
This allows traders to monitor the model's interpretation of the technical indicators and observe how the coefficients change over time.
How to Use:
Parameter Adjustment:
Users can adjust the length of technical indicators (rsi_length, cci_length, etc.) and the Z score length based on their preference and market characteristics.
Set the initial values for the regression coefficients (b0 to b6) and the learning rate (lr) according to your trading strategy.
Signal Interpretation:
Buy signals are indicated by an upward arrow (▲), and sell signals are indicated by a downward arrow (▼).
The color-coded SMA provides a visual representation of the logistic regression output by color.
Table Information:
Monitor the table for real-time information on the input variables, their values, and the learned coefficients.
Keep an eye on the learning rate to ensure a balance between model adjustment speed and stability.
Backtesting and Validation:
Before using the script in live trading, conduct thorough backtesting to evaluate its performance under different market conditions.
Validate the model against historical data to ensure its reliability.
WaveTrend Ribbon [AlgoAlpha]🌟🚀 Introducing the WaveTrend Ribbon by AlgoAlpha - Your Next-Level Trading Companion! 🚀🌟
Dive into the world of advanced trading with the WaveTrend Ribbon by AlgoAlpha, a cutting-edge indicator designed to elevate your trading strategy on TradingView. 📈💡 This powerful tool combines the efficiency of the WaveTrend oscillator with innovative Z-score analysis to offer clear, actionable trading signals. 🌊🎯
Key Features:
🔧 Customizable Parameters: Tailor the indicator to your trading needs with adjustable settings including Channel Length, Average Length, Overbought/Oversold Levels, and more.
📊 WaveTrend Oscillator: Utilizes a smoothed version of the average price to identify potential market reversals.
📉 Z-Score Analysis: Enhances signal reliability by measuring the standard deviation of the current price from the mean.
🎨 Dynamic Color Coding: Visual cues shift between up and down colors to indicate market trends, making it easy to read at a glance.
⚠️ Divergence Detection: Automatic identification of bullish and bearish divergences for early signal warnings.
🔔 Custom Alerts: Stay ahead with real-time alerts for key trading events like bullish/bearish divergences and trend reversals.
How to Use WaveTrend Ribbon :
Maximize your trading potential with the WaveTrend Ribbon by following these simple steps:
🔍 Add to Chart: Locate "WaveTrend Ribbon " in TradingView's Indicators & Strategies and apply it to your chart. Dive into the settings to customize the parameters like Channel Length, Average Length, and the Overbought/Oversold levels to match your trading strategy.
- Channel Length affects the sensitivity of the WaveTrend oscillator to price movements. A shorter Channel Length increases responsiveness, useful in volatile markets but may lead to false signals. It's ideal for traders looking for quick reactions to price changes.
- Average Length is used to smooth the oscillator further, influencing how quickly the indicator responds to trend changes. A shorter Average Length allows for a quicker response to the oscillator's movements, suitable for short-term trading strategies.
📊 Analyze the Market: Pay close attention to the color transitions and position of the Z-score in relation to its moving average for insights into market direction. Look out for the overbought and oversold conditions for potential reversal points.
🔔 Set Up Alerts: Utilize the built-in alert system to get notified of key events like trend reversals, bullish and bearish divergences, and more, so you can make timely decisions without having to constantly monitor the charts.
Basic Logic Explained:
The WaveTrend Ribbon is an advanced trading indicator that leverages the WaveTrend oscillator, enhanced by Z-score normalization and moving averages for precise market trend analysis. It calculates the average price deviation over a set period (Channel Length), smoothing it with an Average Length to identify trends. Z-score analysis further refines signals by comparing oscillator deviations against its historical performance, highlighting overbought or oversold conditions. The indicator generates signals for potential reversals and market entries/exits, visualized through dynamic color coding and customizable alerts for traders to act upon efficiently. This multi-layered approach provides a deeper insight into market dynamics, offering a blend of trend following and momentum strategies.
By highlighting overbought and oversold conditions with dynamic color changes and providing reversal signals, this indicator is a must-have tool for traders aiming to capitalize on market trends. 📈🚀
Elevate your trading experience with the WaveTrend Ribbon, your go-to indicator for navigating the markets with confidence and precision. Happy trading! 🌟🚀
Adaptive Fisherized Z-scoreHello Fellas,
It's time for a new adaptive fisherized indicator of me, where I apply adaptive length and more on a classic indicator.
Today, I chose the Z-score, also called standard score, as indicator of interest.
Special Features
Advanced Smoothing: JMA, T3, Hann Window and Super Smoother
Adaptive Length Algorithms: In-Phase Quadrature, Homodyne Discriminator, Median and Hilbert Transform
Inverse Fisher Transform (IFT)
Signals: Enter Long, Enter Short, Exit Long and Exit Short
Bar Coloring: Presents the trade state as bar colors
Band Levels: Changes the band levels
Decision Making
When you create such a mod you need to think about which concepts are the best to conclude. I decided to take Inverse Fisher Transform instead of normalization to make a version which fits to a fixed scale to avoid the usual distortion created by normalization.
Moreover, I chose JMA, T3, Hann Window and Super Smoother, because JMA and T3 are the bleeding-edge MA's at the moment with the best balance of lag and responsiveness. Additionally, I chose Hann Window and Super Smoother because of their extraordinary smoothing capabilities and because Ehlers favours them.
Furthermore, I decided to choose the half length of the dominant cycle instead of the full dominant cycle to make the indicator more responsive which is very important for a signal emitter like Z-score. Signal emitters always need to be faster or have the same speed as the filters they are combined with.
Usage
The Z-score is a low timeframe scalper which works best during choppy/ranging phases. The direction you should trade is determined by the last trend change. E.g. when the last trend change was from bearish market to bullish market and you are now in a choppy/ranging phase confirmed by e.g. Chop Zone or KAMA slope you want to do long trades.
Interpretation
The Z-score indicator is a momentum indicator which shows the number of standard deviations by which the value of a raw score (price/source) is above or below the mean value of what is being observed or measured. Easily explained, it is almost the same as Bollinger Bands with another visual representation form.
Signals
B -> Buy -> Z-score crosses above lower band
S -> Short -> Z-score crosses below upper band
BE -> Buy Exit -> Z-score crosses above 0
SE -> Sell Exit -> Z-score crosses below 0
If you were reading till here, thank you already. Now, follows a bunch of knowledge for people who don't know the concepts I talk about.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Inverse Fisher Transform
The Inverse Fisher Transform is a transform used in DSP to alter the Probability Distribution Function (PDF) of a signal or in our case of indicators.
The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
Hann Window
The Hann function (aka Hann Window) is named after the Austrian meteorologist Julius von Hann. It is a window function used to perform Hann smoothing.
Super Smoother
The Super Smoother uses a special mathematical process for the smoothing of data points.
The Super Smoother is a technical analysis indicator designed to be smoother and with less lag than a traditional moving average.
Adaptive Length
Length based on the dominant cycle length measured by a "dominant cycle measurement" algorithm.
Happy Trading!
Best regards,
simwai
---
Credits to
@cheatcountry
@everget
@loxx
@DasanC
@blackcat1402
Goertzel Adaptive JMA T3Hello Fellas,
The Goertzel Adaptive JMA T3 is a powerful indicator that combines my own created Goertzel adaptive length with Jurik and T3 Moving Averages. The primary intention of the indicator is to demonstrate the new adaptive length algorithm by applying it on bleeding-edge MAs.
It is useable like any moving average, and the new Goertzel adaptive length algorithm can be used to make own indicators Goertzel adaptive.
Used Adaptive Length Algorithms
Normalized Goertzel Power: This uses the normalized power of the Goertzel algorithm to compute an adaptive length without the special operations, like detrending, Ehlers uses for his DFT adaptive length.
Ehlers Mod: This uses the Goertzel algorithm instead of the DFT, originally used by Ehlers, to compute a modified version of his original approach, which sticks as close as possible to the original approach.
Scoring System
The scoring system determines if bars are red or green and collects them.
Then, it goes through all collected red and green bars and checks how big they are and if they are above or below the selected MA. It is positive when green bars are under MA or when red bars are above MA.
Then, it accumulates the size for all positive green bars and for all positive red bars. The same happens for negative green and red bars.
Finally, it calculates the score by ((positiveGreenBars + positiveRedBars) / (negativeGreenBars + negativeRedBars)) * 100 with the scale 0–100.
Signals
Is the price above MA? -> bullish market
Is the price below MA? -> bearish market
Usage
Adjust the settings to reach the highest score, and enjoy an outstanding adaptive MA.
It should be useable on all timeframes. It is recommended to use the indicator on the timeframe where you can get the highest score.
Now, follows a bunch of knowledge for people who don't know about the concepts used here.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Goertzel Algorithm
The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of individual terms of the Discrete Fourier Transform (DFT). It's particularly useful when you need to compute a small number of selected frequency components. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. This makes it more numerically efficient when computing a small number of selected frequency components¹.
Discrete Fourier Transform
The Discrete Fourier Transform (DFT) is a mathematical technique used in signal processing to convert a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency . The DFT provides a frequency domain representation of the original input sequence .
Usage of DFT/Goertzel In Adaptive Length Algorithms
Adaptive length algorithms are automated trading systems that can dynamically adjust their parameters in response to real-time market data. This adaptability enables them to optimize their trading strategies as market conditions fluctuate. Both the Goertzel algorithm and DFT can be used in these algorithms to analyze market data and detect cycles or patterns, which can then be used to adjust the parameters of the trading strategy.
The Goertzel algorithm is more efficient than the DFT when you need to compute a small number of selected frequency components. However, for covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms.
I hope this can help you somehow.
Thanks for reading, and keep it up.
Best regards,
simwai
---
Credits to:
@ClassicScott
@yatrader2
@cheatcountry
@loxx
Seasonality ForecastThe Seasonality Forecast indicator equips TradingView users with a detailed analysis of seasonal price trends, utilizing historical data across daily, weekly, and monthly timeframes. By calculating average price movements over selectable periods up to 10 years, it overlays a seasonal chart on the price chart to elucidate potential trends.
Operational Mechanics
Historical Data Analysis: The indicator processes historical data, calculating average price changes from one bar to the next. This forms the basis of the seasonal chart, offering insights into long-term price movements.
Seasonal Chart Overlay: Adjustments are made to ensure the seasonal chart aligns with the price chart in height, providing a unified view. The de-trending process standardizes each year's data, facilitating direct comparison across time without the influence of overarching price trends.
Customization and Methodology
User Inputs: Traders can tailor the analysis with settings for the lookback period, future projection, and smoothing, aligning the tool with diverse trading strategies.
De-trending and Smoothing: The de-trending method isolates cyclical patterns by removing linear trends, while smoothing techniques reduce data noise, sharpening the focus on meaningful trends.
Pivot Point Analysis: It uses algorithms for detecting pivot points based on historical price actions, signaling potential market turns. This analytical method is crucial for identifying shifts that may indicate future market directions.
Technical Foundations
The Seasonality Forecast indicator leverages known financial analysis techniques to enhance its effectiveness:
Time Series Analysis: Fundamental to the indicator's operation is time series analysis, particularly focusing on cyclical patterns within market data. This approach underpins the seasonal trend analysis, offering a structured view of historical price behavior.
Statistical Smoothing: Smoothing methods, such as moving averages, are applied to the seasonal data to clarify trends by mitigating volatility and short-term fluctuations, making underlying patterns more apparent.
Technical Analysis for Pivot Points: The calculation of pivot points draws on principles of technical analysis, identifying areas where the market's direction has historically shown a tendency to change. This aspect of the tool is instrumental in forecasting potential market movements.
Practical Application
This indicator is invaluable for traders aiming to leverage historical market performance in their analysis, enabling:
Strategic planning based on seasonal patterns, enhancing entry and exit decisions.
Adjusted risk management strategies in anticipation of seasonal volatility.
Identification of potential trend reversals or continuations at pivotal moments in the market cycle.
By integrating historical analysis with technical insights, the Seasonality Forecast indicator provides a nuanced tool for traders looking to deepen their market analysis and refine their trading strategies with a historical perspective.
Supertrend with Target Price & ATREE [SS]Hey everyone,
Releasing this supertrend mashup indicator.
This is your basic supertrend, but with two additions:
1. The integration of the ATREE technical probability modeller; and
2. The use of ATR price targets for crossovers
ATREE
ATREE stands for Advanced Technical Range Expectancy Estimator. It has its very own indicator available here . If you are not that familiar with it, I would suggest heading over to that page and reading about it, because it gives you the in-depth details.
But for a recap, ATREE uses technical indicators such as RSI, Stochastics or Z-Score to predict the likely sentiment, whether it be bullish or bearish. The indicator allows you to select the ATREE model type and supports 3 separate probability models based on either:
1. RSI
2. Stochastics; or
3. Z-Score
If you want to know which model is most effective for the ticker and timeframe you are using, you can launch up the native ATREE indicator and review the backtesting results to ascertain which model performs optimally for that particular ticker on that particular timeframe.
When ATREE assesses the sentiment as bearish, you will get a red fill. When it assesses the sentiment as bullish, you will get a green fill. This will help you adjust your bias to focus on either dip buying or rip shorting.
The ATREE timeframe is also customizable, so you can pull data from higher timeframes than you are on.
ATR Price Targets
As with my EMA 9/21 crossover with the target price, this is essentially the same concept. When the trend shifts to bullish or bearish, bull and bear targets will be printed so you know where to look for potential reversal and you can also set realistic target prices if you are scalping or day trading.
Supertrend
The last and base feature is the supertrend. The supertrend settings are customizeable.
It will provide a green line for uptrend and a redline for downtrend, the basic supertrend functionality.
And that's the indicator!
Let me know what you think and hope you enjoy!
Safe trades as always!
SuperTrend Fisher [AlgoAlpha]🚀🌟 Introducing the "Super Fisher" by AlgoAlpha, a sophisticated and versatile tool crafted for the discerning trader. This innovative indicator merges the precision of the Fisher Transform with the adaptability of the SuperTrend methodology, offering a fresh perspective on market analysis. 📈🔍
Key Features:
🔶 Customizable Settings: Tailor the indicator to your trading style with adjustable inputs like "Fair-value Period" and "EMA Length". Choose your preferred "Up Color" and "Down Color" for a personalized visual experience.
🔶 Advanced Fisher Transform: At the heart of this tool is the Fisher Transform, an algorithm renowned for pinpointing potential price reversals by normalizing asset prices.
🔶 Integrated SuperTrend Functionality: This feature adds a layer of trend analysis, using the refined Fisher Transform values to generate dynamic, trend-following signals.
🔶 Enhanced Visualization: Clearly distinguishable bullish and bearish market phases, thanks to the color-coded plots of Fisher Transform and SuperTrend values.
🔶 Overbought/Oversold Levels: Visual plots and fills for these levels provide additional insights into market extremities.
🔶 Configurable Alerts: Stay informed with alerts for critical market movements like crossing the zero line or the SuperTrend.
Logic:
The "Super Fisher" operates on a sophisticated algorithm:
1. Fisher Transform Calculation: It starts by calculating the Detrended Price Oscillator (DPO) and its standard deviation. These values are then transformed using the Fisher Transform formula, which is subsequently smoothed with a Hull Moving Average.
2. SuperTrend Integration: The SuperTrend function employs the Fisher Transform values to create a dynamic trend-following tool. It calculates upper and lower bands and determines which one to use for market direction based on whether the fisher is above or below the bands, offering an insightful view of the price trend.
3. Overbought/Oversold Identification: The tool plots specific levels to indicate overbought and oversold conditions, aiding in the identification of potential reversal points.
Here's a closer look at the core calculations:
Calculates the Fisher Transform:
value = 0.0
value := round_(.66 * ((src - low_) / (high_ - low_) - .5) + .67 * nz(value ))
fish1 = 0.0
fish1 := .5 * math.log((1 + value) / (1 - value)) + .5 * nz(fish1 )
fish1 := ta.hma(fish1, l)
Calculates the SuperTrend:
supertrend(factor, atrPeriod, srcc) =>
src = srcc
atr = atrr(srcc, atrPeriod)
upperBand = src + factor * atr
lowerBand = src - factor * atr
prevLowerBand = nz(lowerBand )
prevUpperBand = nz(upperBand )
lowerBand := lowerBand > prevLowerBand or srcc < prevLowerBand ? lowerBand : prevLowerBand
upperBand := upperBand < prevUpperBand or srcc > prevUpperBand ? upperBand : prevUpperBand
int direction = na
float superTrend = na
prevSuperTrend = superTrend
if na(atr )
direction := 1
else if prevSuperTrend == prevUpperBand
direction := srcc > upperBand ? -1 : 1
else
direction := srcc < lowerBand ? 1 : -1
superTrend := direction == -1 ? lowerBand : upperBand
How to Use:
📊 To maximize the potential of the "Super Fisher", follow these steps:
1. Customize Settings: Adjust the inputs to match your trading preferences. This includes setting the periods for the Fisher Transform and SuperTrend, as well as choosing colors for better visualization.
2. Analyze the Market: Observe the Fisher Transform and SuperTrend plots to gauge market direction. Pay special attention to color changes, as they indicate shifts in market sentiment.
3. Identify Extremes: Use the overbought and oversold plots to understand potential reversal points.
4. Set Alerts: Utilize the alert functionality to stay informed about significant market movements, ensuring you never miss an opportunity.
🔥 In summary the "Super Fisher" is a comprehensive market analysis tool designed to enhance your trading insights and decision-making process. 📉🌟🚨
AI SuperTrend x Pivot Percentile - Strategy [PresentTrading]█ Introduction and How it is Different
The AI SuperTrend x Pivot Percentile strategy is a sophisticated trading approach that integrates AI-driven analysis with traditional technical indicators. Combining the AI SuperTrend with the Pivot Percentile strategy highlights several key advantages:
1. Enhanced Accuracy in Trend Prediction: The AI SuperTrend utilizes K-Nearest Neighbors (KNN) algorithm for trend prediction, improving accuracy by considering historical data patterns. This is complemented by the Pivot Percentile analysis which provides additional context on trend strength.
2. Comprehensive Market Analysis: The integration offers a multi-faceted approach to market analysis, combining AI insights with traditional technical indicators. This dual approach captures a broader range of market dynamics.
BTC 6H L/S Performance
Local
█ Strategy: How it Works - Detailed Explanation
🔶 AI-Enhanced SuperTrend Indicators
1. SuperTrend Calculation:
- The SuperTrend indicator is calculated using a moving average and the Average True Range (ATR). The basic formula is:
- Upper Band = Moving Average + (Multiplier × ATR)
- Lower Band = Moving Average - (Multiplier × ATR)
- The moving average type (SMA, EMA, WMA, RMA, VWMA) and the length of the moving average and ATR are adjustable parameters.
- The direction of the trend is determined based on the position of the closing price in relation to these bands.
2. AI Integration with K-Nearest Neighbors (KNN):
- The KNN algorithm is applied to predict trend direction. It uses historical price data and SuperTrend values to classify the current trend as bullish or bearish.
- The algorithm calculates the 'distance' between the current data point and historical points. The 'k' nearest data points (neighbors) are identified based on this distance.
- A weighted average of these neighbors' trends (bullish or bearish) is calculated to predict the current trend.
For more please check: Multi-TF AI SuperTrend with ADX - Strategy
🔶 Pivot Percentile Analysis
1. Percentile Calculation:
- This involves calculating the percentile ranks for high and low prices over a set of predefined lengths.
- The percentile function is typically defined as:
- Percentile = Value at (P/100) × (N + 1)th position
- Where P is the desired percentile, and N is the number of data points.
2. Trend Strength Evaluation:
- The calculated percentiles for highs and lows are used to determine the strength of bullish and bearish trends.
- For instance, a high percentile rank in the high prices may indicate a strong bullish trend, and vice versa for bearish trends.
For more please check: Pivot Percentile Trend - Strategy
🔶 Strategy Integration
1. Combining SuperTrend and Pivot Percentile:
- The strategy synthesizes the insights from both AI-enhanced SuperTrend and Pivot Percentile analysis.
- It compares the trend direction indicated by the SuperTrend with the strength of the trend as suggested by the Pivot Percentile analysis.
2. Signal Generation:
- A trading signal is generated when both the AI-enhanced SuperTrend and the Pivot Percentile analysis agree on the trend direction.
- For instance, a bullish signal is generated when both the SuperTrend is bullish, and the Pivot Percentile analysis shows strength in bullish trends.
🔶 Risk Management and Filters
- ADX and DMI Filter: The strategy uses the Average Directional Index (ADX) and the Directional Movement Index (DMI) as filters to assess the trend's strength and direction.
- Dynamic Trailing Stop Loss: Based on the SuperTrend indicator, the strategy dynamically adjusts stop-loss levels to manage risk effectively.
This strategy stands out for its ability to combine real-time AI analysis with established technical indicators, offering traders a nuanced and responsive tool for navigating complex market conditions. The equations and algorithms involved are pivotal in accurately identifying market trends and potential trade opportunities.
█ Usage
To effectively use this strategy, traders should:
1. Understand the AI and Pivot Percentile Indicators: A clear grasp of how these indicators work will enable traders to make informed decisions.
2. Interpret the Signals Accurately: The strategy provides bullish, bearish, and neutral signals. Traders should align these signals with their market analysis and trading goals.
3. Monitor Market Conditions: Given that this strategy is sensitive to market dynamics, continuous monitoring is crucial for timely decision-making.
4. Adjust Settings as Needed: Traders should feel free to tweak the input parameters to suit their trading preferences and to respond to changing market conditions.
█Default Settings and Their Impact on Performance
1. Trading Direction (Default: "Both")
Effect: Determines whether the strategy will take long positions, short positions, or both. Adjusting this setting can align the strategy with the trader's market outlook or risk preference.
2. AI Settings (Neighbors: 3, Data Points: 24)
Neighbors: The number of nearest neighbors in the KNN algorithm. A higher number might smooth out noise but could miss subtle, recent changes. A lower number makes the model more sensitive to recent data but may increase noise.
Data Points: Defines the amount of historical data considered. More data points provide a broader context but may dilute recent trends' impact.
3. SuperTrend Settings (Length: 10, Factor: 3.0, MA Source: "WMA")
Length: Affects the sensitivity of the SuperTrend indicator. A longer length results in a smoother, less sensitive indicator, ideal for long-term trends.
Factor: Determines the bandwidth of the SuperTrend. A higher factor creates wider bands, capturing larger price movements but potentially missing short-term signals.
MA Source: The type of moving average used (e.g., WMA - Weighted Moving Average). Different MA types can affect the trend indicator's responsiveness and smoothness.
4. AI Trend Prediction Settings (Price Trend: 10, Prediction Trend: 80)
Price Trend and Prediction Trend Lengths: These settings define the lengths of weighted moving averages for price and SuperTrend, impacting the responsiveness and smoothness of the AI's trend predictions.
5. Pivot Percentile Settings (Length: 10)
Length: Influences the calculation of pivot percentiles. A shorter length makes the percentile more responsive to recent price changes, while a longer length offers a broader view of price trends.
6. ADX and DMI Settings (ADX Length: 14, Time Frame: 'D')
ADX Length: Defines the period for the Average Directional Index calculation. A longer period results in a smoother ADX line.
Time Frame: Sets the time frame for the ADX and DMI calculations, affecting the sensitivity to market changes.
7. Commission, Slippage, and Initial Capital
These settings relate to transaction costs and initial investment, directly impacting net profitability and strategy feasibility.
Enhanced Candle Sticks [AlgoAlpha]🚀🌟 Introducing the Enhanced Candle Sticks by AlgoAlpha, a Pine Script tool designed to provide traders with an enhanced view of market dynamics through candlestick analysis. This script aims to visualise if price has hit the high or low of the candle first, aiding in back-testing, and to identify smaller trends using market structure.📊🔍
Key Features:
Timeframe Flexibility: Users can select their desired timeframe for analysis, offering a range of options from M15 to H12. This flexibility allows for detailed and specific timeframe analysis.
Micro Trend Identification: The script includes an option to enable 'MicroTrends', giving traders insights into smaller movements and trends within the larger market context.
Customizable Visuals: Traders can customize the colors of bullish and bearish candlesticks, enhancing visual clarity and personalizing the chart to their preferences.
State Tracking: The script tracks the 'state' of the market on lower timeframes to detect if the high or the low was formed first.
Warning System: When the selected timeframe does not match the chart timeframe, the script generates a warning, ensuring accurate analysis and preventing potential misinterpretations.
Usages:
Enhanced Back-testing: Users can now get a more accurate interpretation of the candlesticks by know if the high or the low came first (denoted with ⩚ or ⩛), especially in scenarios where the high and the low of the larger timeframe candle is touching both the take-profit and stop-loss levels.
Squeeze Analysis: Users can identify squeezes in price when the microtrend shows both an uptrend and a downtrend, possibly giving more insight into the market.
Lower Timeframe Market Structure Analysis: Microtrends form when the low of the candle is consecutively increasing and the high is consecutively falling, which means on a lower timeframe, price is forming higher lows or lower highs.
Basic Logic Explanation:
- The script starts by setting up the necessary parameters and importing the required library. Users can customize the timeframe, colors, and whether to enable micro trends and candlestick plotting.
- It then calculates the lower timeframe (1/12th of the current timeframe) for more detailed analysis. The `minutes` function helps in converting the selected timeframe into minutes.
- The script tracks new bars and calculates the highest and lowest values within an hour, using `ta.highestSince` and `ta.lowestSince`.
- It determines the market 'state' by checking if the current high is breaking the previous high and if the current low is breaking the previous low on lower timeframes to determine if the high or the low was formed first.
- The script uses the `plotchar` and `plotcandle` functions to visually represent these trends and states on the chart. This visual representation is key for quick and effective analysis.
Alerts:
Alerts can be set for microtrend formations:
This script is a valuable tool for traders looking to deepen their market analysis with enhanced candlestick visualization and micro trend tracking. 📈🔶💡
Standardized Orderflow [AlgoAlpha]Introducing the Standardized Orderflow indicator by AlgoAlpha. This innovative tool is designed to enhance your trading strategy by providing a detailed analysis of order flow and velocity. Perfect for traders who seek a deeper insight into market dynamics, it's packed with features that cater to various trading styles. 🚀📊
Key Features:
📈 Order Flow Analysis: At its core, the indicator analyzes order flow, distinguishing between bullish and bearish volume within a specified period. It uses a unique standard deviation calculation for normalization, offering a clear view of market sentiment.
🔄 Smoothing Options: Users can opt for a smoothed representation of order flow, using a Hull Moving Average (HMA) for a more refined analysis.
🌪️ Velocity Tracking: The indicator tracks the velocity of order flow changes, providing insights into the market's momentum.
🎨 Customizable Display: Tailor the display mode to focus on either order flow, order velocity, or both, depending on your analysis needs.
🔔 Alerts for Critical Events: Set up alerts for crucial market events like crossover/crossunder of the zero line and overbought/oversold conditions.
How to Use:
1. Setup: Easily configure the indicator to match your trading strategy with customizable input parameters such as order flow period, smoothing length, and moving average types.
2. Interpretation: Watch for bullish and bearish columns in the order flow chart, utilize the Heiken Ashi RSI candle calculation, and look our for reversal notations for additional market insights.
3. Alerts: Stay informed with real-time alerts for key market events.
Code Explanation:
- Order Flow Calculation:
The core of the indicator is the calculation of order flow, which is the sum of volumes for bullish or bearish price movements. This is followed by normalization using standard deviation.
orderFlow = math.sum(close > close ? volume : (close < close ? -volume : 0), orderFlowWindow)
orderFlow := useSmoothing ? ta.hma(orderFlow, smoothingLength) : orderFlow
stdDev = ta.stdev(orderFlow, 45) * 1
normalizedOrderFlow = orderFlow/(stdDev + stdDev)
- Velocity Calculation:
The velocity of order flow changes is calculated using moving averages, providing a dynamic view of market momentum.
velocityDiff = ma((normalizedOrderFlow - ma(normalizedOrderFlow, velocitySignalLength, maTypeInput)) * 10, velocityCalcLength, maTypeInput)
- Display Options:
Users can choose their preferred display mode, focusing on either order flow, order velocity, or both.
orderFlowDisplayCond = displayMode != "Order Velocity" ? display.all : display.none
wideDisplayCond = displayMode != "Order Flow" ? display.all : display.none
- Reversal Indicators and Divergences:
The indicator also includes plots for potential bullish and bearish reversals, as well as regular and hidden divergences, adding depth to your market analysis.
bullishReversalCond = reversalType == "Order Flow" ? ta.crossover(normalizedOrderFlow, -1.5) : (reversalType == "Order Velocity" ? ta.crossover(velocityDiff, -4) : (ta.crossover(velocityDiff, -4) or ta.crossover(normalizedOrderFlow, -1.5)) )
bearishReversalCond = reversalType == "Order Flow" ? ta.crossunder(normalizedOrderFlow, 1.5) : (reversalType == "Order Velocity" ? ta.crossunder(velocityDiff, 4) : (ta.crossunder(velocityDiff, 4) or ta.crossunder(normalizedOrderFlow, 1.5)) )
In summary, the Standardized Orderflow indicator by AlgoAlpha is a versatile tool for traders aiming to enhance their market analysis. Whether you're focused on short-term momentum or long-term trends, this indicator provides valuable insights into market dynamics. 🌟📉📈
Standardized Median Proximity [AlgoAlpha]Introducing the Standardized Median Proximity by AlgoAlpha 🚀📊 – a dynamic tool designed to enhance your trading strategy by analyzing price fluctuations relative to the median value. This indicator is built to provide clear visual cues on the price deviation from its median, allowing for a nuanced understanding of market trends and potential reversals.
🔍 Key Features:
1. 📈 Median Tracking: At the core of this indicator is the calculation of the median price over a specified lookback period. By evaluating the current price against this median, the indicator provides a sense of whether the price is trending above or below its recent median value.
medianValue = ta.median(priceSource, lookbackLength)
2. 🌡️ Normalization of Price Deviation: The deviation of the price from the median is normalized using standard deviation, ensuring that the indicator's readings are consistent and comparable across different time frames and instruments.
standardDeviation = ta.stdev(priceDeviation, 45)
normalizedValue = priceDeviation / (standardDeviation + standardDeviation)
3. 📌 Boundary Calculations: The indicator sets upper and lower boundaries based on the normalized values, helping to identify overbought and oversold conditions.
upperBoundary = ta.ema(positiveValues, lookbackLength) + ta.stdev(positiveValues, lookbackLength) * stdDevMultiplier
lowerBoundary = ta.ema(negativeValues, lookbackLength) - ta.stdev(negativeValues, lookbackLength) * stdDevMultiplier
4. 🎨 Visual Appeal and Clarity: With carefully chosen colors, the plots provide an intuitive and clear representation of market states. Rising trends are indicated in a shade of green, while falling trends are shown in red.
5. 🚨 Alert Conditions: Stay ahead of market movements with customizable alerts for trend shifts and impulse signals, enabling timely decisions.
alertcondition(ta.crossover(normalizedValue, 0), "Bullish Trend Shift", "Median Proximity Crossover Zero Line")
🔧 How to Use:
- 🎯 Set your preferred lookback lengths and standard deviation multipliers to tailor the indicator to your trading style.
- 💹 Utilize the boundary plots to understand potential overbought or oversold conditions.
- 📈 Analyze the color-coded column plots for quick insights into the market's direction relative to the median.
- ⏰ Set alerts to notify you of significant trend changes or conditions that match your trading criteria.
Basic Logic Explained:
- The indicator first calculates the median of the selected price source over your chosen lookback period. This median serves as a baseline for measuring price deviation.
- It then standardizes this deviation by dividing it by the standard deviation of the price deviation over a 45-period lookback, creating a normalized value.
- Upper and lower boundaries are computed using the exponential moving average (EMA) and standard deviation of these normalized values, adjusted by your selected multiplier.
- Finally, color-coded plots provide a visual representation of these calculations, offering at-a-glance insights into market conditions.
Remember, while this tool offers valuable insights, it's crucial to use it as part of a comprehensive trading strategy, complemented by other analysis and indicators. Happy trading!
🚀
Volume-Trend Sentiment (VTS) [AlgoAlpha]Introducing the Volume-Trend Sentiment by AlgoAlpha, a unique tool designed for traders who seek a deeper understanding of market sentiment through volume analysis. This innovative indicator offers a comprehensive view of market dynamics, blending volume trends with price action to provide an insightful perspective on market sentiment. 🚀📊
Key Features:
1. 🌟 Dual Trend Analysis: This indicator combines the concepts of price movement and volume, offering a multi-dimensional view of market sentiment. By analyzing the relationship between the closing and opening prices relative to volume, it provides a nuanced understanding of market dynamics.
2. 🎨 Customizable Settings: Flexibility is at the core of this indicator. Users can adjust various parameters such as the length of the volume trend, standard deviation, and SMA length, ensuring a tailored experience to match individual trading strategies.
3. 🌈 Visual Appeal: With options to display noise, the main plot, and background colors, the indicator is not only informative but also visually engaging. Users can choose their preferred colors for up and down movements, making the analysis more intuitive.
4. ⚠️ Alerts for Key Movements: Stay ahead of market changes with built-in alert conditions. These alerts notify traders when the Volume-Trend Sentiment crosses above or below the midline, signaling potential shifts in market momentum.
How It Works:
The core of the indicator is the calculation of the Volume-Trend Sentiment (VTS). It is computed by subtracting a double-smoothed Exponential Moving Average (EMA) of the price-volume ratio from a single EMA of the same ratio. This method highlights the trend in volume relative to price changes.
volumeTrend = ta.ema((close - open) / volume, volumeTrendLength) - ta.ema(ta.ema((close - open) / volume, volumeTrendLength), volumeTrendLength)
To manage volatility and noise in the volume trend, the indicator employs a standard deviation calculation and a Simple Moving Average (SMA). This smoothing process helps in identifying the true underlying trend by filtering out extreme fluctuations.
standardDeviation = ta.stdev(volumeTrend, standardDeviationLength) * 1
smoothedVolumeTrend = ta.sma(volumeTrend / (standardDeviation + standardDeviation), smaLength)
A unique feature is the dynamic background color, which changes based on the sentiment level. This visual cue instantly communicates the market's bullish or bearish sentiment, enhancing the decision-making process.
getColor(volumeTrendValue) =>
sentimentLevel = math.abs(volumeTrendValue * 10)
baseTransparency = 60 // Base transparency level
colorTransparency = math.max(90 - sentimentLevel * 5, baseTransparency)
volumeTrendValue > 0 ? color.new(upColor, colorTransparency) : color.new(downColor, colorTransparency)
bgcolor(showBackgroundColor ? getColor(smoothedVolumeTrend) : na)
In summary, the Volume-Trend Sentiment by AlgoAlpha is a comprehensive tool that enhances market analysis through a unique blend of volume and price trends. Whether you're a seasoned trader or just starting out, this indicator offers valuable insights into market sentiment and helps in making informed trading decisions. 📈📉🔍🌐
RSI Missmatch(Divergence) OSC. by Neo_ with Missmatch Alert█ Definition
A divergence or missmatch occurs when an asset’s price is moving opposite to a specific technical indicator or is moving in a different direction from other relevant data. The divergence indicator warns traders and technical analysts of changes in a price trend, oftentimes that it is weakening or changing direction.
Divergence or missmatch can be either positive, signifying the possibility of a move that is higher in the asset’s price, or it can be negative, signifying the possibility of a move that is lower in the asset’s price.
█ Takeaways
Divergence or missmatch often works with other indicators and data. It is usually used by technical analysts and traders when the asset’s price is moving counter to the direction of another indicator.
As mentioned above, positive divergence or missmatch indicates that the price could start rising and usually occurs when the price is moving lower, but while another indicator counters this direction by moving higher. In other words, showing bullish signals.
Negative divergence or missmatch indicates that the price could start declining and usually occurs when the price is moving higher, while another indicator moves lower as well. In other words, showing bearish signals.
█ What to look for
Divergence or missmatch is most often used to track and analyze the momentum in an asset’s price and the odds of a price reversal within the current trend. While using divergence, traders and analysts can decide on whether or not they would like to exit the position or set a stop loss in the case the divergence is negative and prices begin to fall.
█ Limitations
It is best to use divergence or missmatch with the aid of other indicators and analysis tools in order to help identify and confirm trend reversals and major market patterns. Divergence should not be relied on by itself to tell you the pertinent information you need to know as an investor. Risk control is key in your analysis and the fact that divergence is not always present in price reversals should definitely be what pushes you to combine it with other tools and indicators.
Additionally, divergence or missmatch can reflect long-term or short-term changes. When making snap decisions, acting on divergence alone could prove detrimental to your trading. Make sure you have other risk factors applied to your charting and general market analysis.
█ What exactly is RSI Missmatches discrepancies using a lookback period in trading?
In trading, lookback period is the number of periods of historical data used for observation and calculation. It is how far into the past the system looks when trying to calculate the variable under consideration. The concept was based on the fact that history can provide information about the future, and my aim was to predict the periods when trend changes would begin within these periods with the RSI oscillator. But this is only true if you're locked back far enough, not locked any further or less!
We already use the idea of looking back in different aspects of our lives, and even in the world of financial trading it can be used in various ways. Of course you will want to learn more about the concept, so in this article we will cover the following topics:
█ What kind of hindsight is this?
The aim here is to check whether trends will change in certain cycles, so we chose the High + Low / 2 formula as the source. Because no matter how much the prices swing up or down, sometimes the rebound can go further. The aim here is to notice the points where the price leaves a needle at the levels where it oscillates and the slowdown in momentum.
█ What does look-back period mean in trade?
To understand what a lookback period means in trading, you need to ask yourself: What is a lookback period in trading? In financial trading, period refers to the duration of a particular trading session. For example, a one-week period means one full week of trading sessions or five trading days. In 5 trading days, the average time is 120 hours in FX markets and 40 hours in stock markets. Regardless of what happens in these cycles, I prefer to choose a time period of 55 periods. Because I noticed that in all the charts I examined, the cycles generally changed during this time period.
█ Let's talk about the meaning of catching Missmatches
As you know, technical indicators are all a mathematical calculation using historical market data (price, volume, or a combination of both). It shows the behavior of the price better and helps in the analysis of price movement. But the indicator can only serve your intended purpose if you get the lookback time right. What we mean here is the setting parameter that determines how much historical data it will use in its calculation. In other words, it is the retrospective review period.
For example, on the RSI indicator you can set this period to 13 periods (default setting) or even 2 periods. The period you choose can determine what the indicator tells you, which in turn determines the strategy you can create with the indicator. The 13- period RSI gives you information about price momentum, so you can effectively use it to create a momentum strategy. On the other hand, the 2-periods RSI can be used to create a mean reversion strategy. To catch any incompatibilities, I set this period to 55 periods. Nothing more, nothing less!
█ Summary
The missmatch indicator helps traders assess changes in the price trend and indicates when price will move with or against the direction of another indicator. It can be either positive or negative, but it is important to note its limitations and that it should be used with other indicators that can also monitor price trends.
We wish you to identify these incompatibilities in the market in the best way possible... Good luck.
█ Tanım
Bir varlığın fiyatı belirli bir teknik göstergenin tersi yönünde hareket ettiğinde veya diğer ilgili verilerden farklı bir yönde hareket ettiğinde bir sapma veya uyumsuzluk meydana gelir. Farklılık göstergesi, tüccarları ve teknik analistleri fiyat eğilimindeki değişiklikler konusunda uyarır; çoğu zaman zayıflıyor veya yön değiştiriyor.
Farklılık veya uyumsuzluk, varlığın fiyatında daha yüksek bir hareket olasılığını işaret ederek pozitif olabilir veya varlığın fiyatında daha düşük bir hareket olasılığını işaret ederek negatif olabilir.
█ Çıkarımlar
Farklılık veya uyumsuzluk çoğu zaman diğer göstergeler ve verilerle de çalışır. Genellikle teknik analistler ve yatırımcılar tarafından varlığın fiyatı başka bir göstergenin yönünün tersine hareket ettiğinde kullanılır.
Yukarıda bahsedildiği gibi pozitif sapma veya uyumsuzluk, fiyatın yükselmeye başlayabileceğini gösterir ve genellikle fiyat düşerken meydana gelir, ancak başka bir gösterge bu yöne yükselerek karşı koyar. Başka bir deyişle yükseliş sinyalleri veriyor.
Negatif sapma veya uyumsuzluk, fiyatın düşmeye başlayabileceğini gösterir ve genellikle fiyat yükselirken başka bir gösterge de düşerken meydana gelir. Başka bir deyişle düşüş sinyalleri veriyor.
█ Nelere bakılmalı
Farklılık veya uyumsuzluk çoğunlukla bir varlığın fiyatındaki momentumu ve mevcut trend içinde fiyatın tersine dönme olasılığını izlemek ve analiz etmek için kullanılır. Farklılaşmayı kullanırken tüccarlar ve analistler, sapmanın negatif olması ve fiyatların düşmeye başlaması durumunda pozisyondan çıkmak isteyip istemeyeceklerine veya zararı durdurma kararı verip veremeyeceklerine karar verebilirler.
█ Sınırlamalar
Trend dönüşlerini ve ana piyasa modellerini tanımlamaya ve doğrulamaya yardımcı olmak için diğer göstergeler ve analiz araçlarının yardımıyla sapmayı veya uyumsuzluğu kullanmak en iyisidir. Bir yatırımcı olarak bilmeniz gereken ilgili bilgileri size söylemesi için farklılığa tek başına güvenilmemelidir. Risk kontrolü analizinizin anahtarıdır ve fiyat dönüşlerinde farklılığın her zaman mevcut olmaması gerçeği kesinlikle sizi onu diğer araç ve göstergelerle birleştirmeye iten şey olmalıdır.
Ek olarak, farklılık veya uyumsuzluk uzun vadeli veya kısa vadeli değişiklikleri yansıtabilir. Ani kararlar verirken yalnızca farklılıklara göre hareket etmek ticaretinize zarar verebilir. Grafiğinize ve genel piyasa analizinize başka risk faktörlerinin uygulandığından emin olun.
█ Ticarette yeniden inceleme dönemi kullanan RSI Missmatches tutarsızlıkları tam olarak nedir?
Ticarette yeniden inceleme süresi, gözlem ve hesaplama için kullanılan geçmiş verilerin dönemlerinin sayısıdır. Söz konusu değişkeni hesaplamaya çalışırken sistemin ne kadar geçmişe baktığıdır. Konsept tarihin geleceğe dair bilgi verebileceği gerçeği üzerine kuruluydu ve amacım RSI osilatörü ile bu dönemler içerisinde trend değişimlerinin başlayacağı dönemleri tahmin etmekti. Ancak bu yalnızca yeterince geriye kilitlenmişseniz geçerlidir, daha fazla veya daha az kilitlenmemişseniz!
Geriye bakma fikrini hayatımızın farklı yönlerinde zaten kullanıyoruz ve hatta finansal ticaret dünyasında bile bu fikir çeşitli şekillerde kullanılabilir. Elbette konsept hakkında daha fazla bilgi edinmek isteyeceksiniz, bu nedenle bu yazıda aşağıdaki konuları ele alacağız:
█ Bu nasıl bir sonradan görmedir?
Burada amaç belli döngülerde trendlerin değişip değişmeyeceğini kontrol etmek olduğundan kaynak olarak Yüksek + Düşük / 2 formülünü seçtik. Çünkü fiyatlar ne kadar yukarı veya aşağı hareket ederse etsin bazen toparlanma daha da ileri gidebiliyor. Burada amaç fiyatın salınım yaptığı seviyelerde iğne bıraktığı noktaları ve momentumdaki yavaşlamayı fark etmektir.
█ Ticarette geriye bakma süresi ne anlama geliyor?
Ticarette yeniden inceleme süresinin ne anlama geldiğini anlamak için kendinize şu soruyu sormanız gerekir: Ticarette yeniden inceleme süresi nedir? Finansal ticarette dönem, belirli bir ticaret seansının süresini ifade eder. Örneğin, bir haftalık dönem, bir tam haftalık işlem seansı veya beş işlem günü anlamına gelir. 5 işlem gününde ortalama süre döviz piyasalarında 120 saat, borsalarda ise 40 saattir. Bu döngülerde ne olursa olsun 55 periyotluk bir zaman dilimini seçmeyi tercih ediyorum. Çünkü incelediğim tüm grafiklerde bu zaman diliminde döngülerin genel olarak değiştiğini fark ettim.
█ Kaçak Eşleşmeleri yakalamanın anlamı hakkında konuşalım
Bildiğiniz gibi teknik göstergeler, geçmiş piyasa verileri (fiyat, hacim veya her ikisinin birleşimi) kullanılarak yapılan matematiksel hesaplamalardır. Fiyatın davranışını daha iyi gösterir ve fiyat hareketinin analizine yardımcı olur. Ancak gösterge yalnızca yeniden inceleme süresini doğru yaparsanız amacınıza hizmet edebilir. Burada kast ettiğimiz, hesaplamasında ne kadar geçmiş veri kullanacağını belirleyen ayar parametresidir. Bir başka deyişle geriye dönük inceleme dönemidir.
Örneğin RSI göstergesinde bu süreyi 13 döneme (varsayılan ayar) ve hatta 2 döneme ayarlayabilirsiniz. Seçeceğiniz dönem, göstergenin size ne söyleyeceğini belirleyebilir ve bu da gösterge ile oluşturabileceğiniz stratejiyi belirler. 13 dönemlik RSI size fiyat momentumu hakkında bilgi verir, böylece onu bir momentum stratejisi oluşturmak için etkili bir şekilde kullanabilirsiniz. Öte yandan, ortalamaya dönüş stratejisi oluşturmak için 2 dönemlik RSI kullanılabilir. Herhangi bir uyumsuzluğu yakalamak için bu periyodu 55 periyoda ayarladım. Ne fazla ne eksik!
█ Özet
Uyumsuzluk göstergesi, yatırımcıların fiyat eğilimindeki değişiklikleri değerlendirmesine yardımcı olur ve fiyatın ne zaman başka bir göstergenin yönüne göre veya ona karşı hareket edeceğini gösterir. Olumlu ya da olumsuz olabilir, ancak sınırlamalarına dikkat etmek ve fiyat eğilimlerini de izleyebilecek diğer göstergelerle birlikte kullanılması gerektiğini unutmamak önemlidir.
Piyasadaki bu uyumsuzlukları en iyi şekilde tespit etmenizi dileriz... Bol Kazançlar.
Median Proximity Percentile [AlgoAlpha]📊🚀 Introducing the "Median Proximity Percentile" by AlgoAlpha, a dynamic and sophisticated trading indicator designed to enhance your market analysis! This tool efficiently tracks median price proximity over a specified lookback period and finds it's percentile between 2 dynamic standard deviation bands, offering valuable insights for traders looking to make informed decisions.
🌟 Key Features:
Color-Coded Visuals: Easily interpret market trends with color-coded plots indicating bullish or bearish signals.
Flexibility: Customize the indicator with your preferred price source and lookback lengths to suit your trading strategy.
Advanced Alert System: Stay ahead with customizable alerts for key trend shifts and market conditions.
🔍 Deep Dive into the Code:
Choose your preferred price data source and define lookback lengths for median and EMA calculations. priceSource = input.source(close, "Source") and lookbackLength = input.int(21, minval = 1, title = "Lookback Length")
Calculate median value, price deviation, and normalized value to analyze market position relative to the median. medianValue = ta.median(priceSource, lookbackLength)
Determine upper and lower boundaries based on standard deviation and EMA. upperBoundary = ta.ema(positiveValues, lookbackLength) + ta.stdev(positiveValues, lookbackLength) * stdDevMultiplier
lowerBoundary = ta.ema(negativeValues, lookbackLength) - ta.stdev(negativeValues, lookbackLength) * stdDevMultiplier
Compute the percentile value to track market position within these boundaries. percentileValue = 100 * (normalizedValue - lowerBoundary)/(upperBoundary - lowerBoundary) - 50
Enhance your analysis with Hull Moving Average (HMA) for smoother trend identification. emaValue = ta.hma(percentileValue, emaLookbackLength)
Visualize trends with color-coded plots and characters for easy interpretation. plotColor = percentileValue > 0 ? colorUp : percentileValue < 0 ? colorDown : na
Set up advanced alerts to stay informed about significant market movements. // Alerts
alertcondition(ta.crossover(emaValue, 0), "Bullish Trend Shift", "Median Proximity Percentile Crossover Zero Line")
alertcondition(ta.crossunder(emaValue, 0), "Bearish Trend Shift", "Median Proximity Percentile Crossunder Zero Line")
alertcondition(ta.crossunder(emaValue,emaValue ) and emaValue > 90, "Bearish Reversal", "Median Proximity Percentile Bearish Reversal")
alertcondition(ta.crossunder(emaValue ,emaValue) and emaValue < -90, "Bullish Reversal", "Median Proximity Percentile Bullish Reversal")
🚨 Remember, the "Median Proximity Percentile " is a tool to aid your analysis. It’s essential to combine it with other analysis techniques and market understanding for best results. Happy trading! 📈📉
Fibonacci Averages Trend OscillatorOverview:
The Fibonacci Averages Trend Oscillator is a unique technical indicator that leverages Fibonacci numbers to analyze market trends. It calculates the average trend sentiment over periods determined by Fibonacci numbers and smooths the result to create an oscillator.
Key Features:
Uses Fibonacci sequences for trend analysis.
Smooths the trend data to create a clear oscillator.
Offers adjustable oversold and overbought levels for customized analysis.
Inputs:
Max Fib Number: Select the highest Fibonacci number for trend calculation.
Smooth: Adjust the smoothness of the oscillator line.
Using the Oscillator:
A rising oscillator indicates a bullish trend, while a falling oscillator suggests bearish sentiment.
Oversold and overbought levels help identify potential reversal points.
Use the oscillator in conjunction with other indicators for comprehensive market analysis.
Tips for Effective Use:
Adjusting Fibonacci Levels: Experiment with different 'Max Fib Number' settings to find the one that best matches your trading style and the asset's characteristics. Higher Fibonacci numbers consider longer periods, which might be more suitable for long-term trend analysis.
Smoothing Level: The 'Smooth' input helps in reducing noise. A higher smooth level results in a less responsive but smoother line, which can be useful for identifying the overall trend direction.
Interpreting Overbought/Oversold: Watch for the oscillator reaching overbought or oversold levels. These points could signal potential trend reversals or consolidation phases.
Combination with Other Tools: For best results, combine the Fibonacci Averages Trend Oscillator with other technical tools like moving averages, RSI, or MACD to validate the signals and develop a robust trading strategy.
Conclusion:
The Fibonacci Averages Trend Oscillator offers a unique approach to trend analysis by incorporating Fibonacci numbers into its calculation. Its adjustable settings allow for customization to fit various trading styles and market conditions, making it a versatile tool for traders seeking to enhance their technical analysis capabilities.
Intraday Fibonacci Levels [Gorb]Welcome to the Intraday Fibonacci Levels indicator, a dynamic and customizable tool designed for traders who incorporate Fibonacci retracements and extensions into their technical analysis. This indicator focuses on intraday price action, allowing you to select a specific candle from any trading session and visualize crucial Fibonacci levels derived from its high and low prices.
Features:
Candle Selection for Fibonacci Analysis: Select any candle based on its time to plot Fibonacci levels, giving you control over the period of analysis.
Customizable .236 and .618 Fibonacci Levels: Plot and customize the .236 and .618 Fibonacci retracement and extension levels. Adjust color and line style for each level to fit your chart preferences.
User-Friendly Interface: Easy-to-use input fields for selecting the candle time and configuring Fibonacci settings. Intuitive toggle options to display or hide specific Fibonacci levels.
Dynamic Updating: The indicator updates in real-time as new price data is received, ensuring you have the latest Fibonacci levels on your chart.
Usage Case:
This indicator is ideal for day traders and technical analysts who utilize Fibonacci tools to identify potential support and resistance levels, trend reversals, or continuation patterns. Whether you are analyzing a high-impact news event candle or a significant pivot point in the trading day, this indicator helps bring clarity to your Fibonacci-based trading strategy.
Guidelines for Usage:
Use the indicator settings to select the candle time and configure your desired Fibonacci levels. You can choose any time you want, in order to use premarket candles you need to enable electronic hours on your chart to have levels plot from pre/post.
These auto plotted levels can act as pivot points or points of support and resistance for traders to make informed trading decisions near these levels. Analyze the plotted Fibonacci levels in conjunction with other technical indicators or price action patterns for informed trading decisions.
Conclusion:
This indicator is a versatile and essential tool for traders who rely on Fibonacci analysis as part of their trading arsenal. By offering the flexibility to analyze specific candles and customize Fibonacci levels, this indicator empowers you to make more informed trading decisions. Its user-friendly design and real-time updating capabilities ensure that you have access to accurate and relevant Fibonacci data at any moment of the trading day.
RISK DISCLAIMER
All content, tools, scripts & education provided by Monstanzer or Gorb Algo are for informational & educational purposes only. Trading is risk and most lose their money, past performance does not guarantee future results.
