Smart Money Breakouts [iskess 01-02 11:05]This is an big update to the excellent Smart Money Breakout Script published in Oct 2023 by ChartPrime who, to my knowledge, was the original author.
FULL CREDIT GOES TO CHARTPRIME FOR THIS ORIGINAL WORK.
Per the moderator's rules, you will find below a meaningful, detailed self-contained description that does not rely on delegation to the open source code or links to other content. You will find in the description details on what the script does, how it does that, how to use it, and how it is original.
The "Smart Money Breakouts" indicator is designed to identify breakouts based on changes in character (CHOCH) or breaks of structure (BOS) patterns, facilitating automated trading with user-defined Take Profit (TP) level.
The indicator incorporates essential elements such as volume analysis and a data table to assist traders in optimizing their strategies.
🔸Breakout Detection:
The indicator scans price movements for "Change in Character" (CHOCH) and "Break of Structure" (BOS) patterns, signaling potential breakout opportunities in the market.
🔸User-Defined TP/SL :
Traders can customize the Take Profit (TP) and Stop Loss (SL) through the indicator settings, with these levels dynamically calculated based on the Average True Range (ATR). This allows for precise risk management and profit targets that adapt to market volatility. Traders can also select the lookback period for the TP/SL calculations.
🔸Volume Analysis and Trade Direction Specific Analysis:
The indicator includes a volume checker that provides valuable insights into the strength of the breakout, taking into account trade direction.
🔸If the volume label is red and the trade is long, it suggests a higher likelihood of hitting the Stop Loss (SL).
🔸If the volume label is green and the trade is long, it indicates a higher probability of hitting the Take Profit (TP).
🔸For short trades, a red volume label suggests a higher likelihood of hitting TP, while a green label suggests a higher likelihood of hitting SL.
🔸A yellow volume label suggests that the volume is inconclusive, neither favoring bullish nor bearish movements.
🔸Data Table:
The indicator features a data table that keeps track of the number of winning and losing trades for specific timeframes or configurations. It also shows the percentage of profits vs losses, and the overall profit/loss for the selected lookback period.
This table serves as a valuable tool for traders to analyze performance and discover optimal settings and timeframes.
The "Smart Money Breakouts" indicator provides traders with a comprehensive solution for breakout trading, combining technical analysis of changes in character and breaks of structure, volume insights, and performance tracking while dynamically adjusting TP and SL levels based on market volatility through the ATR.
This version of the script is a "significant improvement" from Chart Prime's original work in the following ways:
- A selectable range of candles for the profit/loss calculations to look back on.
- An updated table that includes the percentage of wins/losses, and and overall P&L during the selected lookback range.
- The user can now select only Long trades, Short trades, or both.
- The percentage gain/loss is now indicated for every trade on the chart.
- The user can now select a different multiplier for Stop Loss or Take Profit thresholds.
Cari dalam skrip untuk "中海达+股票走势+2023年5月"
4-Year Cycles [jpkxyz]Overview of the Script
I wanted to write a script that encompasses the wide-spread macro fund manager investment thesis: "Crypto is simply and expression of macro." A thesis pioneered by the likes of Raoul Pal (EXPAAM) , Andreesen Horowitz (A16Z) , Joe McCann (ASYMETRIC) , Bob Loukas and many more.
Cycle Theory Background:
The 2007-2008 financial crisis transformed central bank monetary policy by introducing:
- Quantitative Easing (QE): Creating money to buy assets and inject liquidity
- Coordinated global monetary interventions
Proactive 4-year economic cycles characterised by:
- Expansionary periods (low rates, money creation)
- Followed by contraction/normalisation
Central banks now deliberately manipulate liquidity, interest rates, and asset prices to control economic cycles, using monetary policy as a precision tool rather than a blunt instrument.
Cycle Characteristics (based on historical cycles):
- A cycle has 4 seasons (Spring, Summer, Fall, Winter)
- Each season with a cycle lasts 365 days
- The Cycle Low happens towards the beginning of the Spring Season of each new cycle
- This is followed by a run up throughout the Spring and Summer Season
- The Cycle High happens towards the end of the Fall Season
- The Winter season is characterised by price corrections until establishing a new floor in the Spring of the next cycle
Key Functionalities
1. Cycle Tracking
- Divides market history into 4-year cycles (Spring, Summer, Fall, Winter)
- Starts tracking cycles from 2011 (first cycle after the 2007 crisis cycle)
- Identifies and marks cycle boundaries
2. Visualization
- Colors background based on current cycle season
- Draws lines connecting:
- Cycle highs and lows
- Inter-cycle price movements
- Adds labels showing:
- Percentage gains/losses between cycles
- Number of days between significant points
3. Customization Options
- Allows users to customize:
- Colors for each season
- Line and label colors
- Label size
- Background opacity
Detailed Mechanism
Cycle Identification
- Uses a modulo calculation to determine the current season in the 4-year cycle
- Preset boundary years include 2015, 2019, 2023, 2027
- Automatically tracks and marks cycle transitions
Price Analysis
- Tracks highest and lowest prices within each cycle
- Calculates percentage changes:
- Intra-cycle (low to high)
- Inter-cycle (previous high to current high/low)
Visualization Techniques
- Background color changes based on current cycle season
- Dashed and solid lines connect significant price points
- Labels provide quantitative insights about price movements
Unique Aspects
1. Predictive Cycle Framework: Provides a structured way to view market movements beyond traditional technical analysis
2. Seasonal Color Coding: Intuitive visual representation of market cycle stages
3. Comprehensive Price Tracking: Captures both intra-cycle and inter-cycle price dynamics
4. Highly Customizable: Users can adjust visual parameters to suit their preferences
Potential Use Cases
- Technical analysis for long-term investors
- Identifying market cycle patterns
- Understanding historical price movement rhythms
- Educational tool for market cycle theory
Limitations/Considerations
- Based on a predefined 4-year cycle model (Liquidity Cycles)
- Historic Cycle Structures are not an indication for future performance
- May not perfectly represent all market behavior
- Requires visual interpretation
This script is particularly interesting for investors who believe in cyclical market theories and want a visual, data-driven representation of market stages.
McRib Bull Market Indicator# McRib Bull Market Indicator
## Overview
The McRib Bull Market Indicator is a unique technical analysis tool that marks McDonald's McRib sandwich release dates on your trading charts. While seemingly unconventional, this indicator serves as a fascinating historical reference point for market analysis, particularly for studying periods of market expansion.
## Key Features
- Visual yellow labels marking verified McRib release dates from 2012 to 2024
- Clean, unobtrusive design that overlays on any chart timeframe
- Covers both U.S. and international releases (including UK and Australia)
## Historical Reference Points
The indicator includes release dates from:
- December 2012
- October-December 2014
- January 2015
- October 2016
- November 2017
- October 2018
- October 2019
- December 2020
- October 2022
- November 2023
- December 2024
## Usage Guide
1. Add the indicator to any chart by searching for "McRib Bull Market Indicator"
2. The indicator will automatically display yellow labels above price candles on McRib release dates
3. Use these reference points to:
- Analyze market conditions during McRib releases
- Study potential correlations between releases and market movements
- Compare market behavior across different McRib release periods
- Identify any patterns in market expansion phases coinciding with releases
## Trading Application
While initially created as a novelty indicator, it can be used to:
- Mark specific historical points of reference for broader market analysis
- Study potential market psychology around major promotional events
- Compare seasonal market patterns with recurring release dates
- Analyze market expansion phases that coincide with releases
Remember: While this indicator provides interesting historical reference points, it should be used as part of a comprehensive trading strategy rather than as a standalone trading signal.
Historical Fed Interest rate This script is Historical Fed Interest rate
The data is between 1991 - 2023 , but for some reason data between 1991 - 10/2001 is not work
Green line for rate cut and Red line for rate hike and detail at the label
Bull Market Drawdowns V1.0 [ADRIDEM]Bull Market Drawdowns V1.0
Overview
The Bull Market Drawdowns V1.0 script is designed to help visualize and analyze drawdowns during a bull market. This script calculates the highest high price from a specified start date, identifies drawdown periods, and plots the drawdown areas on the chart. It also highlights the maximum drawdowns and marks the start of the bull market, providing a clear visual representation of market performance and potential risk periods.
Unique Features of the New Script
Default Timeframe Configuration: Allows users to set a default timeframe for analysis, providing flexibility in adapting the script to different trading strategies and market conditions.
Customizable Bull Market Start Date: Users can define the start date of the bull market, ensuring the script calculates drawdowns from a specific point in time that aligns with their analysis.
Drawdown Calculation and Visualization: Calculates drawdowns from the highest high since the bull market start date and plots the drawdown areas on the chart with distinct color fills for easy identification.
Maximum Drawdown Tracking and Labeling: Tracks the maximum drawdown for each period and places labels on the chart to indicate significant drawdowns, helping traders identify and assess periods of higher risk.
Bull Market Start Marker: Marks the start of the bull market on the chart with a label, providing a clear reference point for the beginning of the analysis period.
Originality and Usefulness
This script provides a unique and valuable tool by combining drawdown analysis with visual markers and customizable settings. By calculating and plotting drawdowns from a user-defined start date, traders can better understand the performance and risks associated with a bull market. The script’s ability to track and label maximum drawdowns adds further depth to the analysis, making it easier to identify critical periods of market retracement.
Signal Description
The script includes several key visual elements that enhance its usefulness for traders:
Drawdown Area : Plots the upper and lower boundaries of the drawdown area, filling the space between with a semi-transparent color. This helps traders easily identify periods of market retracement.
Maximum Drawdown Labels : Labels are placed on the chart to indicate the maximum drawdown for each period, providing clear markers for significant drawdowns.
Bull Market Start Marker : A label is placed at the start of the bull market, marking the beginning of the analysis period and helping traders contextualize the drawdown data.
These visual elements help quickly assess the extent and impact of drawdowns within a bull market, aiding in risk management and decision-making.
Detailed Description
Input Variables
Default Timeframe (`default_timeframe`) : Defines the timeframe for the analysis. Default is 720 minutes
Bull Market Start Date (`start_date_input`) : The starting date for the bull market analysis. Default is January 1, 2023
Functionality
Highest High Calculation : The script calculates the highest high price on the specified timeframe from the user-defined start date.
```pine
var float highest_high = na
if (time >= start_date)
highest_high := na(highest_high ) ? high : math.max(highest_high , high)
```
Drawdown Calculation : Determines the drawdown starting point and calculates the drawdown percentage from the highest high.
```pine
var float drawdown_start = na
if (time >= start_date)
drawdown_start := na(drawdown_start ) or high >= highest_high ? high : drawdown_start
drawdown = (drawdown_start - low) / drawdown_start * 100
```
Maximum Drawdown Tracking : Tracks the maximum drawdown for each period and places labels above the highest high when a new high is reached.
```pine
var float max_drawdown = na
var int max_drawdown_bar_index = na
if (time >= start_date)
if na(max_drawdown ) or high >= highest_high
if not na(max_drawdown ) and not na(max_drawdown_bar_index) and max_drawdown > 10
label.new(x=max_drawdown_bar_index, y=drawdown_start , text="Max -" + str.tostring(max_drawdown , "#") + "%",
color=color.red, style=label.style_label_down, textcolor=color.white, size=size.normal)
max_drawdown := 0
max_drawdown_bar_index := na
else
if na(max_drawdown ) or drawdown > max_drawdown
max_drawdown := drawdown
max_drawdown_bar_index := bar_index
```
Drawdown Area Plotting : Plots the drawdown area with upper and lower boundaries and fills the area with a semi-transparent color.
```pine
drawdown_area_upper = time >= start_date ? drawdown_start : na
drawdown_area_lower = time >= start_date ? low : na
p1 = plot(drawdown_area_upper, title="Drawdown Area Upper", color=color.rgb(255, 82, 82, 60), linewidth=1)
p2 = plot(drawdown_area_lower, title="Drawdown Area Lower", color=color.rgb(255, 82, 82, 100), linewidth=1)
fill(p1, p2, color=color.new(color.red, 90), title="Drawdown Fill")
```
Current Maximum Drawdown Label : Places a label on the chart to indicate the current maximum drawdown if it exceeds 10%.
```pine
var label current_max_drawdown_label = na
if (not na(max_drawdown) and max_drawdown > 10)
current_max_drawdown_label := label.new(x=bar_index, y=drawdown_start, text="Max -" + str.tostring(max_drawdown, "#") + "%",
color=color.red, style=label.style_label_down, textcolor=color.white, size=size.normal)
if (not na(current_max_drawdown_label))
label.delete(current_max_drawdown_label )
```
Bull Market Start Marker : Places a label at the start of the bull market to mark the beginning of the analysis period.
```pine
var label bull_market_start_label = na
if (time >= start_date and na(bull_market_start_label))
bull_market_start_label := label.new(x=bar_index, y=high, text="Bull Market Start", color=color.blue, style=label.style_label_up, textcolor=color.white, size=size.normal)
```
How to Use
Configuring Inputs : Adjust the default timeframe and start date for the bull market as needed. This allows the script to be tailored to different market conditions and trading strategies.
Interpreting the Indicator : Use the drawdown areas and labels to identify periods of significant market retracement. Pay attention to the maximum drawdown labels to assess the risk during these periods.
Signal Confirmation : Use the bull market start marker to contextualize drawdown data within the overall market trend. The combination of drawdown visualization and maximum drawdown labels helps in making informed trading decisions.
This script provides a detailed view of drawdowns during a bull market, helping traders make more informed decisions by understanding the extent and impact of market retracements. By combining customizable settings with visual markers and drawdown analysis, traders can better align their strategies with the underlying market conditions, thus improving their risk management and decision-making processes.
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.
Bitcoin's Logarithmic ChannelLogarithmic growth is a reasonable way to describe the long term growth of bitcoin's market value: for a network that is experiencing growing adoption and is powered by an asset with a finite and disinflationary supply, it’s natural to expect a more explosive growth of its market capitalization early on, followed by diminishing returns as the network and the asset mature.
I used publicly available data to model the market capitalization of bitcoin, deriving thereform a set of three curves forming a logarithmic growth channel for the market capitalization of bitcoin. Using the time series for the circulating supply, we derive a logarithmic growth channel for the bitcoin price.
Model uses publicly available data from July 17, 2010 to December 31, 2022. Everything since the beginning of 2023 is a prediction.
Past performance is not a guarantee of future results.
SandTigerSandTiger is an auto-counting tool that counts naturally occurring events in a price series. This version has been reduced to 377 lines of code and should run faster than previous versions. Although not shown here, I highly recommend running my 'ELB' script with SandTiger. ELB is an 'event locator' and will mark all points that SandTiger numbers - giving you visual cues as to where these points are located. ELB also displays support/resistance levels.
SandTiger is designed to be used with MAGENTA - a counting system for Forex and other markets.
MAGENTA is a free and open framework for understanding and explaining price movement in financial markets. Any materials associated with MAGENTA are strictly for educational purposes only.
SandTiger tracks Component Values, Dyads, and Sum Table Values (STV's) over straight and curved trends, allowing a trader to discern where directional shifts are likely to occur.
SandTiger requires just 3 things to function accurately:
1) A correct starting point (this will typically be an obvious trend turn high or low in a series of price moves).
2) A 'push 1' count ('push 1' runs from the starting point to the event prior to the first terminal of the first FCT or Fractured Counter-Trend).
3) A 'high prime' value (the high prime count runs from the starting point through to the second terminal of the first FCT with no skips).
FRAMEWORK OVERVIEW: 'Component' values are filtered from the prime set (including the half prime and further reductions). Once we have the comp table we add the values to get a 'total'. With the 'total' we divide and multiply by two to get two additional values. 'Derivatives' are based on various calculations using these three values.
We're looking for 'total/2' to count into either itself, 'total', 'total*2', or a derivative. Comp counts are in Tx form and counted from trend start. If the trend doesn't turn on a comp value it will likely turn on a Dyad or STV value. If that also doesn't happen it's likely you have a 'curved' trend/sequence that will turn on one of the above after moving away from its high/low. This can also be traded using SandTiger's 'Seg Terminals' skip option.
Sum tables and Dyad values are drawn from the 'primes' and Dyads use the 'push1' value as well. In a structural trend, primes are gotten by counting pushpulls 1 & 2 in 'Ti' form. Comps, Sum table values, and Dyads are equivalent, sequences can turn on either value type belonging to the 1st or 2nd prime set. Both STV's and Dyads are counted in 'Tx' form (except where count-through signals occur).
Types and antitypes correlate and are associated with a 12-count 'cycle.' (Ti = 'Terminals Included'; Tx = 'Terminals eXcluded'; both refer to FCT terminals)
THE STRATEGY:
For Structures: Trade Comps, Dyads, and STV's from sets 1 (all) and 2 (Dyads and STV's only) in the 'main' segment then on the 'carry-over' by skipping segment terminals. If a PC or cycle caps the sequence, trade that as well.
For NSM's: Trade movements that flash a signal prior to the end of the initial cycle. The mark will be the push1 value. Twelve will be the 'high prime.' Skip interrupts and trade carry-over values.
The first version of SandTiger was conceived/planned/authored by Erek A.D. and coded by Erek A.D. and @SimpleCryptoLife beginning in August 2022 and finishing in Dec. 2022
The current version was written and developed July 3, 2023 and has been refined and upgraded by Erek A.D. through Jan. 2024...
Measured MoveThis indicator was made for those who look to profit on “Measured Moves.”
Upon opening the settings one will need to set the time to begin (Start Time in settings) the colored background of the potential move areas, and the high (First Price Level in settings) and low (Second Price Level in settings) prices for the measured area for the measured move.
After those are selected they can be easily moved on the chart. I created a table for the user to tap with the pointer to highlight the setting lines for easy adjustment.
Measured moves are used by some algo’s and some traders to determine the take profit levels. They are moves from a particular pattern conclusion to a distance equal to that distance in the desired direction.
This is an image of the measured move which occurred on Dec 13th, 2023 at about 1pm on the ES 1m chart:
The center area in lightly shaded blue is the measured area. The green and red would be the same distance and would equate to the measured move distance.
This example shows the same day – the second move up was a measured move by some traders:
www.tradingview.com
Again, the same day on the way down. This one didn’t quite complete the move:
Again, same day on the way back up – almost perfect:
And, finally, the same day for the last move up:
This indicator will require the user to know what to look for in creating the measured movement. The script is quite simple – but, can be effective in assisting a user to know potential profit targets.
I conducted several searches for “measured move” and found no other indicators that provide this functionality. I understand that one could use fibs to do the same thing – but, I didn’t want to have to alter the fib settings (which I use for actual fibs) to perform this functionality.
Please comment with any questions/suggestions/etc.
TimeFormattingLibraryLibrary "TimeFormattingLibrary"
Time formatting functions: formating functions to make timestrings more human readable friendly (for both fixed time and time-elapsed).
Also functions for last and first instance in month of day of week input.
Also a function for identifying bank holiday Mondays.
timeFormatFxn(showDayOfWeek, showDayOfMonth, showMonth, showYear, showHrMin, _time, _timezone)
converts time into readable format
Parameters:
showDayOfWeek (bool) : if you want to show day of week (i.e. Mon, Tues etc)
showDayOfMonth (bool) : if you want to show day number of month with superscript ordinals (i.e. 1ˢᵗ, 2ⁿᵈ, etc)
showMonth (bool) : if you want to show the month (i.e. Jan, Feb, etc)
showYear (bool) : if you want to show the year (i.e. 2023)
showHrMin (bool) : if you want to show time in 24hr clock format
_time (int) : is the unix time (i.e. time or time_close)
_timezone (string) : the user timezone input as string (e.g. "America/New_York", "UTC-5", "GMT+0530")
Returns: time date string
timeElapsedFxn(timespan)
converts timespan into readable format
Parameters:
timespan (int) : is the length of time in milliseconds to be converted into a human readable string
Returns: timespan string (whether it be a for showing 'time-elapsed' or for showing a 'countdown timer')
isFirstXdayofmonth(_dayofweek)
gives bool result for when first occurence in month of the day-of-week input
Parameters:
_dayofweek (int) : (can be integer 1-7 or can be dayofweek variable; i.e. dayofweek.wednesday)
isLastXdayofmonth(_dayofweek)
gives bool result for when last occurence in month of the day-of-week input
Parameters:
_dayofweek (int) : (can be integer 1-7 or can be dayofweek variable; i.e. dayofweek.wednesday)
wasBankHolidayMonday()
gives bool result for if yesterday was a bank holiday monday. Only for use with with request.security() function, see example code below
Customizable Vertical LinesCustomizable Vertical Lines (CVL) Indicator
Version 2.0 (November 2023)
Description:
The Customizable Vertical Lines (CVL) indicator is a powerful tool for traders who want to mark specific times on their charts with vertical lines. This indicator allows users to customize the appearance of these lines, including their color, line style, and width, providing a flexible and visually intuitive way to highlight key time points.
Key Features:
User-Friendly Inputs: Easily set up to five distinct times, each associated with a unique color for the vertical lines.
Color Customization: Choose from a wide range of colors for each vertical line, allowing for clear differentiation.
Line Style Options: Tailor the appearance of the lines with three selectable styles: solid, dotted, or dashed.
Adjustable Line Width: Modify the thickness of the lines to suit your preferred visual style.
Timezone Offset: Account for different time zones by adjusting the timezone offset parameter.
How to Use:
Simply input the desired times, colors, and other parameters in the script settings. The CVL indicator will then automatically plot vertical lines at the specified times on the chart with the chosen customization.
chrono_utilsLibrary "chrono_utils"
📝 Description
Collection of objects and common functions that are related to datetime windows session days and time ranges. The main purpose of this library is to handle time-related functionality and make it easy to reason about a future bar checking if it will be part of a predefined session and/or inside a datetime window. All existing session functionality I found in the documentation e.g. "not na(time(timeframe, session, timezone))" are not suitable for strategy scripts, since the execution of the orders is delayed by one bar, due to the script execution happening at the bar close. Moreover, a history operator with a negative value that looks forward is not allowed in any pinescript expression. So, a prediction for the next bar using the bars_back argument of "time()"" and "time_close()" was necessary. Thus, I created this library to overcome this small but very important limitation. In the meantime, I added useful functionality to handle session-based behavior. An interesting utility that emerged from this development is data anomaly detection where a comparison between the prediction and the actual value is happening. If those two values are different then a data inconsistency happens between the prediction bar and the actual bar (probably due to a holiday, half session day, a timezone change etc..)
🤔 How to Guide
To use the functionality this library provides in your script you have to import it first!
Copy the import statement of the latest release by pressing the copy button below and then paste it into your script. Give a short name to this library so you can refer to it later on. The import statement should look like this:
import jason5480/chrono_utils/2 as chr
To check if a future bar will be inside a window first of all you have to initialize a DateTimeWindow object.
A code example is the following:
var dateTimeWindow = chr.DateTimeWindow.new().init(fromDateTime = timestamp('01 Jan 2023 00:00'), toDateTime = timestamp('01 Jan 2024 00:00'))
Then you have to "ask" the dateTimeWindow if the future bar defined by an offset (default is 1 that corresponds th the next bar), will be inside that window:
// Filter bars outside of the datetime window
bool dateFilterApproval = dateTimeWindow.is_bar_included()
You can visualize the result by drawing the background of the bars that are outside the given window:
bgcolor(color = dateFilterApproval ? na : color.new(color.fuchsia, 90), offset = 1, title = 'Datetime Window Filter')
In the same way, you can "ask" the Session if the future bar defined by an offset it will be inside that session.
First of all, you should initialize a Session object.
A code example is the following:
var sess = chr.Session.new().from_sess_string(sess = '0800-1700:23456', refTimezone = 'UTC')
Then check if the given bar defined by the offset (default is 1 that corresponds th the next bar), will be inside the session like that:
// Filter bars outside the sessions
bool sessionFilterApproval = view.sess.is_bar_included()
You can visualize the result by drawing the background of the bars that are outside the given session:
bgcolor(color = sessionFilterApproval ? na : color.new(color.red, 90), offset = 1, title = 'Session Filter')
In case you want to visualize multiple session ranges you can create a SessionView object like that:
var view = SessionView.new().init(SessionDays.new().from_sess_string('2345'), array.from(SessionTimeRange.new().from_sess_string('0800-1600'), SessionTimeRange.new().from_sess_string('1300-2200')), array.from('London', 'New York'), array.from(color.blue, color.orange))
and then call the draw method of the SessionView object like that:
view.draw()
🏋️♂️ Please refer to the "EXAMPLE DATETIME WINDOW FILTER" and "EXAMPLE SESSION FILTER" regions of the script for more advanced code examples of how to utilize the full potential of this library, including user input settings and advanced visualization!
⚠️ Caveats
As I mentioned in the description there are some cases that the prediction of the next bar is not accurate. A wrong prediction will affect the outcome of the filtering. The main reasons this could happen are the following:
Public holidays when the market is closed
Half trading days usually before public holidays
Change in the daylight saving time (DST)
A data anomaly of the chart, where there are missing and/or inconsistent data.
A bug in this library (Please report by PM sending the symbol, timeframe, and settings)
Special thanks to @robbatt and @skinra for the constructive feedback 🏆. Without them, the exposed API of this library would be very lengthy and complicated to use. Thanks to them, now the user of this library will be able to get the most, with only a few lines of code!
Noa: Z-distance from VWAP with Kalman Smoother
Title: Noa: Z-distance from VWAP with Kalman Smoother
Description:
The "Z-distance from VWAP with Kalman Smoother" is a tool constructed on the premise that price evolves in distinct stages: normal or extreme trends (upward or downward) and transitional periods, termed as 'flips'. The Volume Weighted Average Price (VWAP) serves as a benchmark, representing the market's expectation of a fair value over a given time frame. However, since each stock trades on its unique price scale, direct comparisons are not feasible. This script introduces a standardized method, using the Z-score from the VWAP, to understand and compare these relationships across diverse scales.
Core Principles:
Stages of Price Movement:
- Prices don't move purely randomly; while they contain a random element, they oscillate in discernible patterns or stages—either maintaining a trend (normal or extreme) or undergoing transition (flip).
- VWAP as Fair Value: VWAP offers a dynamic representation of what the market perceives as fair value for a stock over a specific period.
- Standardizing Price Relations: Given the varied scales at which different stocks trade, a model was imperative to standardize these relations. The Z-score from the VWAP fulfills this role, offering a normalized measure of how far the price deviates from its perceived fair value.
Features:
Z-score Levels:
The indicator demarcates various stages of price movements, offering clarity on potential overbought or oversold conditions.
- Extreme Up Trend: Indicated when the Z-score surpasses the upper limit.
- Normal Up Trend: Represented when the Z-score lies between the flip upper and the upper limit.
- Transition (Flip): Recognized when the Z-score oscillates within the flip range.
- Normal Down Trend: Denoted when the Z-score is between the flip lower and the lower limit.
- Extreme Down Trend: Marked when the Z-score falls below the lower limit.
Visual Aids:
- Color-coded regions between specific Z-score levels and the Z-score plot itself elucidate the current market state.
- Kalman Filter: By incorporating a Kalman filter, the indicator offers a less noisy and smoother representation of the Z-score, enhancing its interpretability.
Usage:
Trend Analysis:
- The Z-score states and the color-coded plot facilitate a nuanced understanding of the prevailing market trend.
- Potential Reversal Points: Extremely positive or negative Z-scores might hint at impending reversals.
- Buy/Sell Signals: Z-score's interactions with the flip level can be interpreted as potential trading signals.
Example (for illustration purposes only):
AAPL since April 2022: The stock exited from a normal uptrend and transitioned potentially towards a downtrend. By the end of April, AAPL flipped twice before transitioning to a normal downtrend. By early May, the stock moved into an aggressive downtrend. Market buyers were able to counter this downtrend by June, but selling pressure persisted, pushing the stock back into an aggressive downtrend. By the end of June, buyers halted the aggressive selling and transitioned the stock from an aggressive to normal downtrend, then to a flip, and finally to a normal uptrend by the end of August. AAPL briefly peaked into an aggressive uptrend before being pressured back to a normal downtrend. The rest of 2022 saw AAPL attempting several short-lived uptrend flips. However, 2023 brought a change, with AAPL flipping into a normal uptrend by the end of January, maintaining it until August of that year.
Credits:
This script, inspired by Z distance from VWAP by LazyBear and Kalman Smoother by alexgrover, was revamped and enriched by nord-ouestadvisors to embed these core principles and heighten its usability. A special acknowledgment to ChatGPT by OpenAI for the guidance.
[Excalibur] Ehlers AutoCorrelation Periodogram ModifiedKeep your coins folks, I don't need them, don't want them. If you wish be generous, I do hope that charitable peoples worldwide with surplus food stocks may consider stocking local food banks before stuffing monetary bank vaults, for the crusade of remedying the needs of less than fortunate children, parents, elderly, homeless veterans, and everyone else who deserves nutritional sustenance for the soul.
DEDICATION:
This script is dedicated to the memory of Nikolai Dmitriyevich Kondratiev (Никола́й Дми́триевич Кондра́тьев) as tribute for being a pioneering economist and statistician, paving the way for modern econometrics by advocation of rigorous and empirical methodologies. One of his most substantial contributions to the study of business cycle theory include a revolutionary hypothesis recognizing the existence of dynamic cycle-like phenomenon inherent to economies that are characterized by distinct phases of expansion, stagnation, recession and recovery, what we now know as "Kondratiev Waves" (K-waves). Kondratiev was one of the first economists to recognize the vital significance of applying quantitative analysis on empirical data to evaluate economic dynamics by means of statistical methods. His understanding was that conceptual models alone were insufficient to adequately interpret real-world economic conditions, and that sophisticated analysis was necessary to better comprehend the nature of trending/cycling economic behaviors. Additionally, he recognized prosperous economic cycles were predominantly driven by a combination of technological innovations and infrastructure investments that resulted in profound implications for economic growth and development.
I will mention this... nation's economies MUST be supported and defended to continuously evolve incrementally in order to flourish in perpetuity OR suffer through eras with lasting ramifications of societal stagnation and implosion.
Analogous to the realm of economics, aperiodic cycles/frequencies, both enduring and ephemeral, do exist in all facets of life, every second of every day. To name a few that any blind man can naturally see are: heartbeat (cardiac cycles), respiration rates, circadian rhythms of sleep, powerful magnetic solar cycles, seasonal cycles, lunar cycles, weather patterns, vegetative growth cycles, and ocean waves. Do not pretend for one second that these basic aforementioned examples do not affect business cycle fluctuations in minuscule and monumental ways hour to hour, day to day, season to season, year to year, and decade to decade in every nation on the planet. Kondratiev's original seminal theories in macroeconomics from nearly a century ago have proven remarkably prescient with many of his antiquated elementary observations/notions/hypotheses in macroeconomics being scholastically studied and topically researched further. Therefore, I am compelled to honor and recognize his statistical insight and foresight.
If only.. Kondratiev could hold a pocket sized computer in the cup of both hands bearing the TradingView logo and platform services, I truly believe he would be amazed in marvelous delight with a GARGANTUAN smile on his face.
INTRODUCTION:
Firstly, this is NOT technically speaking an indicator like most others. I would describe it as an advanced cycle period detector to obtain market data spectral estimates with low latency and moderate frequency resolution. Developers can take advantage of this detector by creating scripts that utilize a "Dominant Cycle Source" input to adaptively govern algorithms. Be forewarned, I would only recommend this for advanced developers, not novice code dabbling. Although, there is some Pine wizardry introduced here for novice Pine enthusiasts to witness and learn from. AI did describe the code into one super-crunched sentence as, "a rare feat of exceptionally formatted code masterfully balancing visual clarity, precision, and complexity to provide immense educational value for both programming newcomers and expert Pine coders alike."
Understand all of the above aforementioned? Buckle up and proceed for a lengthy read of verbose complexity...
This is my enhanced and heavily modified version of autocorrelation periodogram (ACP) for Pine Script v5.0. It was originally devised by the mathemagician John Ehlers for detecting dominant cycles (frequencies) in an asset's price action. I have been sitting on code similar to this for a long time, but I decided to unleash the advanced code with my fashion. Originally Ehlers released this with multiple versions, one in a 2016 TASC article and the other in his last published 2013 book "Cycle Analytics for Traders", chapter 8. He wasn't joking about "concepts of advanced technical trading" and ACP is nowhere near to his most intimidating and ingenious calculations in code. I will say the book goes into many finer details about the original periodogram, so if you wish to delve into even more elaborate info regarding Ehlers' original ACP form AND how you may adapt algorithms, you'll have to obtain one. Note to reader, comparing Ehlers' original code to my chimeric code embracing the "Power of Pine", you will notice they have little resemblance.
What you see is a new species of autocorrelation periodogram combining Ehlers' innovation with my fascinations of what ACP could be in a Pine package. One other intention of this script's code is to pay homage to Ehlers' lifelong works. Like Kondratiev, Ehlers is also a hardcore cycle enthusiast. I intend to carry on the fire Ehlers envisioned and I believe that is literally displayed here as a pleasant "fiery" example endowed with Pine. With that said, I tried to make the code as computationally efficient as possible, without going into dozens of more crazy lines of code to speed things up even more. There's also a few creative modifications I made by making alterations to the originating formulas that I felt were improvements, one of them being lag reduction. By recently questioning every single thing I thought I knew about ACP, combined with the accumulation of my current knowledge base, this is the innovative revision I came up with. I could have improved it more but decided not to mind thrash too many TV members, maybe later...
I am now confident Pine should have adequate overhead left over to attach various indicators to the dominant cycle via input.source(). TV, I apologize in advance if in the future a server cluster combusts into a raging inferno... Coders, be fully prepared to build entire algorithms from pure raw code, because not all of the built-in Pine functions fully support dynamic periods (e.g. length=ANYTHING). Many of them do, as this was requested and granted a while ago, but some functions are just inherently finicky due to implementation combinations and MUST be emulated via raw code. I would imagine some comprehensive library or numerous authored scripts have portions of raw code for Pine built-ins some where on TV if you look diligently enough.
Notice: Unfortunately, I will not provide any integration support into member's projects at all. I have my own projects that require way too much of my day already. While I was refactoring my life (forgoing many other "important" endeavors) in the early half of 2023, I primarily focused on this code over and over in my surplus time. During that same time I was working on other innovations that are far above and beyond what this code is. I hope you understand.
The best way programmatically may be to incorporate this code into your private Pine project directly, after brutal testing of course, but that may be too challenging for many in early development. Being able to see the periodogram is also beneficial, so input sourcing may be the "better" avenue to tether portions of the dominant cycle to algorithms. Unique indication being able to utilize the dominantCycle may be advantageous when tethering this script to those algorithms. The easiest way is to manually set your indicators to what ACP recognizes as the dominant cycle, but that's actually not considered dynamic real time adaption of an indicator. Different indicators may need a proportion of the dominantCycle, say half it's value, while others may need the full value of it. That's up to you to figure that out in practice. Sourcing one or more custom indicators dynamically to one detector's dominantCycle may require code like this: `int sourceDC = int(math.max(6, math.min(49, input.source(close, "Dominant Cycle Source"))))`. Keep in mind, some algos can use a float, while algos with a for loop require an integer.
I have witnessed a few attempts by talented TV members for a Pine based autocorrelation periodogram, but not in this caliber. Trust me, coding ACP is no ordinary task to accomplish in Pine and modifying it blessed with applicable improvements is even more challenging. For over 4 years, I have been slowly improving this code here and there randomly. It is beautiful just like a real flame, but... this one can still burn you! My mind was fried to charcoal black a few times wrestling with it in the distant past. My very first attempt at translating ACP was a month long endeavor because PSv3 simply didn't have arrays back then. Anyways, this is ACP with a newer engine, I hope you enjoy it. Any TV subscriber can utilize this code as they please. If you are capable of sufficiently using it properly, please use it wisely with intended good will. That is all I beg of you.
Lastly, you now see how I have rasterized my Pine with Ehlers' swami-like tech. Yep, this whole time I have been using hline() since PSv3, not plot(). Evidently, plot() still has a deficiency limited to only 32 plots when it comes to creating intense eye candy indicators, the last I checked. The use of hline() is the optimal choice for rasterizing Ehlers styled heatmaps. This does only contain two color schemes of the many I have formerly created, but that's all that is essentially needed for this gizmo. Anything else is generally for a spectacle or seeing how brutal Pine can be color treated. The real hurdle is being able to manipulate colors dynamically with Merlin like capabilities from multiple algo results. That's the true challenging part of these heatmap contraptions to obtain multi-colored "predator vision" level indication. You now have basic hline() food for thought empowerment to wield as you can imaginatively dream in Pine projects.
PERIODOGRAM UTILITY IN REAL WORLD SCENARIOS:
This code is a testament to the abilities that have yet to be fully realized with indication advancements. Periodograms, spectrograms, and heatmaps are a powerful tool with real-world applications in various fields such as financial markets, electrical engineering, astronomy, seismology, and neuro/medical applications. For instance, among these diverse fields, it may help traders and investors identify market cycles/periodicities in financial markets, support engineers in optimizing electrical or acoustic systems, aid astronomers in understanding celestial object attributes, assist seismologists with predicting earthquake risks, help medical researchers with neurological disorder identification, and detection of asymptomatic cardiovascular clotting in the vaxxed via full body thermography. In either field of study, technologies in likeness to periodograms may very well provide us with a better sliver of analysis beyond what was ever formerly invented. Periodograms can identify dominant cycles and frequency components in data, which may provide valuable insights and possibly provide better-informed decisions. By utilizing periodograms within aspects of market analytics, individuals and organizations can potentially refrain from making blinded decisions and leverage data-driven insights instead.
PERIODOGRAM INTERPRETATION:
The periodogram renders the power spectrum of a signal, with the y-axis representing the periodicity (frequencies/wavelengths) and the x-axis representing time. The y-axis is divided into periods, with each elevation representing a period. In this periodogram, the y-axis ranges from 6 at the very bottom to 49 at the top, with intermediate values in between, all indicating the power of the corresponding frequency component by color. The higher the position occurs on the y-axis, the longer the period or lower the frequency. The x-axis of the periodogram represents time and is divided into equal intervals, with each vertical column on the axis corresponding to the time interval when the signal was measured. The most recent values/colors are on the right side.
The intensity of the colors on the periodogram indicate the power level of the corresponding frequency or period. The fire color scheme is distinctly like the heat intensity from any casual flame witnessed in a small fire from a lighter, match, or camp fire. The most intense power would be indicated by the brightest of yellow, while the lowest power would be indicated by the darkest shade of red or just black. By analyzing the pattern of colors across different periods, one may gain insights into the dominant frequency components of the signal and visually identify recurring cycles/patterns of periodicity.
SETTINGS CONFIGURATIONS BRIEFLY EXPLAINED:
Source Options: These settings allow you to choose the data source for the analysis. Using the `Source` selection, you may tether to additional data streams (e.g. close, hlcc4, hl2), which also may include samples from any other indicator. For example, this could be my "Chirped Sine Wave Generator" script found in my member profile. By using the `SineWave` selection, you may analyze a theoretical sinusoidal wave with a user-defined period, something already incorporated into the code. The `SineWave` will be displayed over top of the periodogram.
Roofing Filter Options: These inputs control the range of the passband for ACP to analyze. Ehlers had two versions of his highpass filters for his releases, so I included an option for you to see the obvious difference when performing a comparison of both. You may choose between 1st and 2nd order high-pass filters.
Spectral Controls: These settings control the core functionality of the spectral analysis results. You can adjust the autocorrelation lag, adjust the level of smoothing for Fourier coefficients, and control the contrast/behavior of the heatmap displaying the power spectra. I provided two color schemes by checking or unchecking a checkbox.
Dominant Cycle Options: These settings allow you to customize the various types of dominant cycle values. You can choose between floating-point and integer values, and select the rounding method used to derive the final dominantCycle values. Also, you may control the level of smoothing applied to the dominant cycle values.
DOMINANT CYCLE VALUE SELECTIONS:
External to the acs() function, the code takes a dominant cycle value returned from acs() and changes its numeric form based on a specified type and form chosen within the indicator settings. The dominant cycle value can be represented as an integer or a decimal number, depending on the attached algorithm's requirements. For example, FIR filters will require an integer while many IIR filters can use a float. The float forms can be either rounded, smoothed, or floored. If the resulting value is desired to be an integer, it can be rounded up/down or just be in an integer form, depending on how your algorithm may utilize it.
AUTOCORRELATION SPECTRUM FUNCTION BASICALLY EXPLAINED:
In the beginning of the acs() code, the population of caches for precalculated angular frequency factors and smoothing coefficients occur. By precalculating these factors/coefs only once and then storing them in an array, the indicator can save time and computational resources when performing subsequent calculations that require them later.
In the following code block, the "Calculate AutoCorrelations" is calculated for each period within the passband width. The calculation involves numerous summations of values extracted from the roofing filter. Finally, a correlation values array is populated with the resulting values, which are normalized correlation coefficients.
Moving on to the next block of code, labeled "Decompose Fourier Components", Fourier decomposition is performed on the autocorrelation coefficients. It iterates this time through the applicable period range of 6 to 49, calculating the real and imaginary parts of the Fourier components. Frequencies 6 to 49 are the primary focus of interest for this periodogram. Using the precalculated angular frequency factors, the resulting real and imaginary parts are then utilized to calculate the spectral Fourier components, which are stored in an array for later use.
The next section of code smooths the noise ridden Fourier components between the periods of 6 and 49 with a selected filter. This species also employs numerous SuperSmoothers to condition noisy Fourier components. One of the big differences is Ehlers' versions used basic EMAs in this section of code. I decided to add SuperSmoothers.
The final sections of the acs() code determines the peak power component for normalization and then computes the dominant cycle period from the smoothed Fourier components. It first identifies a single spectral component with the highest power value and then assigns it as the peak power. Next, it normalizes the spectral components using the peak power value as a denominator. It then calculates the average dominant cycle period from the normalized spectral components using Ehlers' "Center of Gravity" calculation. Finally, the function returns the dominant cycle period along with the normalized spectral components for later external use to plot the periodogram.
POST SCRIPT:
Concluding, I have to acknowledge a newly found analyst for assistance that I couldn't receive from anywhere else. For one, Claude doesn't know much about Pine, is unfortunately color blind, and can't even see the Pine reference, but it was able to intuitively shred my code with laser precise realizations. Not only that, formulating and reformulating my description needed crucial finesse applied to it, and I couldn't have provided what you have read here without that artificial insight. Finding the right order of words to convey the complexity of ACP and the elaborate accompanying content was a daunting task. No code in my life has ever absorbed so much time and hard fricking work, than what you witness here, an ACP gem cut pristinely. I'm unveiling my version of ACP for an empowering cause, in the hopes a future global army of code wielders will tether it to highly functional computational contraptions they might possess. Here is ACP fully blessed poetically with the "Power of Pine" in sublime code. ENJOY!
MarketSmith Stochasticversion=5
This version of the stochastic produces the identical stochastic as used in MarketSmith
The three primary differences from a classic stochastic are as follows:
1. Close values only
2. 5-day ema instead of 3-day simple moving averages for smoothing the fast and slow lines
3. Slow and fast lines are truncated to integer values
by Mike Scott
2023-09-11
Dynamic GANN Square Of 9 BandsDynamic GANN Square Of 9 Bands
Created on 3 Sept 2023
Adjust Increment Value:
Customize increment to match symbol and price characteristics for accuracy.
Green Line:
200 EMA. Identifies trend direction; moves with the prevailing trend.
Red Lines:
Mark prominent reversal levels closer to the red range; ideal for mean reversion strategies.
Crossing red levels may indicate trend continuation to the next red level.
Grey Lines:
Show immediate target reversal levels; watch for potential reversals.
Key Features:
Levels are different from Standard Deviation Lines.
Levels remain fixed and parallel, unaffected by volatility.
Despite its dynamism, it can serve as a leading indicator, revealing potential trend changes.
Primarily designed for trend-following strategies.
Additional Tips:
Use additional confirmations
Manage predefined risk and quantity
Additional Resources:
GANN Square Of 9 Pivots:
Velocity Acceleration Indicator [CC]The Velocity Acceleration Indicator was created by Scott Cong (Stocks and Commodities Sep 2023, pgs 8-15). This is another personal variation of his formula designed to capture the overall velocity acceleration of the underlying stock by applying the velocity formula to the original indicator to find the acceleration of the underlying velocity. I changed a few things around and managed actually to get less lag and quicker signals for this version, so make sure you compare the Velocity Indicator script that I published yesterday. This indicator is also visually similar to a typical stochastic indicator but uses a different underlying calculation. This works well as a momentum indicator, and the values are completely unbounded, so the best ways to determine bullish or bearish trends is either by using a crossover or crossunder between the indicator and the midline or to buy or sell the indicator when it reaches a high or low point and starts to fall or rise respectively. I used the zero line for my default version to help determine the bullish or bearish trends. I have also included multiple colors to differentiate between very strong signals and normal signals, so very strong signals are darker in color, and normal signals use lighter colors. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators or scripts you would like to see me publish! I will have some more new scripts in the next week or so.
Velocity Indicator [CC]The Velocity Indicator was created by Scott Cong (Stocks and Commodities Sep 2023, pgs 8-15). This is my variation of his formula designed to capture the overall velocity of the underlying stock by applying the typical velocity formula. This indicator is visually similar to a typical stochastic indicator but uses a different underlying calculation. This works well as a momentum indicator, and the values are completely unbounded, so the best ways to determine bullish or bearish trends is either by using a crossover or crossunder between the indicator and the midline or to buy or sell the indicator when it reaches a high or low point and starts to fall or rise respectively. For my default version, I used the zero line to help determine the bullish or bearish trends. I have also included multiple colors to differentiate between very strong signals and normal signals, so very strong signals are darker in color, and normal signals use lighter colors. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators or scripts you would like to see me publish! I will have some more new scripts in the next week or so.
Volume ValueWhen VelocityTitle: Volume ValueWhen Velocity Trading Strategy
▶ Introduction:
The " Volume ValueWhen Velocity " trading strategy is designed to generate long position signals based on various technical conditions, including volume thresholds, RSI (Relative Strength Index), and price action relative to the Simple Moving Average (SMA). The strategy aims to identify potential buy opportunities when specific criteria are met, helping traders capitalize on potential bullish movements.
▶ How to use and conditions
★ Important : Only on Spot Binance BINANCE:BTCUSDT
Name: Volume ValueWhen Velocity
Operating mode: Long on Spot BINANCE BINANCE:BTCUSDT
Timeframe: Only one hour
Market: Crypto
currency: Bitcoin only
Signal type: Medium or short term
Entry: All sections in the Technical Indicators and Conditions section must be saved to enter (This is explained below)
Exit: Based on loss limit and profit limit It is removed in the settings section
Backtesting:
⁃ Exchange: BINANCE BINANCE:BTCUSDT
⁃ Pair: BTCUSDT
⁃ Timeframe:1h
⁃ Fee: 0.1%
- Initial Capital: 1,000 USDT
- Position sizing: 500 usdt
-Trading Range: 2022-07-01 11:30 ___ 2023-07-21 14:30
▶ Strategy Settings and Parameters:
1. `strategy(title='Volume ValueWhen Velocity', ...`: Sets the strategy title, initial capital, default quantity type, default quantity value, commission value, and trading currency.
↬ Stop-Loss and Take-Profit Settings:
1. long_stoploss_value and long_stoploss_percentage : Define the stop-loss percentage for long positions.
2. long_takeprofit_value and long_takeprofit_percentage : Define the take-profit percentage for long positions.
↬ ValueWhen Occurrence Parameters:
1. occurrence_ValueWhen_1 and occurrence_ValueWhen_2 : Control the occurrences of value events.
2. `distance_value`: Specifies the minimum distance between occurrences of ValueWhen 1 and ValueWhen 2.
↬ RSI Settings:
1. rsi_over_sold and rsi_length : Define the oversold level and RSI length for RSI calculations.
↬ Volume Thresholds:
1. volume_threshold1 , volume_threshold2 , and volume_threshold3 : Set the volume thresholds for multiple volume conditions.
↬ ATR (Average True Range) Settings:
1. atr_small and atr_big : Specify the periods used to calculate the Average True Range.
▶ Date Range for Back-Testing:
1. start_date, end_date, start_month, end_month, start_year, and end_year : Define the date range for back-testing the strategy.
▶ Technical Indicators and Conditions:
1. rsi: Calculates the Relative Strength Index (RSI) based on the defined RSI length and the closing prices.
2. was_over_sold: Checks if the RSI was oversold in the last 10 bars.
3. getVolume and getVolume2 : Custom functions to retrieve volume data for specific bars.
4. firstCandleColor : Evaluates the color of the first candle based on different timeframes.
5. sma : Calculates the Simple Moving Average (SMA) of the closing price over 13 periods.
6. numCandles : Counts the number of candles since the close price crossed above the SMA.
7. atr1 : Checks if the ATR_small is less than ATR_big for the specified security and timeframe.
8. prevClose, prevCloseBarsAgo, and prevCloseChange : ValueWhen functions to calculate the change in the close price between specific occurrences.
9. atrval: A condition based on the ATR_value3.
▶ Buy Signal Condition:
Condition: A combination of multiple volume conditions.
buy_signal: The final buy signal condition that considers various technical conditions and their interactions.
▶ Long Strategy Execution:
1. The strategy will enter a long position (buy) when the buy_signal condition is met and within the specified date range.
2. A stop-loss and take-profit will be set for the long position to manage risk and potential profits.
▶ Conclusion:
The " Volume ValueWhen Velocity " trading strategy is designed to identify long position opportunities based on a combination of volume conditions, RSI, and price action. The strategy aims to capitalize on potential bullish movements and utilizes a stop-loss and take-profit mechanism to manage risk and optimize potential returns. Traders can use this strategy as a starting point for their own trading systems or further customize it to suit their preferences and risk appetite. It is crucial to thoroughly back-test and validate any trading strategy before deploying it in live markets.
↯ Disclaimer:
Risk Management is crucial, so adjust stop loss to your comfort level. A tight stop loss can help minimise potential losses. Use at your own risk.
How you or we can improve? Source code is open so share your ideas!
Leave a comment and smash the boost button!
Visible Range Streaks of Unbroken Prior Highs/Lows [vnhilton](OVERVIEW)
This indicator keeps track of the number of unbroken prior highs/lows (unbroken being no price closes above/below the prior high/low). Great for entries and take profits. The indicator calculates on visible bars for convenience when looking back into the history without having to use bar replay, which those on the TradingView free plan cannot use intraday bar replay, so the visible range is a perfect work-around. The 2 minute chart above shows NASDAQ:NKLA on Thursday 13th July, 2023, with a significant level of $1.5 leading to a breakout. Streaks lower than 10 were hidden in the chart.
(FEATURES)
- Custom minimum streak size to start displaying plots (the smaller the size the more signals)
- Ability to show/hide numbers (that keep count of unbroken streaks), text signals (for when a streak is broken), break shapes (where the prior high/low was broken), and Zig Zag (lines between break shapes)
- Customisable Zig Zag line width, style, and colours (1 colour for a positive gradient line, and another for a negative gradient line)
- Customisable text signal text
- Customisable numbers, text signal, break shape, number label & text signal label colours
- Customisable number label, text signal label and break shape styles and sizes (number and text signal label share the same size)
Opening Range Gap + Std Dev [starclique]The ICT Opening Range Gap is a concept taught by Inner Circle Trader and is discussed in the videos: 'One Trading Setup For Life' and 2023 ICT Mentorship - Opening Range Gap Repricing Macro
ORGs, or Opening Range Gaps, are gaps that form only on the Regular Trading Hours chart.
The Regular Trading Hours gap occurs between 16:15 PM - 9:29 AM EST (UTC-4)
These times are considered overnight trading, so it is useful to filter the PA (price action) formed there.
The RTH option is only available for futures contracts and continuous futures from CME Group.
To change your chart to RTH, first things first, make sure you’re looking at a futures contract for an asset class, then on the bottom right of your chart, you’ll see ETH (by default) - Click on that, and change it to RTH.
Now your charts are filtering the price action that happened overnight.
To draw out your gap, use the Close of the 4:14 PM candle and the open of the 9:30 AM candle.
How is this concept useful?
Well, It can be used in many ways.
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How To Use The ORG
One of the ways you can use the opening range gap is simply as support and resistance
If we extend out the ORG from the example above, we can see that there is a clean retest of the opening range gap high after breaking structure to the upside and showing acceptance outside of the gap after consolidating within it.
The ORG High (4:14 Candle Close in this case) was used as support.
We then see an expansion to the upside.
Another way to implement the ORG is by using it as a draw on liquidity (magnet for price)
In this example, if we looked to the left, there was a huge ORG to the downside, leaving a massive gap.
The market will want to rebalance that gap during the regular trading hours.
The market rallies higher, rejects, comes down to clear the current days ORG low, then closes.
That is one example of how you can combine liquidity & ICT market structure concepts with Opening Range Gaps to create a story in the charts.
Now let’s discuss standard deviations.
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Standard Deviations
Standard Deviations are essentially projection levels for ranges / POIs (Point of Interests)
By this I mean, if you have a range, and you would like to see where it could potentially expand to, you’d place your fibonacci retracement tool on and high and low of the range, then use extension levels to find specific price points where price might reject from.
Since 0 and 1 are your Range High and Low respectively, your projection levels would be something like 1.5, 2, 2.5, and 3, for the extension from your 1 Fib Level, and -0.5, -1, -1.5, and -2 for your 0 Fib level.
The -1 and 2 level produce a 1:1 projection of your range low and high, meaning, if you expect price to expand as much as it did from the range low to range high, then you can project a -1 and 2 on your Fib, and it would show you what ICT calls “symmetrical price”
Now, how are standard deviations relevant here?
Well, if you’ve been paying attention to ICT’s recent videos, you would’ve caught that he’s recently started using Standard Deviation levels on breakers.
So my brain got going while watching his video on ORGs, and I decided to place the fib on the ORG high and low and see what it’d produce.
The results were very interesting.
Using this same example, if we place our fib on the ORG High and Low, and add some projection levels, we can see that we rejected right at the -2 Standard Deviation Level.
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You can see that I also marked out the EQ (Equilibrium, 50%, 0.5 of Fib) of the ORG. This is because we can use this level as a take profit level if we’re using an old ORG as our draw.
In days like these, where the gap formed was within a consolidation, and it continued to consolidate within the ORG zone that we extended, we can use the EQ in the same way we’d use an EQ for a range.
If it’s showing acceptance above the EQ, we are bullish, and expect the high of the ORG to be tapped, and vice versa.
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Using The Indicator
Here’s where our indicator comes in play.
To avoid having to do all this work of zooming in and marking out the close and open of the respective ORG candles, we created the Opening Range Gap + Standard Deviations Indicator, with the help of our dedicated Star Clique coder, a1tmaniac.
With the ORG + STD DEV indicator, you will be able to view ORG’s and their projections on the ETH (Electronic Trading Hours) chart.
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Features
Range Box
- Change the color of your Opening Range Gap to your liking
- Enable or disable the box from appearing using the checkbox
Range Midline
- Change the color of your Opening Range Gap Equilibrium
- Enable or disable the midline from appearing using the checkbox
Std. Dev
- Add whichever standard deviation levels you’d like.
- By default, the indicator comes with 0.5, 1, 1.5, and 2 standard deviation levels.
- Ensure that you add a comma ( , ) in between each standard deviation level
- Enable or disable the standard deviations from appearing using the opacity of the color (change to 0%)
Labels / Offset
- Adjust the offset of the label for the Standard Deviations
- Enable or disable the Labels from appearing using the checkbox
Time
- Adjust the time used for the indicators range
- If you’d like to use this for a Session or ICT Killzone instead, adjust the time
- Adjust the timezone used for the time referenced
- Options are UTC, US (UTC-4, New York Local Time) or UK (UTC+1, London Time)
- By default, the indicator is set to US
[blackcat] L2 Barbara Star Supertrend IndicatorLevel 2
Background
Barbara Star’s article on July 2023, “Stay On Track With The Supertrend Indicator”, I rewrote it as pine script for your information.
Function
A supertrend indicator is displayed either above or below the closing price to signal a buy or sell. The indicator changes color depending on whether you should buy or not. When the Supertrend indicator falls below the closing price, the indicator turns green, signaling one or more entry points to buy.
Author Barbara Star describes the Supertrend indicator and how it can be used as a means for traders to stay in sync with the larger trend. She explains how J. Welles Wilder's Average True Range (ATR) forms a basis for supertrend calculations. ATR does not measure price direction, but rather provides a measure of volatility over a period of time. The Supertrend indicator, on the other hand, provides a more comprehensive view of trend direction. In addition, the indicator provides price levels at which a trend reversal would occur.
Green color stands for up trend;
Red color stands for down trend.
Remarks
Feedbacks are appreciated.
Discrete Fourier Transformed Money Flow IndexThe Discrete Fourier Transform Money Flow Index indicator integrates the Money Flow Index (MFI) with Discrete Fourier Transform (credit to author wbburgin - May 26 2023 ) smoothing to offer a refined and smoothed depiction of the MFI's underlying trend. The MFI is calculated using the formula: MFI = 100 - (100 / (1 + MR)), where a high MFI value indicates robust buying pressure (signaling an overbought condition), and a low MFI value indicates substantial selling pressure (signaling an oversold condition).
Why is the DFT and MFI combined?
The aim of this combination between DFT and MFI is to effectively filter out short-term fluctuations and noise, enabling a clearer assessment of the overall trend. This smoothing process enhances the reliability of the MFI by emphasizing dominant and sustained buying or selling pressures. This script executes a full DFT but only uses filtering from one frequency component. The choice to focus on the magnitude at index 0 is significant as it captures the dominant or fundamental frequency in the data. By analyzing this primary cyclic behavior, we can identify recurring patterns and potential turning points more easily. This streamlined approach simplifies interpretation and enhances efficiency by reducing complexity associated with multiple frequency components. Overall, focusing on the dominant frequency and applying it to the MFI provides a concise and actionable assessment of the underlying data.
Note: The FMFI indicator provides both smoothed and non-smoothed versions of the MFI, with the option to toggle the original non-smoothed MFI on or off in the settings.
Application
FMFI functions as a trend-following indicator. Bullish trends are denoted by the color white, while bearish trends are represented by the color purple. Circles plotted on the FMFI indicate regular bull and bear signals. Additionally, red arrows indicate a strong negative trend, while green arrows indicate a strong positive trend. These arrows are calculated based on the presence of regular bull and bear signals within overbought and oversold zones. To enhance its effectiveness, it is recommended to combine this indicator with other complementary technical analysis tools and integrate it into a comprehensive trading strategy. Traders are encouraged to explore a wide range of settings and timeframes to align the indicator with their unique trading preferences and adapt it to the current market conditions. By doing so, traders can optimize the indicator's performance and increase their potential for successful trading outcomes.
Utility
Traders and investors can employ this indicator to enhance their trend-following strategies. The white-colored components of the FMFI can help identify potential buying zones, while the purple-colored components can assist in identifying potential selling points. The red and green arrows can be used to pinpoint moments of strong bull or bear momentum, allowing traders to position themselves advantageously in their trading activities. Please note that future performance of any trading strategy is fundamentally unknowable, and past results do not guarantee future performance.
