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.
在腳本中搜尋"curve"
ETHE Premium SmoothedThis script visualizes the "premium" or "deflection" between the price of Ethereum in a fund (ETHE) and the price of Ethereum itself. It's used to detect when the ETHE fund is trading at a significant premium or discount compared to the actual value of Ethereum it represents.
Components:
Two-Pole Smoothing Function: This function acts as a filter to smoothen data, specifically the calculated deflection. Using a combination of exponential math and trigonometry, the function reduces the noise from the raw deflection data, providing a clearer view of the trend.
ETH Per Share: A constant that represents the amount of Ethereum backing each share of ETHE.
Tickers: The script fetches data for two tickers:
ETHE ticker from OTC markets.
Ethereum's ticker from Coinbase.
Deflection Calculation: This represents the difference between the price of one share of ETHE and its actual value in Ethereum. This percentage gives an idea of how much more or less the ETHE is trading compared to its intrinsic Ethereum value.
Smoothing: The raw deflection data is then passed through the Two-Pole Smoothing function to produce the "smoothed" deflection curve.
Visuals:
A horizontal dashed red line at 0%, indicating the point where ETHE trades exactly at its intrinsic Ethereum value.
A plot of the smoothed deflection, with its color changing based on whether the value is above or below zero (green for above, red for below).
Usage:
Traders can use this script to identify potential buy or sell opportunities. For instance, if ETHE is trading at a significant discount (a negative deflection value), it might be an attractive buying opportunity, assuming the discrepancy will eventually correct itself. Conversely, if ETHE is trading at a significant premium (a positive deflection value), it might indicate a potential overvaluation.
[blackcat] L3 CCI-RSI ComboCCI-RSI Combo indicator is a combination indicator that includes CCI and RSI. It uses some parameters to calculate the values of CCI and RSI, and generates corresponding charts based on these values. On the chart, when CCI exceeds 100 or falls below -100, yellow or magenta filling areas are displayed. Additionally, gradient colors are used on the RSI chart to represent different value ranges. Based on the values of CCI and RSI, buying or selling signals can be identified and "B" or "S" labels are displayed at the corresponding positions. It utilizes some technical indicators and logic to generate buying and selling signals, and displays the corresponding labels on the chart.
Here are the main parts of the code:
1. Definition of some variables:
- `N`, `M`, `N1`: Parameters used to calculate CCI and RSI.
- `xcn(cond, len)` and `xex(cond, len)`: Two functions used to calculate the number of times a condition is met.
2. Calculation of CCI (Commodity Channel Index):
- Calculate the CCI value based on the formula `(TYP - ta.sma(TYP, M)) / (0.015 * ta.stdev(TYP, M))`.
- Use the `plot()` function to plot CCI on the chart and set the color based on its value.
3. Calculation of RSI (Relative Strength Index):
- First calculate RSI1 by taking the average of positive differences between closing prices and the average of all absolute differences, and then multiplying by 100.
- Then use the ALMA function to transform RSI1 into a smoother curve.
- Use the `plot()` function to plot RSI on the chart and select gradient colors for shading based on its value.
4. Setting up the gradient color array:
- Create a color array using `array.new_color()` and add a series of color values to it.
5. Generating buying and selling signals based on conditions:
- Use logical operators and technical indicator functions to determine the conditions for buying and selling.
- Use the `label.new()` function to draw the corresponding labels on the chart to represent buying or selling signals.
RSI Primed [ChartPrime]
RSI Primed combines candlesticks, patterns, and the classic RSI indicator for advanced market trend indications
Introduction
Technical traders are always looking for innovative methods to pinpoint potential entry and exit points in the market. The RSI Prime indicator provides such traders with an enhanced view of market conditions by combining various charting styles and the Relative Strength Index (RSI). It offers users a unique perspective on the market trends and price momentum, enabling them to make better-informed decisions and stay ahead of the market curve.
The RSI Primed is a versatile indicator that combines different charting styles with the Relative Strength Index (RSI) to help traders analyze market trends and price momentum. It offers multiple visualization modes that serve specific purposes and provide unique insights into market performance:
Regular Candlesticks
Candlesticks with Patterns
Heikin Ashi Candles
Line Style
Regular Candlestick Mode
The Regular Candlestick Mode in RSI Primed depicts traditional Japanese candlesticks that most traders are familiar with. This mode bypasses any smoothing or modified calculations, representing real-price movements. Regular candlesticks offer a clear and straightforward way to visualize market trends and price action.
Candlestick with Patterns Mode
The Candlestick with Patterns Mode focuses on identifying high-probability candlestick patterns while incorporating RSI values. By leveraging the information captured by the RSI, this mode allows traders to spot significant market reversals or continuation patterns that could signal potential trading opportunities. Some recognizable patterns include engulfing bullish, engulfing bearish, morning star bullish, and evening star bearish patterns.
Heikin Ashi Candles Mode
The Heikin Ashi Candles Mode presents an advanced candlestick charting technique known for its excellent trend-following capabilities. Heikin Ashi Candles filter out noise in the market and provide a clear representation of market trends. In this mode, candlesticks are plotted based on RSI values of the open, high, low, and close prices, helping traders understand and utilize market trends effectively.
Line Style Mode
The Line Style Mode offers a simpler and minimalistic representation of the RSI values by using a line instead of candlesticks to visualize market trends. This mode helps traders focus on the overall trend direction and eliminates potential distractions caused by the complexity of candlestick patterns.
Candle Color Overlay Mode
The Candle Color Overlay Mode is a unique feature in the RSI Primed indicator that allows traders to visualize the RSI values on the chart's candles as a heat gradient. This mode adds a color overlay to the candlesticks, representing the RSI values in relation to the candlesticks' price action.
By displaying the RSI as a color gradient, traders can quickly assess market momentum and identify overbought or oversold conditions without having to switch between different modes or charts. The gradient ranges from cool colors (blue and green) for lower RSI values, indicating oversold conditions, to warm colors (orange and red) for higher RSI values, signifying overbought situations.
To enable the Candle Color Overlay Mode, traders can toggle the "Color Candles" option in the indicator settings. Once enabled, the color gradient will be applied to the candlesticks on the chart, providing a visually striking and informative representation of the RSI values in relation to price action. This mode can be used in tandem with any of the other charting styles, allowing traders to gain even more insights into market trends and momentum.
RSI Primed Implementation
The RSI Primed indicator combines the benefits of various charting styles with the RSI to help traders gain a comprehensive view of market trends and price momentum. It incorporates the Heikin Ashi and RSI values as inputs to generate several visualization modes, enabling traders to select the one that best suits their needs.
Chebyshev Digital Audio Filter in RSI Primed Indicator
A unique feature of the RSI Primed Indicator is the incorporation of the Chebyshev Digital Audio Filter, a powerful tool that significantly influences the indicator's accuracy and responsiveness. This signal processing method brings several benefits to the context of the RSI indicator, improving its performance and capabilities.
1. Improved Signal Filtering
The Chebyshev filter excels in its ability to remove high-frequency noise and unwanted signals from the RSI data. While other filtering techniques might introduce unwanted side effects or distort the RSI data, the Chebyshev filter accurately retains the main signal components, enhancing the RSI Primed's overall accuracy and reliability.
2. Faster Response Time
The Chebyshev filter offers a faster response time than most other filtering techniques. In the context of the RSI Primed Indicator, this means that the filtering process is quicker and more efficient, allowing traders to act swiftly during rapidly changing market conditions.
3. Enhanced Trend Detection
By effectively removing noise from the RSI data, the Chebyshev filter contributes to the enhanced detection of underlying market trends. This feature helps traders identify potential entry and exit points more accurately, improving their overall trading strategy and performance.
How to Use RSI Primed
Traders can choose from different visualization modes to suit their preferences while using the RSI Primed indicator. By closely monitoring the chosen visualization mode and the position of the moving average, traders can make informed decisions about market trends.
Green candlesticks or an upward line slope indicate a bullish trend, and red candlesticks or a downward line slope suggest a bearish trend. If the candles or line are above the moving average, it could signify an uptrend, whereas a position below the moving average may indicate a downtrend.
The RSI Primed indicator offers a unique and comprehensive perspective on market trends and price momentum by combining various charting styles with the RSI. Traders can choose from different visualization modes and make well-informed decisions to capitalize on market opportunities. This innovative indicator provides a clear and concise view of the market, enabling traders to make swift decisions and enhance their trading results.
Aggregate Medians [wbburgin]This indicator recursively finds the average of all high/low medians under your chosen length. This can be very, very helpful for analyzing trends where a moving average or a normal median would produce a bunch of false signals.
Settings:
The "Length" setting is the maximum median that you want the algorithm to add into the sum. The "Start at Period" setting is the the minimum median that you want the algorithm to take into account. Starting at a higher period means that the faster, more sensitive medians of lower lengths are not included, and will smooth out your curve.
I haven't seen many recursive algorithms on TradingView so feel free to use this script as inspiration for any of your ideas. In theory, you can essentially replace the median function with any other function - a moving average, a supertrend, or anything else.
The start must be lower than the length, because this is a sum from the start to the length of all medians in between.
Сoncentrated Market Maker Strategy by oxowlConcentrated Market Maker Strategy by oxowl. This script plots an upper and lower bound for liquidity provision, and checks for rebalancing conditions. It also includes alert conditions for when the price crosses the upper or lower bounds.
Here's an overview of the script:
It defines the input parameters: liquidity range percentage, rebalance frequency in minutes, and minimum trade size in assets.
It calculates the upper and lower bounds for liquidity provision based on the liquidity range percentage.
It initializes variables for the last rebalance time and price.
It defines a rebalance condition based on the frequency and current price within the specified range.
If the rebalance condition is met, it updates the last rebalance time and price.
It plots the upper and lower bounds on the chart as lines and adds price labels for both bounds.
It defines alert conditions for when the price crosses the upper or lower bounds.
Finally, it creates alert conditions with appropriate messages for when the price crosses the upper or lower bounds.
Concentrated liquidity is a concept often used in decentralized finance (DeFi) market-making strategies. It allows liquidity providers (LPs) to focus their liquidity within a specific price range, rather than across the entire price curve. Using an indicator with concentrated liquidity can offer several advantages:
Increased capital efficiency: Concentrated liquidity allows LPs to allocate their capital within a narrower price range. This means that the same amount of capital can generate more significant price impact and potentially higher returns compared to providing liquidity across a broader range.
Customized risk exposure: LPs can choose the price range they feel most comfortable with, allowing them to better manage their risk exposure. By selecting a range based on their market outlook, they can optimize their positions to maximize potential returns.
Adaptive strategies: Indicators that support concentrated liquidity can help traders adapt their strategies based on market conditions. For example, they can choose to provide liquidity around a stable price range during low-volatility periods or adjust their range when market conditions change.
To continue integrating this script into your trading strategy, follow these steps:
Import the script into your TradingView account. Navigate to the Pine editor, paste the code, and save it as a new script.
Apply the indicator to a trading pair chart. You can customize the input parameters (liquidity range percentage, rebalance frequency, and minimum trade size) based on your preferences and risk tolerance.
Set alerts for when the price crosses the upper or lower bounds. This will notify you when it's time to take action, such as adding or removing liquidity, or rebalancing your position.
Monitor the performance of your strategy over time. Adjust the input parameters as needed to optimize your returns and manage risk effectively.
(Optional) Integrate the script with a trading bot or automation platform. If you're using an API-based trading solution, you can incorporate the logic and conditions from the script into your bot's algorithm to automate the process of providing concentrated liquidity and rebalancing your positions.
Remember that no strategy is foolproof, and past performance is not indicative of future results. Always exercise caution when trading and carefully consider your risk tolerance.
Channel Trend and 3 EMA Stratgy MHG-V.6.1.4Dear Traders..
This Startgy use the 3 tools
1- Trend Detection
2- draw the Ascending and Descending channels
3- Use the 3 Moving average for take the acceptant for position entry.
If ascending channel and make the buy signal, then watch the EMA 2 if the price above the 2 EMA you can open the Long position.
SL alittle under the 2 EMA Curve.
for short position riverce the above manual...
Percent Volatility MomentumThis pine script calculates percent volatility momentum, negative percent volatility and positive percent volatility. The blue line is the overall momentum of the current percent volatility trend. The red line only includes negative movements in the percent volatility of the source. The green line includes only positive movements of the percent volatility of the source. The script also includes an angle and a normalized angle setting that allows one to determine the angle of the source curve. Note, the angle was transformed from -90 to 90 to 0 to 100. Such that an angle of -90 is transformed to 0. An angle of 0 is transformed to 50 and an angle of 90 is transformed to 100. This is the first draft of this script and my first pine script published. Any feedback is welcome. I borrowed code from TradingView's Linear Regression Channel and Relative Strength Index pine scripts.
Minervini QualifierThe Minervini Qualifier indicator calculates the qualifying conditions from Mark Minervini’s book “Trade like a Stock Market Wizard”.
The condition matching is been shown as fill color inside an SMA 20day envelope curve.
If the envelope color is red, current close price is below the SMA20 and when blue, current close price is above the SMA20. The fill color can be transparent (not matching qualifying conditions), yellow (matching all conditions except close is still below SMA50), green (all conditions match, SMA200 trending for at least one month up) or blue (all conditions match, SMA200 trending up for at least 5 months)
As I wanted also to see which of the qualifying conditions match over time, I’ve added add. lines, each representing one conditions. If it matches, line color is blue, or red if not. Use the data windows (right side), so you know what line represents which condition. Can be turned on/off (default:on)
In addition, a relative strength is been calculated, to compare the stock to a reference index. It is just one possible way to calculate it, might be different to what Mark Minervini is using. If the shown value (top right) is above 100, stock performs better compared to reference index (can be set in settings), when below 100, stock performs worse compared to reference index. Can be turned on/off (default:on)
How to use it:
For more details, read Mark’s book and watch his videos.
Limitations:
It gives only useful information on daily timeframe
(No financial advise, for testing purposes only)
Heikin Ashi SupertrendAbout this Strategy
This supertrend strategy uses the Heikin Ashi candles to generate the supertrend but enters and exits trades using normal candle close prices. If you use the standard built in Supertrend indicator on Heikin Ashi candles, it will produce very unrealistic backtesting results because it uses the Heikin Ashi prices instead of the real prices. However, by signaling the supertrend reversals using Heikin Ashi while using standard candle close prices for the entries and exits, it corrects the backtesting errors and gives you a more realistic equity curve. You should set the chart to use standard candles and then hide them (the strategy creates the candles).
This strategy includes:
Plotting of Heikin Ashi candles
Heikin Ashi Supertrend
Long and Short Entry Signals
Move stop loss after trade is X% in profit
Profit Target
Stop Loss
Built in Alertatron automation
Alertatron Trade Automation Integration
For Alertatron integration, be sure to configure the strategy settings and "Enable Webhook Messages" before creating an alert with {{strategy.order.alert_message}} in the body of your alert message. Be sure to enable webhooks and point it to your Incoming Alertatron webhook URL.
Notes
While this strategy does pretty well during trending markets, It's worth noting that the Buy and Hold ROI is much better during peak times of the bull market
Not financial advice. Do not risk more than you can afford to lose.
Smoothed Repulse w/ Floating Levels [Loxx]Smoothed Repulse w/ Floating Levels indicator measures and displays the bullish or bearish pressure associated with each price candlestick in the form of a curve.
It is more relevant when compared to price and offers valuable additional information on the feeling and confidence that traders have about the markets.
This version can use one of the 4 basic averages types for smoothing.
Coloring can be chosen depending on :
slope
outer levels cross
middle ("zero") level cross
Since the "repulse" indicator is not limited to known bounds, levels are dynamic — the "zero" value too. That makes it more responsive in the times of elevated volatility. Alerts are triggered based on the color change.
Included:
Bar coloring
Signals
Alerts
Your choice of moving average for smoothing
Moving Average Filters Add-on w/ Expanded Source Types [Loxx]Moving Average Filters Add-on w/ Expanded Source Types is a conglomeration of specialized and traditional moving averages that will be used in most of indicators that I publish moving forward. There are 39 moving averages included in this indicator as well as expanded source types including traditional Heiken Ashi and Better Heiken Ashi candles. You can read about the expanded source types clicking here . About half of these moving averages are closed source on other trading platforms. This indicator serves as a reference point for future public/private, open/closed source indicators that I publish to TradingView. Information about these moving averages was gleaned from various forex and trading forums and platforms as well as TASC publications and other assorted research publications.
________________________________________________________________
Included moving averages
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA, it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA.
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average (DEMA) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA. It's also considered a leading indicator compared to the EMA, and is best utilized whenever smoothness and speed of reaction to market changes are required.
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA (Simple Moving Average). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA.
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Hull Moving Average - HMA
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points.
IE/2 - Early T3 by Tim Tilson
The IE/2 is a Moving Average that uses Linear Regression slope in its calculation to help with smoothing. It's a worthy Moving Average on it's own, even though it is the precursor and very early version of the famous "T3 Indicator".
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA (Simple Moving Average) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and it's smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA (Least Squares Moving Average)
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA. Although it's similar to the Simple Moving Average, the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track price better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average. The Linear Weighted Moving Average calculates the average by assigning different weight to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrows price.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA.
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average (SMA), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen a an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA (Smoothed Moving Average). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a a Two pole Butterworth filter combined with a 2-bar SMA (Simple Moving Average) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA. They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
The TMA and Sine Weighted Moving Average Filter are almost identical at times.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, it's signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers.
Volume Weighted EMA - VEMA
Utilizing tick volume in MT4 (or real volume in MT5), this EMA will use the Volume reading in its decision to plot its moves. The more Volume it detects on a move, the more authority (confirmation) it has. And this EMA uses those Volume readings to plot its movements.
Studies show that tick volume and real volume have a very strong correlation, so using this filter in MT4 or MT5 produces very similar results and readings.
Zero Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers, as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA, this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
________________________________________________________________
What are Heiken Ashi "better" candles?
The "better formula" was proposed in an article/memo by BNP-Paribas (In Warrants & Zertifikate, No. 8, August 2004 (a monthly German magazine published by BNP Paribas, Frankfurt), there is an article by Sebastian Schmidt about further development (smoothing) of Heikin-Ashi chart.)
They proposed to use the following:
(Open+Close)/2+(((Close-Open)/( High-Low ))*ABS((Close-Open)/2))
instead of using :
haClose = (O+H+L+C)/4
According to that document the HA representation using their proposed formula is better than the traditional formula.
What are traditional Heiken-Ashi candles?
The Heikin-Ashi technique averages price data to create a Japanese candlestick chart that filters out market noise.
Heikin-Ashi charts, developed by Munehisa Homma in the 1700s, share some characteristics with standard candlestick charts but differ based on the values used to create each candle. Instead of using the open, high, low, and close like standard candlestick charts, the Heikin-Ashi technique uses a modified formula based on two-period averages. This gives the chart a smoother appearance, making it easier to spots trends and reversals, but also obscures gaps and some price data.
Expanded generic source types:
Close = close
Open = open
High = high
Low = low
Median = hl2
Typical = hlc3
Weighted = hlcc4
Average = ohlc4
Average Median Body = (open+close)/2
Trend Biased = (see code, too complex to explain here)
Trend Biased (extreme) = (see code, too complex to explain here)
Included:
-Toggle bar color on/off
-Toggle signal line on/off
StapleIndicatorsLibrary "StapleIndicators"
This Library provides some common indicators commonly referenced from other studies in Pine Script
squeeze(bbSrc, bbPeriod, bbDev, kcSrc, kcPeriod, kcATR, signalPeriod) Volatility Squeeze
Parameters:
bbSrc : (Optional) Bollinger Bands Source. By default close
bbPeriod : (Optional) Bollinger Bands Period. By default 20
bbDev : (Optional) Bollinger Bands Standard Deviation. By default 2.0
kcSrc : (Optional) Keltner Channel Source. By default close
kcPeriod : (Optional) Keltner Channel Period. By default 20
kcATR : (Optional) Keltner Channel ATR Multiplier. By default 1.5
signalPeriod : (Optional) Keltner Channel ATR Multiplier. By default 1.5
Returns:
adx(diPeriod, adxPeriod, signalPeriod, adxTier1, adxTier2, adxTier3) ADX: Average Directional Index
Parameters:
diPeriod : (Optional) Directional Indicator Period. By default 14
adxPeriod : (Optional) ADX Smoothing. By default 14
signalPeriod : (Optional) Signal Period. By default 13
adxTier1 : (Optional) ADX Tier #1 Level. By default 20
adxTier2 : (Optional) ADX Tier #2 Level. By default 15
adxTier3 : (Optional) ADX Tier #3 Level. By default 10
Returns:
smaPreset(srcMa) Delivers a set of frequently used Simple Moving Averages
Parameters:
srcMa : (Optional) MA Source. By default 'close'
Returns:
emaPreset(srcMa) Delivers a set of frequently used Exponential Moving Averages
Parameters:
srcMa : (Optional) MA Source. By default 'close'
Returns:
maSelect(ma, srcMa) Filters and outputs the selected MA
Parameters:
ma : (Optional) MA text. By default 'Ema-21'
srcMa : (Optional) MA Source. By default 'close'
Returns: maSelected
periodAdapt(modeAdaptative, src, maxLen, minLen) Adaptative Period
Parameters:
modeAdaptative : (Optional) Adaptative Mode. By default 'Average'
src : (Optional) Source. By default 'close'
maxLen : (Optional) Max Period. By default '60'
minLen : (Optional) Min Period. By default '4'
Returns: periodAdaptative
azlema(modeAdaptative, srcMa) Azlema: Adaptative Zero-Lag Ema
Parameters:
modeAdaptative : (Optional) Adaptative Mode. By default 'Average'
srcMa : (Optional) MA Source. By default 'close'
Returns: azlema
ssma(lsmaVar, srcMa, periodMa) SSMA: Smooth Simple MA
Parameters:
lsmaVar : Linear Regression Curve.
srcMa : (Optional) MA Source. By default 'close'
periodMa : (Optional) MA Period. By default '13'
Returns: ssma
jvf(srcMa, periodMa) Jurik Volatility Factor
Parameters:
srcMa : (Optional) MA Source. By default 'close'
periodMa : (Optional) MA Period. By default '7'
Returns:
jBands(srcMa, periodMa) Jurik Bands
Parameters:
srcMa : (Optional) MA Source. By default 'close'
periodMa : (Optional) MA Period. By default '7'
Returns:
jma(srcMa, periodMa, phase) Jurik MA (JMA)
Parameters:
srcMa : (Optional) MA Source. By default 'close'
periodMa : (Optional) MA Period. By default '7'
phase : (Optional) Phase. By default '50'
Returns: jma
maCustom(ma, srcMa, periodMa, lrOffset, almaOffset, almaSigma, jmaPhase, azlemaMode) Creates a custom Moving Average
Parameters:
ma : (Optional) MA text. By default 'Ema'
srcMa : (Optional) MA Source. By default 'close'
periodMa : (Optional) MA Period. By default '13'
lrOffset : (Optional) Linear Regression Offset. By default '0'
almaOffset : (Optional) Alma Offset. By default '0.85'
almaSigma : (Optional) Alma Sigma. By default '6'
jmaPhase : (Optional) JMA Phase. By default '50'
azlemaMode : (Optional) Azlema Adaptative Mode. By default 'Average'
Returns: maTF
MZ Adaptive Ichimoku Cloud (Volume, Volatility, Chikou Filter) This study is a functional enhancement to conventionally used Ichimoku Clouds . It uses particular effective adaptive parameters (Relative Volume Strength Index (MZ RVSI ), Volatility and Chikou Backward Trend Filter) to adapt dynamic lengths for Kijun-sen, Tenkan-sen, Senkou-span and Chikou .
This study uses complete available Ichimoku Clouds trading methodology to provide trade confirmations. Its still in experimental phase so will be updated accordingly.
ADAPTIVE LENGTH PARAMETERS
Relative Volume Strength Index (MZ RVSI )
For adaptive length, I tried using Volume and for this purpose I used my Relative Volume Strength Index " RVSI " indicator. RVSI is the best way to detect if Volume is going for a breakout or not and based on that indication length changes.
RVSI breaking above provided value would indicate Volume breakout and hence dynamic length would accordingly make Ichimoku Clouds more over-fitted to better act as support and resistance . Similar case would happen if Volume goes down and dynamic length becomes more under-fit.
Original RVSI Library and study can be found here:
Volatility
Average true range is used as volatility measurement and detection tool. Dual ATR condition would decide over-fitting or under-fitting of curve.
Chikou Backward Trend Filter
Chikou is basically close value of ticker offset to close and it is a good for indicating if close value has crossed potential Support/Resistance zone from past. Chikou is usually used with 26 period.
Chikou filter uses a lookback length calculated from provided lookback percentage and checks if trend was bullish or bearish within that lookback period.
Original Chikou Filter library and study can be found here:
ADAPTIVE ICHIMOKU CLOUD
Tenkan-Sen (Conversion Line)
Tenkan-sen is a moving average that is calculated by taking the average of the high and the low for the last nine periods conventionally but in this study its length is dynamically adapted based on Volume, Volatility and Chikou filter. Default adaption range is set to 9-30 which I found universally applicable to almost every market on all time-frames.
Kijun-Sen (Base Line)
The Kijun-Sen is usually considered a support/resistance line which also acts as an indicator of price movements in the future and takes a longer period into consideration, usually 26 periods compared to Tenkan-Sen’s nine periods is used conventionally. In this study, its length is set to vary in range of 20-60 based on adaptive parameters.
Senkou-Span (Leading Span)
Senkou-Span A : Senkou Span A is the average of the highs and lows of Tenkan-Sen and Kijun-Sen so it automatically adapts accroding to dynamic lengths of Tenkan and Kijun.
Senkou-Span B : Senkou Span B is usually calculated by averaging highs and lows of the past 52 periods and plotting it 26 points to the right but this study uses adaptive parameters to adapt its dynamic length in range of 50-120 which makes Kumo (Ichimoku Cloud) a better area for support and resistance. I don’t consider its necessary to adapt Kumo’s displacement to the right, so I used conventional 26 period as offset.
Chikou -Span (Lagging Span)
The Chikou Span, also known as the lagging span is formed by taking the price source and offsetting it back 26 periods to the left but I used adaptive length in range 26-50 which makes this tool a better option to check for Chikou -Price cross check in wide range.
TRADE SIGNALS & CONFIRMATIONS
Volume : RVSI used to detect volume breakout about given point. By default, On Balance Volume based RVSI is selected for all dynamic length adaption and also for trade confirmations.
Cross(Tenkan,Kijun) : Easiest way to detect trend as if Tenkan is above Kijun then market is uptrend and vice versa.
Volatility : High volatility is a good way to confirm if price is on the move or not.
Tenkan = Kijun : Because of a wide range of Tenkan and Kijun length; their value can become equal before reversal.
Chikou > Source : A very conventional way to detect price momentum as if Chikou is above price then market is in uptrend and vice versa.
Chikou Momentum : Another simpler way to represent Chikou > Source as if momentum of price source is uptrend then price will tend to follow.
Source > Kumo : Using the best tool of Ichimoku Clouds i.e. Kumo. If price crosses both Senkou-Span A & B then market has broken potential resistance leading to a good uptrend and vice versa.
Source > Tenkan : Better way to detect price trend in short term.
Chikou Backward Trend Filter : Different from Chikou >Source in a way that Chikou filter makes sure that price crosses highest/lowest within defined period.
CHARTING
Bars Coloring : Bars coloring is set as following :
src > tenkan-Sen and src > kijun-Sen : Strong uptrend detection and shown by green bars.
src < tenkan-Sen and src < kijun-Sen : Strong downtrend detection and shown by green bars.
src > tenkan-Sen and src < kijun-Sen : Better way to detect bottom reversals as if price comes above tenkan but remains below kijun; that’s early signs of recovery. Light red bars are used for this by default.
src < tenkan-Sen and src > kijun-Sen : Better way to detect top reversals as if price comes below tenkan but remains above kijun; that’s early signs of losing potential in uptrend. Dark Grey bars are used for this by default.
Kumo Coloring : Following steps are used to derive Kumo’s dynamic color:
Average of Senkou-span A and B is calculated.
RSI with 14 period of that average is calculated.
Gradient color based on calculated RSI values with 0-100 range is derived which is final Kumo color.
Chikou Span Coloring : Dynamic coloring from Chikou Filter is used as Indicator’s Chikou ’s color.
Signals Overlay : Red and Green small triangles are used as signals overlay.
Adaptive MA Difference constructor [lastguru]A complimentary indicator to my Adaptive MA constructor. It calculates the difference between the two MA lines (inspired by the Moving Average Difference (MAD) indicator by John F. Ehlers). You can then further smooth the resulting curve. The parameters and options are explained here:
The difference is normalized by dividing the difference by twice its Root mean square (RMS) over Slow MA length. Inverse Fisher Transform is then used to force the -1..1 range.
Same Postfilter options are provided as in my Adaptive Oscillator constructor:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic ) by John F. Ehlers
Inverse Fisher Transform - Inverse Fisher Transform
Noise Elimination Technology - a simplified Kendall correlation algorithm "Noise Elimination Technology" by John F. Ehlers
Momentum - momentum (derivative)
Except for Inverse Fisher Transform, all Postfilter algorithms can have Length parameter. If it is not specified (set to 0), then the calculated Slow MA Length is used.
Indicators Combination Framework v3 IND [DTU]Hello All,
This script is a framework to analyze and see the results by combine selected indicators for (long, short, longexit, shortexit) conditions.
I was designed this for beginners and users to facilitate to see effects of the technical indicators combinations on the chart WITH NO CODE
You can improve your strategies according the results of this system by connecting the framework to a strategy framework/template such as Pinecoder, Benson, daveatt or custom.
This is enhanced version of my previous indicator "Indicators & Conditions Test Framework "
Currently there are 93 indicators (23 newly added) connected over library. You can also import an External Indicator or add Custom indicator (In the source)
It is possible to change it from Indicator to strategy (simple one) by just remarking strategy parts in the source code and see real time profit of your combinations
Feel free to change or use it in your source
Special thanks goes to Pine wizards: Trading view (built-in Indicators), @Rodrigo, @midtownsk8rguy, @Lazybear, @Daveatt and others for their open source codes and contributions
SIMPLE USAGE
1. SETTING: Show Alerts= True (To see your entries and Exists)
2. Define your Indicators (ex: INDICATOR1: ema(close,14), INDICATOR2: ema(close,21), INDICATOR3: ema(close,200)
3. Define Your Combinations for long & Short Conditions
a. For Long: (INDICATOR1 crossover INDICATOR2) AND (INDICATOR3 < close)
b. For Short: (INDICATOR1 crossunder INDICATOR2) AND (INDICATOR3 > close)
4. Select Strategy/template (Import strategy to chart) that you export your signals from the list
5. Analyze the best profit by changing Indicators values
SOME INDICATORS DETAILS
Each Indicator includes:
- Factorization : Converting the selected indicator to Double, triple Quadruple such as EMA to DEMA, TEMA QEMA
- Log : Simple or log10 can be used for calculation on function entries
- Plot Type : You can overlay the indicator on the chart (such ema) or you can use stochastic/Percentrank approach to display in the variable hlines range
- Extended Parametes : You can use default parameters or you can use extended (P1,P2) parameters regarding to indicator type and your choice
- Color : You can define indicator color and line properties
- Smooth : you can enable swma smooth
- indicators : you can select one of the 93 function like ema(),rsi().. to define your indicator
- Source : you can select from already defined indicators (IND1-4), External Indicator (EXT), Custom Indicator (CUST), and other sources (close, open...)
CONDITION DETAILS
- There are are 4 type of conditions, long entry, short entry, long exit, short exit.
- Each condition are built up from 4 combinations that joined with "AND" & "OR" operators
- You can see the results by enabling show alerts check box
- If you only wants to enter long entry and long exit, just fill these conditions
- If "close on opposite" checkbox selected on settings, long entry will be closed on short entry and vice versa
COMBINATIONS DETAILS
- There are 4 combinations that joined with "AND" & "OR" operators for each condition
- combinations are built up from compare 1st entry with 2nd one by using operator
- 1st and 2nd entries includes already defined indicators (IND1-5), External Indicator (EXT), Custom Indicator (CUST), and other sources (close, open...)
- Operators are comparison values such as >,<, crossover,...
- 2nd entry include "VALUE" parameter that will use to compare 1st indicator with value area
- If 2nd indicator selected different than "VALUE", value are will mean previous value of the selection. (ex: value area= 2, 2nd entry=close, means close )
- Selecting "NONE" for the 1st entry will disable calculation of current and following combinations
JOINS DETAILS
- Each combination will join wiht the following one with the JOIN (AND, OR) operator (if the following one is not equal "NONE")
CUSTOM INDICATOR
- Custom Indicator defines harcoded in the source code.
- You can call it with "CUST" in the Indicator definition source or combination entries source
- You can change or implement your custom indicator by updating the source code
EXTERNAL INDICATOR
- You can import an external indicator by selecting it from the ext source.
- External Indicator should be already imported to the chart and it have an plot function to output its signal
EXPORTING SIGNAL
- You can export your result to an already defined strategy template such as Pine coders, Benson, Daveatt Strategy templates
- Or you can define your custom export for other future strategy templates
ALERTS
- By enabling show alerts checkbox, you can see long entry exits on the bottom, and short entry exits aon the top of the chart
ADDITIONAL INFO
- You can see all off the inputs descriptions in the tooltips. (You can also see the previous version for details)
- Availability to set start, end dates
- Minimize repainting by using security function options (Secure, Semi Secure, Repaint)
- Availability of use timeframes
-
Version 3 INDICATORS LIST (More to be added):
▼▼▼ OVERLAY INDICATORS ▼▼▼
alma(src,len,offset=0.85,sigma=6).-------Arnaud Legoux Moving Average
ama(src,len,fast=14,slow=100).-----------Adjusted Moving Average
accdist().-------------------------------Accumulation/distribution index.
cma(src,len).----------------------------Corrective Moving average
dema(src,len).---------------------------Double EMA (Same as EMA with 2 factor)
ema(src,len).----------------------------Exponential Moving Average
gmma(src,len).---------------------------Geometric Mean Moving Average
highest(src,len).------------------------Highest value for a given number of bars back.
hl2ma(src,len).--------------------------higest lowest moving average
hma(src,len).----------------------------Hull Moving Average.
lagAdapt(src,len,perclen=5,fperc=50).----Ehlers Adaptive Laguerre filter
lagAdaptV(src,len,perclen=5,fperc=50).---Ehlers Adaptive Laguerre filter variation
laguerre(src,len).-----------------------Ehlers Laguerre filter
lesrcp(src,len).-------------------------lowest exponential esrcpanding moving line
lexp(src,len).---------------------------lowest exponential expanding moving line
linreg(src,len,loffset=1).---------------Linear regression
lowest(src,len).-------------------------Lovest value for a given number of bars back.
mcginley(src, len.-----------------------McGinley Dynamic adjusts for market speed shifts, which sets it apart from other moving averages, in addition to providing clear moving average lines
percntl(src,len).------------------------percentile nearest rank. Calculates percentile using method of Nearest Rank.
percntli(src,len).-----------------------percentile linear interpolation. Calculates percentile using method of linear interpolation between the two nearest ranks.
previous(src,len).-----------------------Previous n (len) value of the source
pivothigh(src,BarsLeft=len,BarsRight=2).-Previous pivot high. src=src, BarsLeft=len, BarsRight=p1=2
pivotlow(src,BarsLeft=len,BarsRight=2).--Previous pivot low. src=src, BarsLeft=len, BarsRight=p1=2
rema(src,len).---------------------------Range EMA (REMA)
rma(src,len).----------------------------Moving average used in RSI. It is the exponentially weighted moving average with alpha = 1 / length.
sar(start=len, inc=0.02, max=0.02).------Parabolic SAR (parabolic stop and reverse) is a method to find potential reversals in the market price direction of traded goods.start=len, inc=p1, max=p2. ex: sar(0.02, 0.02, 0.02)
sma(src,len).----------------------------Smoothed Moving Average
smma(src,len).---------------------------Smoothed Moving Average
super2(src,len).-------------------------Ehlers super smoother, 2 pole
super3(src,len).-------------------------Ehlers super smoother, 3 pole
supertrend(src,len,period=3).------------Supertrend indicator
swma(src,len).---------------------------Sine-Weighted Moving Average
tema(src,len).---------------------------Triple EMA (Same as EMA with 3 factor)
tma(src,len).----------------------------Triangular Moving Average
vida(src,len).---------------------------Variable Index Dynamic Average
vwma(src,len).---------------------------Volume Weigted Moving Average
volstop(src,len,atrfactor=2).------------Volatility Stop is a technical indicator that is used by traders to help place effective stop-losses. atrfactor=p1
wma(src,len).----------------------------Weigted Moving Average
vwap(src_).------------------------------Volume Weighted Average Price (VWAP) is used to measure the average price weighted by volume
▼▼▼ NON OVERLAY INDICATORS ▼▼
adx(dilen=len, adxlen=14, adxtype=0).----adx. The Average Directional Index (ADX) is a used to determine the strength of a trend. len=>dilen, p1=adxlen (default=14), p2=adxtype 0:ADX, 1:+DI, 2:-DI (def:0)
angle(src,len).--------------------------angle of the series (Use its Input as another indicator output)
aroon(len,dir=0).------------------------aroon indicator. Aroons major function is to identify new trends as they happen.p1 = dir: 0=mid (default), 1=upper, 2=lower
atr(src,len).----------------------------average true range. RMA of true range.
awesome(fast=len=5,slow=34,type=0).------Awesome Oscilator is an indicator used to measure market momentum. defaults : fast=len= 5, p1=slow=34, p2=type: 0=Awesome, 1=difference
bbr(src,len,mult=1).---------------------bollinger %%
bbw(src,len,mult=2).---------------------Bollinger Bands Width. The Bollinger Band Width is the difference between the upper and the lower Bollinger Bands divided by the middle band.
cci(src,len).----------------------------commodity channel index
cctbbo(src,len).-------------------------CCT Bollinger Band Oscilator
change(src,len).-------------------------A.K.A. Momentum. Difference between current value and previous, source - source . is most commonly referred to as a rate and measures the acceleration of the price and/or volume of a security
cmf(len=20).-----------------------------Chaikin Money Flow Indicator used to measure Money Flow Volume over a set period of time. Default use is len=20
cmo(src,len).----------------------------Chande Momentum Oscillator. Calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movement over the same period.
cog(src,len).----------------------------The cog (center of gravity) is an indicator based on statistics and the Fibonacci golden ratio.
copcurve(src,len).-----------------------Coppock Curve. was originally developed by Edwin Sedge Coppock (Barrons Magazine, October 1962).
correl(src,len).-------------------------Correlation coefficient. Describes the degree to which two series tend to deviate from their ta.sma values.
count(src,len).--------------------------green avg - red avg
cti(src,len).----------------------------Ehler s Correlation Trend Indicator by
dev(src,len).----------------------------ta.dev() Measure of difference between the series and its ta.sma
dpo(len).--------------------------------Detrended Price OScilator is used to remove trend from price.
efi(len).--------------------------------Elders Force Index (EFI) measures the power behind a price movement using price and volume.
eom(len=14,div=10000).-------------------Ease of Movement.It is designed to measure the relationship between price and volume.p1 = div: 10000= (default)
falling(src,len).------------------------ta.falling() Test if the `source` series is now falling for `length` bars long. (Use its Input as another indicator output)
fisher(len).-----------------------------Fisher Transform is a technical indicator that converts price to Gaussian normal distribution and signals when prices move significantly by referencing recent price data
histvol(len).----------------------------Historical volatility is a statistical measure used to analyze the general dispersion of security or market index returns for a specified period of time.
kcr(src,len,mult=2).---------------------Keltner Channels Range
kcw(src,len,mult=2).---------------------ta.kcw(). Keltner Channels Width. The Keltner Channels Width is the difference between the upper and the lower Keltner Channels divided by the middle channel.
klinger(type=len).-----------------------Klinger oscillator aims to identify money flow’s long-term trend. type=len: 0:Oscilator 1:signal
macd(src,len).---------------------------MACD (Moving Average Convergence/Divergence)
mfi(src,len).----------------------------Money Flow Index s a tool used for measuring buying and selling pressure
msi(len=10).-----------------------------Mass Index (def=10) is used to examine the differences between high and low stock prices over a specific period of time
nvi().-----------------------------------Negative Volume Index
obv().-----------------------------------On Balance Volume
pvi().-----------------------------------Positive Volume Index
pvt().-----------------------------------Price Volume Trend
ranges(src,upper=len, lower=-5).---------ranges of the source. src=src, upper=len, v1:lower=upper . returns: -1 source=upper otherwise 0
rising(src,len).-------------------------ta.rising() Test if the `source` series is now rising for `length` bars long. (Use its Input as another indicator output)
roc(src,len).----------------------------Rate of Change
rsi(src,len).----------------------------Relative strength Index
rvi(src,len).----------------------------The Relative Volatility Index (RVI) is calculated much like the RSI, although it uses high and low price standard deviation instead of the RSI’s method of absolute change in price.
smi_osc(src,len,fast=5, slow=34).--------smi Oscillator
smi_sig(src,len,fast=5, slow=34).--------smi Signal
stc(src,len,fast=23,slow=50).------------Schaff Trend Cycle (STC) detects up and down trends long before the MACD. Code imported from
stdev(src,len).--------------------------Standart deviation
trix(src,len) .--------------------------the rate of change of a triple exponentially smoothed moving average.
tsi(src,len).----------------------------The True Strength Index indicator is a momentum oscillator designed to detect, confirm or visualize the strength of a trend.
ultimateOsc(len.-------------------------Ultimate Oscillator indicator (UO) indicator is a technical analysis tool used to measure momentum across three varying timeframes
variance(src,len).-----------------------ta.variance(). Variance is the expectation of the squared deviation of a series from its mean (ta.sma), and it informally measures how far a set of numbers are spread out from their mean.
willprc(src,len).------------------------Williams %R
wad().-----------------------------------Williams Accumulation/Distribution.
wvad().----------------------------------Williams Variable Accumulation/Distribution.
HISTORY
v3.01
ADD: 23 new indicators added to indicators list from the library. Current Total number of Indicators are 93. (to be continued to adding)
ADD: 2 more Parameters (P1,P2) for indicator calculation added. Par:(Use Defaults) uses only indicator(Source, Length) with library's default parameters. Par:(Use Extra Parameters P1,P2) use indicator(Source,Length,p1,p2) with additional parameters if indicator needs.
ADD: log calculation (simple, log10) option added on indicator function entries
ADD: New Output Signals added for compatibility on exporting condition signals to different Strategy templates.
ADD: Alerts Added according to conditions results
UPD: Indicator source inputs now display with indicators descriptions
UPD: Most off the source code rearranged and some functions moved to the new library. Now system work like a little bit frontend/backend
UPD: Performance improvement made on factorization and other source code
UPD: Input GUI rearranged
UPD: Tooltips corrected
REM: Extended indicators removed
UPD: IND1-IND4 added to indicator data source. Now it is possible to create new indicators with the previously defined indicators value. ex: IND1=ema(close,14) and IND2=rsi(IND1,20) means IND2=rsi(ema(close,14),20)
UPD: Custom Indicator (CUST) added to indicator data source and Combination Indicator source.
UPD: Volume added to indicator data source and Combination Indicator source.
REM: Custom indicators removed and only one custom indicator left
REM: Plot Type "Org. Range (-1,1)" removed
UPD: angle, rising, falling type operators moved to indicator library
Indicator Functions with Factor and HeikinAshiHello all,
This indicator returns below selected indicators values with entered parameters.
Also you can add factorization, functions candles, function HeikinAshi and more to the plot.
VERSION:
Version 1: returns series only source and Length with already defined default values
Version 2: returns series with source, Length, p1 and p2 parameters according to the indicator definition (ex: )
PARAMETERS p1 p2
for defining multi arguments (See indicators list) indicator input value usable with verison=V2 selected.. ex: for alma( src , len ,offset=0.85,sigma=6), set source=source, len=length, p1=0.85 an p2=6
FACTOR:
Add double triple, Quadruple factors to selected indicator (like converting EMA to 2-DEMA, 3-TEMA, 4-QEMA...)
1-Original
2-Double
3-Triple
4-Quadruple
LOG
Log: Use log, log10 on function entries
PLOTTING:
PType: Plotting type of the function on the screen
Original :use original values
Org. Range (-1,1): usable for indicators between range -1 and 1
Stochastic: Convert indicator values by using stochastic calculation between -1 & 1. (use AT/% length to better view)
PercentRank: Convert indicator values by using Percent Rank calculation between -1 & 1. (use AT/% length to better view)
ST/%: length for plotting Type for stochastic and Percent Rank options
Smooth: Use SWMA for smoothing the function
DISPLAY TYPES
Plot Candles: Display the selected indicator as candle by implementing values
Plot Ind: Display result of indicator with selected source
HeikinAshi: Display Selected indicator candles with Heikin Ashi calculation
INDICATOR LIST:
hide = 'DONT DISPLAY', //Dont display & calculate the indicator. (For my framework usage)
alma = 'alma( src , len ,offset=0.85,sigma=6)', // Arnaud Legoux Moving Average
ama = 'ama( src , len ,fast=14,slow=100)', //Adjusted Moving Average
acdst = 'accdist()', // Accumulation/distribution index.
cma = 'cma( src , len )', //Corrective Moving average
dema = 'dema( src , len )', // Double EMA (Same as EMA with 2 factor)
ema = 'ema( src , len )', // Exponential Moving Average
gmma = 'gmma( src , len )', //Geometric Mean Moving Average
hghst = 'highest( src , len )', //Highest value for a given number of bars back.
hl2ma = 'hl2ma( src , len )', //higest lowest moving average
hma = 'hma( src , len )', // Hull Moving Average .
lgAdt = 'lagAdapt( src , len ,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter
lgAdV = 'lagAdaptV( src , len ,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter variation
lguer = 'laguerre( src , len )', //Ehler's Laguerre filter
lsrcp = 'lesrcp( src , len )', //lowest exponential esrcpanding moving line
lexp = 'lexp( src , len )', //lowest exponential expanding moving line
linrg = 'linreg( src , len ,loffset=1)', // Linear regression
lowst = 'lowest( src , len )', //Lovest value for a given number of bars back.
pcnl = 'percntl( src , len )', //percentile nearest rank. Calculates percentile using method of Nearest Rank.
pcnli = 'percntli( src , len )', //percentile linear interpolation. Calculates percentile using method of linear interpolation between the two nearest ranks.
rema = 'rema( src , len )', //Range EMA (REMA)
rma = 'rma( src , len )', //Moving average used in RSI . It is the exponentially weighted moving average with alpha = 1 / length.
sma = 'sma( src , len )', // Smoothed Moving Average
smma = 'smma( src , len )', // Smoothed Moving Average
supr2 = 'super2( src , len )', //Ehler's super smoother, 2 pole
supr3 = 'super3( src , len )', //Ehler's super smoother, 3 pole
strnd = 'supertrend( src , len ,period=3)', //Supertrend indicator
swma = 'swma( src , len )', //Sine-Weighted Moving Average
tema = 'tema( src , len )', // Triple EMA (Same as EMA with 3 factor)
tma = 'tma( src , len )', //Triangular Moving Average
vida = 'vida( src , len )', // Variable Index Dynamic Average
vwma = 'vwma( src , len )', // Volume Weigted Moving Average
wma = 'wma( src , len )', //Weigted Moving Average
angle = 'angle( src , len )', //angle of the series (Use its Input as another indicator output)
atr = 'atr( src , len )', // average true range . RMA of true range.
bbr = 'bbr( src , len ,mult=1)', // bollinger %%
bbw = 'bbw( src , len ,mult=2)', // Bollinger Bands Width . The Bollinger Band Width is the difference between the upper and the lower Bollinger Bands divided by the middle band.
cci = 'cci( src , len )', // commodity channel index
cctbb = 'cctbbo( src , len )', // CCT Bollinger Band Oscilator
chng = 'change( src , len )', //Difference between current value and previous, source - source.
cmo = 'cmo( src , len )', // Chande Momentum Oscillator . Calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movement over the same period.
cog = 'cog( src , len )', //The cog (center of gravity ) is an indicator based on statistics and the Fibonacci golden ratio.
cpcrv = 'copcurve( src , len )', // Coppock Curve. was originally developed by Edwin "Sedge" Coppock (Barron's Magazine, October 1962).
corrl = 'correl( src , len )', // Correlation coefficient . Describes the degree to which two series tend to deviate from their ta. sma values.
count = 'count( src , len )', //green avg - red avg
dev = 'dev( src , len )', //ta.dev() Measure of difference between the series and it's ta. sma
fall = 'falling( src , len )', //ta.falling() Test if the `source` series is now falling for `length` bars long. (Use its Input as another indicator output)
kcr = 'kcr( src , len ,mult=2)', // Keltner Channels Range
kcw = 'kcw( src , len ,mult=2)', //ta.kcw(). Keltner Channels Width. The Keltner Channels Width is the difference between the upper and the lower Keltner Channels divided by the middle channel.
macd = 'macd( src , len )', // macd
mfi = 'mfi( src , len )', // Money Flow Index
nvi = 'nvi()', // Negative Volume Index
obv = 'obv()', // On Balance Volume
pvi = 'pvi()', // Positive Volume Index
pvt = 'pvt()', // Price Volume Trend
rise = 'rising( src , len )', //ta.rising() Test if the `source` series is now rising for `length` bars long. (Use its Input as another indicator output)
roc = 'roc( src , len )', // Rate of Change
rsi = 'rsi( src , len )', // Relative strength Index
smosc = 'smi_osc( src , len ,fast=5, slow=34)', //smi Oscillator
smsig = 'smi_sig( src , len ,fast=5, slow=34)', //smi Signal
stdev = 'stdev( src , len )', //Standart deviation
trix = 'trix( src , len )' , //the rate of change of a triple exponentially smoothed moving average .
tsi = 'tsi( src , len )', //True Strength Index
vari = 'variance( src , len )', //ta.variance(). Variance is the expectation of the squared deviation of a series from its mean (ta. sma ), and it informally measures how far a set of numbers are spread out from their mean.
wilpc = 'willprc( src , len )', // Williams %R
wad = 'wad()', // Williams Accumulation/Distribution .
wvad = 'wvad()' //Williams Variable Accumulation/Distribution
I will update the indicator list when I will update the library
Thanks to tradingview, @RodrigoKazuma for their open source indicators
lib_Indicators_v2_DTULibrary "lib_Indicators_v2_DTU"
This library functions returns included Moving averages, indicators with factorization, functions candles, function heikinashi and more.
Created it to feed as backend of my indicator/strategy "Indicators & Combinations Framework Advanced v2 " that will be released ASAP.
This is replacement of my previous indicator (lib_indicators_DT)
I will add an indicator example which will use this indicator named as "lib_indicators_v2_DTU example" to help the usage of this library
Additionally library will be updated with more indicators in the future
NOTES:
Indicator functions returns only one series :-(
plotcandle function returns candle series
INDICATOR LIST:
hide = 'DONT DISPLAY', //Dont display & calculate the indicator. (For my framework usage)
alma = 'alma(src,len,offset=0.85,sigma=6)', //Arnaud Legoux Moving Average
ama = 'ama(src,len,fast=14,slow=100)', //Adjusted Moving Average
acdst = 'accdist()', //Accumulation/distribution index.
cma = 'cma(src,len)', //Corrective Moving average
dema = 'dema(src,len)', //Double EMA (Same as EMA with 2 factor)
ema = 'ema(src,len)', //Exponential Moving Average
gmma = 'gmma(src,len)', //Geometric Mean Moving Average
hghst = 'highest(src,len)', //Highest value for a given number of bars back.
hl2ma = 'hl2ma(src,len)', //higest lowest moving average
hma = 'hma(src,len)', //Hull Moving Average.
lgAdt = 'lagAdapt(src,len,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter
lgAdV = 'lagAdaptV(src,len,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter variation
lguer = 'laguerre(src,len)', //Ehler's Laguerre filter
lsrcp = 'lesrcp(src,len)', //lowest exponential esrcpanding moving line
lexp = 'lexp(src,len)', //lowest exponential expanding moving line
linrg = 'linreg(src,len,loffset=1)', //Linear regression
lowst = 'lowest(src,len)', //Lovest value for a given number of bars back.
pcnl = 'percntl(src,len)', //percentile nearest rank. Calculates percentile using method of Nearest Rank.
pcnli = 'percntli(src,len)', //percentile linear interpolation. Calculates percentile using method of linear interpolation between the two nearest ranks.
rema = 'rema(src,len)', //Range EMA (REMA)
rma = 'rma(src,len)', //Moving average used in RSI. It is the exponentially weighted moving average with alpha = 1 / length.
sma = 'sma(src,len)', //Smoothed Moving Average
smma = 'smma(src,len)', //Smoothed Moving Average
supr2 = 'super2(src,len)', //Ehler's super smoother, 2 pole
supr3 = 'super3(src,len)', //Ehler's super smoother, 3 pole
strnd = 'supertrend(src,len,period=3)', //Supertrend indicator
swma = 'swma(src,len)', //Sine-Weighted Moving Average
tema = 'tema(src,len)', //Triple EMA (Same as EMA with 3 factor)
tma = 'tma(src,len)', //Triangular Moving Average
vida = 'vida(src,len)', //Variable Index Dynamic Average
vwma = 'vwma(src,len)', //Volume Weigted Moving Average
wma = 'wma(src,len)', //Weigted Moving Average
angle = 'angle(src,len)', //angle of the series (Use its Input as another indicator output)
atr = 'atr(src,len)', //average true range. RMA of true range.
bbr = 'bbr(src,len,mult=1)', //bollinger %%
bbw = 'bbw(src,len,mult=2)', //Bollinger Bands Width. The Bollinger Band Width is the difference between the upper and the lower Bollinger Bands divided by the middle band.
cci = 'cci(src,len)', //commodity channel index
cctbb = 'cctbbo(src,len)', //CCT Bollinger Band Oscilator
chng = 'change(src,len)', //Difference between current value and previous, source - source .
cmo = 'cmo(src,len)', //Chande Momentum Oscillator. Calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movement over the same period.
cog = 'cog(src,len)', //The cog (center of gravity) is an indicator based on statistics and the Fibonacci golden ratio.
cpcrv = 'copcurve(src,len)', //Coppock Curve. was originally developed by Edwin "Sedge" Coppock (Barron's Magazine, October 1962).
corrl = 'correl(src,len)', //Correlation coefficient. Describes the degree to which two series tend to deviate from their ta.sma values.
count = 'count(src,len)', //green avg - red avg
dev = 'dev(src,len)', //ta.dev() Measure of difference between the series and it's ta.sma
fall = 'falling(src,len)', //ta.falling() Test if the `source` series is now falling for `length` bars long. (Use its Input as another indicator output)
kcr = 'kcr(src,len,mult=2)', //Keltner Channels Range
kcw = 'kcw(src,len,mult=2)', //ta.kcw(). Keltner Channels Width. The Keltner Channels Width is the difference between the upper and the lower Keltner Channels divided by the middle channel.
macd = 'macd(src,len)', //macd
mfi = 'mfi(src,len)', //Money Flow Index
nvi = 'nvi()', //Negative Volume Index
obv = 'obv()', //On Balance Volume
pvi = 'pvi()', //Positive Volume Index
pvt = 'pvt()', //Price Volume Trend
rise = 'rising(src,len)', //ta.rising() Test if the `source` series is now rising for `length` bars long. (Use its Input as another indicator output)
roc = 'roc(src,len)', //Rate of Change
rsi = 'rsi(src,len)', //Relative strength Index
smosc = 'smi_osc(src,len,fast=5, slow=34)', //smi Oscillator
smsig = 'smi_sig(src,len,fast=5, slow=34)', //smi Signal
stdev = 'stdev(src,len)', //Standart deviation
trix = 'trix(src,len)' , //the rate of change of a triple exponentially smoothed moving average.
tsi = 'tsi(src,len)', //True Strength Index
vari = 'variance(src,len)', //ta.variance(). Variance is the expectation of the squared deviation of a series from its mean (ta.sma), and it informally measures how far a set of numbers are spread out from their mean.
wilpc = 'willprc(src,len)', //Williams %R
wad = 'wad()', //Williams Accumulation/Distribution.
wvad = 'wvad()' //Williams Variable Accumulation/Distribution.
}
f_func(string, float, simple, float, float, float, simple) f_func Return selected indicator value with different parameters. New version. Use extra parameters for available indicators
Parameters:
string : FuncType_ indicator from the indicator list
float : src_ close, open, high, low,hl2, hlc3, ohlc4 or any
simple : int length_ indicator length
float : p1 extra parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p2 extra parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p3 extra parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators
Returns: float Return calculated indicator value
fn_heikin(float, float, float, float) fn_heikin Return given src data (open, high,low,close) as heikin ashi candle values
Parameters:
float : o_ open value
float : h_ high value
float : l_ low value
float : c_ close value
Returns: float heikin ashi open, high,low,close vlues that will be used with plotcandle
fn_plotFunction(float, string, simple, bool) fn_plotFunction Return input src data with different plotting options
Parameters:
float : src_ indicator src_data or any other series.....
string : plotingType Ploting type of the function on the screen
simple : int stochlen_ length for plotingType for stochastic and PercentRank options
bool : plotSWMA Use SWMA for smoothing Ploting
Returns: float
fn_funcPlotV2(string, float, simple, float, float, float, simple, string, simple, bool, bool) fn_funcPlotV2 Return selected indicator value with different parameters. New version. Use extra parameters fora available indicators
Parameters:
string : FuncType_ indicator from the indicator list
float : src_data_ close, open, high, low,hl2, hlc3, ohlc4 or any
simple : int length_ indicator length
float : p1 extra parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p2 extra parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p3 extra parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators
string : plotingType Ploting type of the function on the screen
simple : int stochlen_ length for plotingType for stochastic and PercentRank options
bool : plotSWMA Use SWMA for smoothing Ploting
bool : log_ Use log on function entries
Returns: float Return calculated indicator value
fn_factor(string, float, simple, float, float, float, simple, simple, string, simple, bool, bool) fn_factor Return selected indicator's factorization with given arguments
Parameters:
string : FuncType_ indicator from the indicator list
float : src_data_ close, open, high, low,hl2, hlc3, ohlc4 or any
simple : int length_ indicator length
float : p1 parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p2 parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p3 parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators
simple : int fact_ Add double triple, Quatr factor to selected indicator (like converting EMA to 2-DEMA, 3-TEMA, 4-QEMA...)
string : plotingType Ploting type of the function on the screen
simple : int stochlen_ length for plotingType for stochastic and PercentRank options
bool : plotSWMA Use SWMA for smoothing Ploting
bool : log_ Use log on function entries
Returns: float Return result of the function
fn_plotCandles(string, simple, float, float, float, simple, string, simple, bool, bool, bool) fn_plotCandles Return selected indicator's candle values with different parameters also heikinashi is available
Parameters:
string : FuncType_ indicator from the indicator list
simple : int length_ indicator length
float : p1 parameter-1. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p2 parameter-2. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
float : p3 parameter-3. active on Version 2 for defining multi arguments indicator input value. ex: lagAdapt(src_, length_,LAPercLen_=p1,FPerc_=p2)
simple : int version_ indicator version for backward compatibility. V1:dont use extra parameters p1,p2,p3 and use default values. V2: use extra parameters for available indicators
string : plotingType Ploting type of the function on the screen
simple : int stochlen_ length for plotingType for stochastic and PercentRank options
bool : plotSWMA Use SWMA for smoothing Ploting
bool : log_ Use log on function entries
bool : plotheikin_ Use Heikin Ashi on Plot
Returns: float
REAL MARKET RATESBased on near and long term eurodollar yield curve. Attempts to indicate, through a spread median, how major players may position themselves in the near future.
Standard Error of the Estimate -Jon Andersen- V2Original implementation idea of bands by:
Traders issue: Stocks & Commodities V. 14:9 (375-379):
Standard Error Bands by Jon Andersen
Standard Error Bands are quite different than Bollinger's.
First, they are bands constructed around a linear regression curve.
Second, the bands are based on two standard errors above and below this regression line.
The error bands measure the standard error of the estimate around the linear regression line.
Therefore, as a price series follows the course of the regression line the bands will narrow , showing little error in the estimate. As the market gets noisy and random, the error will be greater resulting in wider bands .
Thanks to the work of @glaz & @XeL_arjona
In this version you can change the type of moving averages and the source of the bands.
Add a few studies of @dgtrd
1- ADX Colored Directional Movement Line
Directional Movement (DMI) (created by J. Welles Wilder ) consists of the Average Directional Index ( ADX ), to define whether or not there is a trend present, and Plus Directional Indicator (+D I) and Minus Directional Indicator (-D I) serve the purpose of determining trend direction
ADX Colored Directional Movement Line is custom interpretation of Directional Movement (DMI) with aim to present all 3 DMI indicator components with SINGLE line and ability to be added on top of the price chart (main chart)
How to interpret :
* triangle shapes:
▲- bullish : diplus >= diminus
▼- bearish : diplus < diminus
* colors:
green - bullish trend : adx >= strongTrend and di+ > di-
red - bearish trend : adx >= strongTrend and di+ < di-
gray - no trend : weekTrend < adx < strongTrend
yellow - week trend : adx < weekTrend
* color density:
darker : adx growing
lighter : adx falling
2- Volatility Colored Price/MA Line
Custom interpretation of the idea “Prices high above the moving average (MA) or low below it are likely to be remedied in the future by a reverse price movement”. Further details can be found under study “Price Distance to its MA by DGT”
How to interpret :
-▲ – Bullish , Price Action above Moving Average
-▼ – Bearish , Price Action below Moving Average
-Gray/Black - Low Volatility
-Green/Red – Price Action in Threshold Bands
-Dark Green/Red – Price Action Exceeds Threshold Bands
3- Volume Weighted Bar s
Volume Weighted Bars, a study of Kıvanç Özbilgiç, aims to present whether volume supports price movements. Volume Weighted Bars are calculated based on volume moving average.
How to interpret :
-Volume high above the volume moving average be displayed with red/green colors
-Average volume values will remain as they are and
-Volume low below the volume moving average will be indicated with darker colors
4- Fear & Greed index value, using technical anlysis approach calculated based on :
⮩1 - Price Momentum : Price Distance to its Moving Average
⮩2 - Strenght : Rate of Return, price movement over a period of time
⮩3 - Money Flow : Chaikin Money Flow, quantify changes in buying and selling pressure. CMF calculations is based on Accumulation/Distribution
⮩4 - Market Volatility : CBOE Volatility Index ( VIX ), the Volatility Index, or VIX , is a real-time market index that represents the market's expectation. It provides a measure of market risk and investors' sentiments
⮩5 -Safe Haven Demand: in this study GOLD demand is assumed
M8 BUY @ END OF DAYI've read a couple of times at a couple of different places that most of the move in the market happens after hours, meaning during non-standard trading hours.
After-market and pre-market hours and have seen data presented showing that systems which bought just before end normal market hours and sold the next morning had really amazing resutls.
But when testing those I found the results to be quite poor compared to the pretty graphs I saw, and after much tweaking and trying different ideas I gave up on the idea until I recently decided to try a new position management system.
The System
Buys at the end of the trading day before the close
Sells the next morning at the open IF THE CLOSE OF THE CURRENT BAR IS HIGHER THAN THE ENTRY PRICE
When the current price is not higher, the system will keep the position open until it EITHER gets stops out or closes on profit <<< this is WHY it has the high win %
The system has a high win ratio because it will keep that one position open until it either reaches profit or stops out
This "system" of waiting, and keeping the trade open, actually turned out to be a fantastic way to kind of put the complete trading strategy in a kind of limbo mode. It either waits for market failure or for a profit.
I don't really care about win % at all, almost always high win % ratio systems are just nonsense. What I look for is a PF -- profit factor of 1.5 or above, and a relatively smooth equity curve. -- This has both.
The Stop Loss setting is set @ .95, meaning a 5% stop loss. The Red Line on the chart is the stop loss line.
There is no set profit target -- it simply takes what the market gives.
Non-Repainting System
This does use a 200D Simple Moving Average as a filter. Like a Green Light / Red Light traffic light, the system will only trade long when the price is above its 200 Moving average.
Here is the code: "F1 = close > sma(security(syminfo.tickerid, "D", close ), MarketFilterLen) // HIGH OF OLD DATA -- SO NO REPAINTING"
I use "close ", so that's data from two days ago, it's fixed, confirmed, non-repainting data from the higher timeframe.
-- I would only suggest using this on direction tickers like SPY, QQQ, SSO, TQQQ, market sectors with additional filters in place.
{Gunzo} Trend Sniper (WMA with coefficient)Trend Sniper is a trend-following indicator that sticks closer to the trend than others moving averages as it is using an upgraded weighted moving average implementation.
OVERVIEW :
It is typical to use a moving average indicator (SMA, EMA, WMA or TMA) to identify the trend of an asset. Standard moving averages indicators smooth the price and doesn’t stick very closely to the actual price, showing potential lagging information.
CALCULATION :
In order to have a trendline that sticks to the price, we are going to use a weighted moving average as it puts more weight on recent candles and less on past candles. The weight is usually calculated using the distance from current candle to the other candles used in the calculation. We have the following formula for the standard calculation as implemented in TradingView :
WMA_standard = (Price1 * Weight1 + …… + PriceN * WeightN)) / (Weight1 + …… + WeightN)
This “Trend Sniper” indicator uses an additional coefficient to alter even more the weight of each candle.
WMA_with_coefficient = (Price1 * (Weight1 - Coefficient) + …… + PriceN * (WeightN - Coefficient)) / ((Weight1 - Coefficient) + …… + (WeightN - Coefficient))
SETTINGS :
MA source : Source used for moving average calculation (ex : “close”)
MA length : Length of the moving average. Higher values will give a smoother line, lower values will give a more reactive line.
Use extra smoothing : Enable/disable usage of a EMA to extra smooth the line curve. If activated the indicator may be lagging, but it will also avoid many false buy/sell signals.
MA extra smoothing length : Length of the moving average of the extra smoothing.
Change candle colors : Enable/disable painting the candles of the chart with the colors of the weighted moving average.
Display buy/sell signals : Display buy/sell signals (circles) when the moving average is changing direction
VISUALIZATIONS :
This indicator has 3 possible visualizations :
Moving Average line : the line represents the weighted moving average that is following the price of the asset, when the line goes up we are in a uptrend (green line) when the line goes down we are in a downtrend (red line).
Candle coloring : the color of the moving average line can be applied to the candles of the chart for better readability.
Signals : Buy/Sell signals can be displayed at the bottom of the chart
USAGE :
This indicator can help analyze the trend directional changes :
First of all, if the moving average line is under the price (or above the price), then we can assume that the uptrend is strong (or downtrend is strong).
If the current candle crosses the moving average line, it is the first sign that the trend is weakening and possibly starting to revert.
If the weighted moving average is changing direction, then the trend change is confirmed and the color of the line changes
LV Cop&RMA w HeikinA buy signal is generated when the indicator turns upwards from previous indicator level.
A sell signal is generated when the indicator turns downwards from previous indicator level.
The indicator is trend-following, and based on averages, so by its nature it doesn't pick a bottom, but rather shows when a rally has started.
It is designed for daily period use.
Frequent buy/sell signals can occur on low and high levels.
It is designed with the mentality of Coppock curve. Rma is used instead of Wma and Heikin-Ashi closing price is used instead of standard closing price.
