Weighted Average Volume Depth [QuantraSystems]Weighted Average Volume Depth
Introduction
The Weighted Average Volume Depth (𝓦𝓐𝓥𝓓) indicator is calibrated to provide extensive insights, calculated using volumetric price action and volume depth, and provides dynamic adjustments based upon historical volatility.
This indicator is a valuable asset for traders and investors, aiming to capture trends, measure dynamic volatility, and provide market reversion analysis in a systematic way.
Legend
Volumetric Top Cap: Plotted at y = 0, this line represents the probabilistic maximum value, or ‘cap’ for the signal line. It is colored using a binary color scheme, and indicates the dominant trend direction - green for an uptrend and purple for a downtrend.
Base Line: Calculated using a volume-weighted volatility measurement, this line is used as the benchmark to calculate momentum in the 𝓦𝓐𝓥𝓓 indicator.
Signal Line: The signal line represents the volume and volatility weighted measurements, and oscillates between the Base Line and Top Cap. Its position between these levels provides the depth of insights available in this script.
When the signal line is remaining in close proximity to the base line, this is indicative of a low volatility market environment. These periods are also reflected as muted bar coloring when the ‘Trend Intensity’ setting is enabled.
Conversely, when the signal line approaches, or even breaks above the Top Cap, this is characteristic of an unsustainable trending action - and probabilistically speaking, a reversion or consolation is likely to occur at these levels.
Highlighting: When this setting is enabled, background coloring is applied when the Signal Line breaks above the Top Cap. This highlights green as an oversold zone, and purple as an overbought zone.
Reversal Signals: When price begins to reverse from a zone of overextension, a signal is plotted when this reversion occurs from a high probability zone.
Circle - Shows a possible bullish reversal.
Cross - Shows a possible bearish reversal.
Case Study
In the above image, we showcase three distinct trades in short succession, showcasing the 𝓦𝓐𝓥𝓓’s speed and accuracy under the right conditions.
The first long trade was initiated upon receiving a bullish reversal signal. The trade was then closed after the price experienced a sharp upwards movement - and an overbought signal was indicated by the purple shading.
The second, short trade was entered on the next bar, after a bearish reversal signal was printed by the indicator (a white cross). Similarly, this trade was closed upon the oversold signal.
Once again, a reversal signal was indicated by the 𝓦𝓐𝓥𝓓 indicator. This time a bullish signal (a white circle), and hence a long position was opened. However, this trade was held until a negative trend confirmation (signaled by the Top Cap’s shift in color). This makes apparent the indicator’s flexible nature, and showcases the multiple signaling types available for traders to use.
Recommended Settings
The optimal settings for the 𝓦𝓐𝓥𝓓 indicator will vary upon the chosen asset’s average level volatility, as well as the timeframe it is applied to.
Due to increased volatility levels on lower timeframes, it is recommended to increase the 'Top Cap Multiplier' to take into account the increased frequency of false signals found in these trading environments. The same can be said when used on highly volatile assets - a trader will likely benefit from using a higher 'Top Cap Multiplier.'
On more price-stable assets, as well as any asset on higher timeframes, there is merit to tightening the length of the 'Top Cap Multiplier,' due to the slower nature of price action.
Methodology
The 𝓦𝓐𝓥𝓓 starts with calculating the volume weighted average price and the volume weighted variance - which is the expectation of the squared deviation of a variable from its mean, giving insights into the distribution of trading volume.
Using the volume weighted variance, a standard deviation value is calculated based on user input. This value acts as the ‘Volumetric Top Cap’ - seen in the 𝓦𝓐𝓥𝓓 indicator window as the zero line.
The signal line is calculated as the difference between the current price and the theoretical upper or lower VWAP deviation bands. This line acts as the trigger for identifying prevailing trends and high probability reversal points.
The base line serves as a reference point for historical momentum. It is calculated using an exponential moving average of the lowest signal line values over a defined lookback period. This baseline helps in assessing whether the current momentum is high or low relative to historical norms.
Notes
Bar coloring can be turned off - especially useful when stacking multiple indicators as recommended, or set to 'Trend Intensity,' or 'Binary Trend' (which reflects the top cap coloring).
It is always recommended to never rely on a single indicator - and instead build and test multiple strategies utilizing more than one indicator as confirmation.
Investing
Pulse Profiler [QuantraSystems]Pulse Profiler
Introduction
The Pulse Profiler ( ℙℙ ) is specifically designed to unambiguously indicate weakening momentum after a strong impulse. The upper and lower standard deviation bands also allow the user to assess the strength of an impulse and differentiate it from general noise.
Due to the ℙℙ ’s rapid responsiveness to exhaustion in price movement it is ideally used for the trader to recognize when to start taking profit when combined with other indicators.
The novum is that by dynamically balancing its sensitivity to recent movements the ℙℙ considers the asset’s inherent volatility. By reducing noise without sacrificing signal, and by visualizing it in our typical modern QuantraAI style, the ℙℙ enhances the traders’ ability to distinguish impulses with weakening momentum from strong trending movements.
Legend
Impulse: The ℙℙ showing strength based on momentum and volume.
Dynamic standard deviation bands: Rolling probability based bands based on a rolling normal distribution. Adjustable, recommended are σ = 1.5 to σ = 2.5.
Neutral lines: Dynamic thresholds which get often respected as support or resistance.
Case Study
To properly employ the ℙℙ , the trader should use it to identify out-of-the-ordinary 𝓲𝓶𝓹𝓾𝓵𝓼𝓮𝓼 which cause a following exhaustion.
The rolling standard deviation bands incorporate the asset’s historical behavior in regards to its inherent volatility on a rolling basis. If the asset shows strong 𝓲𝓶𝓹𝓾𝓵𝓼𝓮𝓼 that go beyond the rolling standard deviation, the event has been highly improbable. The trader then needs to determine if the price change was caused by critical external factors. If not, it is highly probable that the momentum exhausts and that price movement plateaus to enter a range.
These signals indicate that it is highly probable that closing a position upon these conditions is the correct choice.
If the 𝓲𝓶𝓹𝓾𝓵𝓼𝓮 reverses and retraces into the opposite direction, while moving more than 1.5σ across just 3 bars on the 4H chart, the signal indicates that a reversal is pushing the price down – in both momentum and volume.
A sharp reversal thus becomes more probable than not.
The ℙℙ can also be calibrated to find possible trend exhaustions on a longer timeframe (1D).
Please always use multiple Quantra indicators to add confirmations to your signals.
Recommended Settings
Swing Trading (4H chart)
Standard Deviation Lookback: 150
Standard Deviation Multiplier (σ): 2.5
Display Variant: Classic
Choose Mode for Bar Coloring: Signal
Trend exhaustion (1D chart)
Standard Deviation Lookback: 200
Standard Deviation Multiplier (σ): 2.0
Display Variant: Classic
Choose Mode for Bar Coloring: Extremes
Notes
Quantra Standard Value Contents:
The Heikin-Ashi (HA) candle visualization smoothes out the signal line to provide more informative insights into momentum and trends. This allows earlier entries and exits by observing the indicator values transformed by the HA.
Various visualization options are available to adjust the indicator to the user’s preference: Aside from HA, a classic line, or a hybrid of both.
A special feature of Quantra’s indicators is that they are probabilistically built - therefore they work well as confluence and can easily be stacked to increase signal accuracy.
To add to Quantra's indicators’ utility we have added the option to change the price bars colors based on different signals:
Choose Mode for Coloring
Trend Following (Indicator above mid line counts as uptrend, below is downtrend)
Extremes (Everything beyond the SD bands is highlighted to signal mean reversion)
Candles (Color of HA candles as barcolor)
Reversions (Only for HA) (Reversion Signals via the triangles if HA candles change trend while beyond the SD bands, high probability entries/exits)
The ℙℙ is also sensitive to divergences for those interested in utilizing this feature.
Through a special combination of price, volume and momentum you get a holistic overview on the impulse strengths of movements.
The two neutral lines in the center act as dynamic, volume and volatility adjusted thresholds. Often the signal line respects them as support and resistance.
The upper and lower standard deviation lines express the rarity of an impulse based on the asset’s inherent volatility.
The indicator needs a long enough timespan to build up its probability estimation, therefore the asset needs sufficient price history.
The indicator requires thorough volume data. If the source of an asset pair does not forward it, try to find another source or exchange for the same pair.
Signal Mode on the 4H chart is a relevant part of this indicator when used in isolation and helps to analyze momentum adjusted by volatility.
Methodology
The ℙℙ combines the Arnaud Legoux Moving Average (ALMA) with a bespoke volume and momentum calculation, with a classical Exponential Moving Average (EMA) on price data.
The ℙℙ itself integrates ALMA for volume and momentum with an EMA calculation on price, creating a unique blend that expresses impulses using their three raw main components.
The indicator calculates dynamic standard deviation bands based on an adjustable lookback period and the adjustable sigma (σ), to signal when the impulse strength is just uncommon or even extraordinary when compared to the usual price movements:
σ = 1.5 the probability of similar impulse strength occuring is 13.37% / 2, hence ~ 6.69%
σ = 2.0 the probability of similar impulse strength occuring is ~ 2.28%
σ = 2.5 the probability of similar impulse strength occuring is ~ 0.62%
By detecting extremely improbable conditions the indicator can create an inversely highly probable signal to its user.
Neutral bands are calculated based on the ℙℙ alongside a rolling, dynamic multiplier. This effectively provides dynamic thresholds for approximating common volatility.
Heikin Ashi method: The indicator uses a custom function to calculate Heikin Ashi values, useful for smoothing impulse data and identifying trends.
Reversion Signals: Specifically for Heikin Ashi displays, we plot triangles as signals, useful to easily spot potential reversals.
The Signal Mode uses these different thresholds to highlight significant market moves.
Rate of Change Suite [QuantraSystems]Rate of Change Suite
Introduction
The "Rate of Change Suite" (𝓡𝓸𝓒 𝓢𝓾𝓲𝓽𝓮) refines traditional RoC concepts by incorporating additional elements that provide more nuanced views of market trends, potential reversions, and momentum shifts.
Its main benefits are that it allows traders to detect momentum changes and frontrun trend shifts.
The suite is designed to be highly adaptable, catering to various trading styles, timeframes and market conditions. It is comprised of 3 metrics:
The RoC base line plots the rate of change, the Signal Histogram to confirm trends, and the Signal Confirmation Oscillator to inform reversal probabilities. For the early detection of trend shifts, the 𝓡𝓸𝓒 𝓢𝓾𝓲𝓽𝓮 is a comprehensive tool for the toolkit of modern traders.
A core component of the 𝓡𝓸𝓒 𝓢𝓾𝓲𝓽𝓮 is the ability to apply its processing techniques to any other indicator found on TradingView - essentially leveraging the signal power of existing analysis methods. This is achieved by modifying the ‘Source’ input.
Legend
𝓡𝓸𝓒 base line: The primary component of the suite, the RoC Line, offers a direct view of market momentum. An upward trending RoC line informs the potential for a long position, while a downward trend might signal the opportunity for a short position. Both include a secondary confirmation by the color change of the line itself. The Heikin Ashi transformed version of the RoC line provides greater resistance to rapid movements, or outliers.
Signal Histogram: This feature works in tandem with the base RoC Line, providing an additional third confirmation of trends. A rising histogram supports the presence of an upward trend. Conversely, a declining histogram aligns with downward trends.
Signal Confirmation Oscillator: This dotted-line is crucial for detecting peaks or troughs in market momentum: These can precede reversals or shifts in the prevailing trend. Traders can use this signal to anticipate and prepare for potential changes quicker than others.
Case Study
Primarily a tool to follow trends, the 𝓡𝓸𝓒 𝓢𝓾𝓲𝓽𝓮 implies much more – you can trade with a confirmed trend signal entry and a mean reversion signal for the exit:
Here we see two practical cases of the 𝓡𝓸𝓒 𝓢𝓾𝓲𝓽𝓮 on the 1h BTC chart.
In the first scenario, the trader waits for three confirmations from the indicator.
The 𝓡𝓸𝓒 baseline to lead the run and looks for confirmation two and three:
𝓡𝓸𝓒 base line color shifts
and the Signal Histogram follows past the null midline.
The trader has adjusted their risk beforehand and enters the long position.
The 𝓡𝓸𝓒 𝓢𝓾𝓲𝓽𝓮 shows traders when to take profit:
The Signal Confirmation Oscillator (SCO, dotted line) moves beyond the 𝓡𝓸𝓒 baseline and the Signal Histogram. The trader can take 50% of the profit already.
The trader waits patiently, and if the SCO reverses, the rest of the position is closed.
The same works inversely for the second trade, which successfully frontran the decline shortly after.
Recommended Settings
Day Trading (1H chart)
Length: 30
Smooth Length: 10
Display Variant: Classic
Choose Mode: Trend Following
Investing – Follow Trend (1D chart)
Default settings
Notes
Quantra Standard Value Contents:
The Heikin-Ashi (HA) candle visualization smoothes out the signal line to provide more informative insights into momentum and trends. This allows earlier entries and exits by observing the indicator values transformed by the HA.
Various visualization options are available to adjust the indicator to the user’s preference: Aside from HA, a classic line, or a hybrid of both.
A special feature of Quantra’s indicators is that they are probabilistically built - therefore they work well as confluence and can easily be stacked to increase signal accuracy.
To add to Quantra's indicators’ utility we have added the option to change the price bars’ colors based on different signals:
Choose Mode for Coloring
Trend Following (Indicator above mid line counts as uptrend, below is downtrend)
Extremes (Everything beyond the SD bands is highlighted to signal mean reversion)
Candles (Color of HA candles as barcolor)
Reversions (Only for HA) (Reversion Signals via the triangles if HA candles change trend while beyond the SD bands, high probability entries/exits)
Divergence Sensitivity: Quantra’s 𝓡𝓸𝓒 𝓢𝓾𝓲𝓽𝓮 is finely tuned to detect divergences, a key feature for identifying possible trend reversals.
Trend Following and Reversions: Primarily a tool for trend following, the 𝓡𝓸𝓒 𝓢𝓾𝓲𝓽𝓮 is also adept at spotting potential reversions and slowdowns in momentum.
Range Trading Compatibility: In its Heikin Ashi Candles mode, the suite becomes particularly effective for range trading strategies.
High Customizability: Traders can customize the suite with various visualization options, including classic line representation, HA transformation, and bar coloring. These can be based on Heikin Ashi Candles or Trend Following approaches, providing flexibility to adapt to different trading scenarios.
Methodology
The 𝓡𝓸𝓒 𝓢𝓾𝓲𝓽𝓮 is built on a foundation of functions that define and calculate the Rate of Change. They employ a variety of moving average types (SMA, EMA, DEMA, TEMA, WMA, etc.) which can be selected to optimize the RoC line.
A bespoke function to calculate Heikin-Ashi values is engineered to offer a more consistent view of the trend.
The Signal Histogram is derived by mathematically processing the base RoC signal. The Signal Confirmation Oscillator is based on a modified formula, adjusted to align with the RoC dynamics.
With a range of customization options for its visual presentation, including color schemes and display styles, the 𝓡𝓸𝓒 𝓢𝓾𝓲𝓽𝓮 is designed to cater to both trend following indications as well as finding signals for mean reversion trades. This multifaceted approach enables the 𝓡𝓸𝓒 𝓢𝓾𝓲𝓽𝓮 to allow the trader to combine signals of both types to de-risk his positions.
Regression Sloped RSI [QuantraSystems]Regression Sloped RSI
Introduction
The Regression Sloped RSI (𝓡𝓢-𝓡𝓢𝓘) enhances the classical RSI by incorporating a form of linear regression analysis, which adjusts the traditional RSI in relation to the calculated slope over a specified lookback period.
Its innovative approach reduces the occurrence of false signals compared to the classical RSI. Furthermore, it is particularly effective in markets characterized by strong trends. This is because it responds faster while retaining a high level of whipsaw resistance. The Heikin-Ashi style processing is critical to this.
It also provides robust reversal signals from dynamic overbought and oversold zones to further enhance mean-reversion trading.
Legend
The coloring of the 𝓡𝓢-𝓡𝓢𝓘 changes based on trend direction: A bright green when upwards, lilac when downwards. The strength of the trend is expressed in its distance to Null. Its acceleration is found in the Heikin-Ashi (HA) candles.
The 𝓡𝓢-𝓡𝓢𝓘 in combination with the HA bars can be used to achieve earlier entries, when the former passes across the latter in an obvious divergence.
Case Study
In this example the 𝓡𝓢-𝓡𝓢𝓘 is used to make a few intra-day trades on the Ethereum 15 minute chart. Each trade was open for approximately 5 hours. On the first trade we enter a long in an early entry. The indicator gives us three confirmations which we should all check for. First we have a positive candle developing, secondly the 𝓡𝓢-𝓡𝓢𝓘 (line) rises above the Heikin-Ashi candles, thirdly the classical RSI (the saturated surface in the background) rises as well.
The trader should then calculate their position sizing responsibly and enter into a short daytrade. Please always have invalidation rules, for example a) if the initial HA candle closes negative b) you can place your stop loss at 1SD into the opposite direction.
Always use adequate risk management, never risk more than 1% of your portfolio, unless you are a seasoned trader with your own calculated position sizes.
Always forward test your rules, assets, timeframe and settings sufficiently.
It is always recommended to use multiple Quantra indicators to add confirmations to your signals - this is by design.
Recommended Settings
Please reset to defaults before enabling recommended settings.
Intra-Day Trading (15min chart)
RSI Length: 22
LR Length: 25
Smoothing: EMA
Toggle SD Bands: On
Mode for Coloring: Candles
Trend Following (4H chart)
RSI Length: 40
LR Length: 35
Smoothing: LSMA
Toggle SD Bands: Off
Mode for Coloring: Extremes or Trend Following
Notes
Quantra Standard Value Contents:
The Heikin-Ashi (HA) candle visualization smoothes out the signal line to provide more informative insights into momentum and trends. This allows earlier entries and exits by observing the indicator values transformed by the HA.
Various visualization options are available to adjust the indicator to the user’s preference: Aside from HA, a classic line, or a hybrid of both.
A special feature of Quantra’s indicators is that they are probabilistically built - therefore they work well as confluence and can easily be stacked to increase signal accuracy.
To add to Quantra's indicators’ utility we have added the option to change the price bars colors based on different signals:
Choose Mode for Coloring
Trend Following (Indicator above mid line counts as uptrend, below is downtrend)
Extremes (Everything beyond the SD bands is highlighted to signal mean reversion)
Candles (Color of HA candles as barcolor)
Reversions (Only for HA) (Reversion Signals via the triangles if HA candles change trend while beyond the SD bands, high probability entries/exits)
The 𝓡𝓢-𝓡𝓢𝓘 is finely tuned to detect divergences.
Primarily utilized for trend following, the 𝓡𝓢-𝓡𝓢𝓘 also demonstrates effectiveness in identifying reversions, intensity of movements and the navigation of range-bound markets.
Allows for easy identification of slowdowns in momentum and thus negative rate of change.
Methodology
The 𝓡𝓢-𝓡𝓢𝓘 takes the classical RSI using a specified lookback length and computes the slope of a linear regression line applied to the RSI values. This slope is used to adjust the RSI.
This sloped RSI can be further smoothed using various Moving Averages with customizable lengths.
For a more nuanced view of market trends, the 𝓡𝓢-𝓡𝓢𝓘 applies a specialized Heikin Ashi method. This transformation modifies the Sloped RSI values in order to weigh and reflect the average price, offering a smoother representation compared to traditional candlestick patterns.
The 𝓡𝓢-𝓡𝓢𝓘 calculates upper and lower bounds based on a specified standard deviation multiplier and adjustable lookback period, providing a dynamic framework to identify extrema and thus overbought and oversold conditions.
Particularly in the Heikin Ashi mode, the 𝓡𝓢-𝓡𝓢𝓘 can display reversion signals. These are plotted as shapes on the chart, indicating high probability reversal points in the market trend.
Wave Pendulum Trend [QuantraSystems]Wave Pendulum Trend
Introduction
The Wave Pendulum Trend (𝓟𝓮𝓷𝓭𝓾𝓵𝓾𝓶 𝓣𝓻𝓮𝓷𝓭) extrapolates market trends using physical principles derived from waves and pendulums. This indicator is a bespoke build, and its performance and behavior cannot be compared to existing indicators.
It is designed for trend following but is also effective for identifying mean reversions, momentum strength, and shows range-bound market periods within the dynamic bands.
In order to ascertain a smooth yet rapid trend direction of the market, the 𝓟𝓮𝓷𝓭𝓾𝓵𝓾𝓶 𝓣𝓻𝓮𝓷𝓭 combines several factors. A bespoke set of functions captures the momentum of price movements and dynamically weighs it over time. The indicator then extrapolates acceleration from the change in delta of price movements.
Legend
With bar coloring enabled, the price section mirrors current trend conditions. Please keep this feature disabled if you intend to use multiple indicators to avoid confusion.
The 𝓟𝓮𝓷𝓭𝓾𝓵𝓾𝓶 𝓣𝓻𝓮𝓷𝓭 presents extensive market insights. The purple and green bands around the oscillator signal the selected standard deviation (default σ = 2), for the trader to calculate how common the trending movements are in relation to the selected asset’s history.
The inner, dynamic thresholds, indicated by the blue “Range-bound market” label in the graphic above, border the area that signals a ranging market if both 𝓐𝓬𝓬𝓮𝓵𝓮𝓻𝓪𝓽𝓲𝓸𝓷 and 𝓜𝓸𝓶𝓮𝓷𝓽𝓾𝓶 signals remain inside. If either line exceeds these thresholds, care is advised as a shift in market behavior is underway.
“Trend strength” in the graphic provides a good estimate for the trending movements strength.
If the signal lines exceed the set standard deviation in non-classic mode, a reversal is very likely.
Case Study
As shown in the above case study we see two profitable swing trades on the 4H chart of Ethereum. Please note the display variant here is set to “Heikin-Ashi”.
We always recommend using a multitude of indicators to attain multiple signals on the likelihood of opening the correct position. However, this standalone scenario serves as an example on how the 𝓟𝓮𝓷𝓭𝓾𝓵𝓾𝓶 𝓣𝓻𝓮𝓷𝓭 added two profitable swing trades.
The first short trade was opened after the 𝓐𝓬𝓬𝓮𝓵𝓮𝓻𝓪𝓽𝓲𝓸𝓷 and 𝓜𝓸𝓶𝓮𝓷𝓽𝓾𝓶 reversed after crossing the threshold of standard deviation. This trade offered a late entry only, these two factors were followed late by the third signal in this case – the trend reversal. Such a trade would require additional indicators to signal at the same time, so the trader can get more confirmations. The trade was closed after 6D with an 8% gain on a 1x short position.
The second trade is a long position that enters in the same manner. The trader takes the reversal beyond the select standard deviation as a likely entry. After 7D a triple confirmation was received, as indicated by the triangle, that a reversal or at least a plateau is extremely likely. The trade was closed after 7D with a 17.23% gain on a 1x long position.
Recommended Settings
Trend Following / Investing (1D chart)
Please use the default settings!
Swing Trading (4H chart)
Wave MA - Type: TEMA
Wave MA – Length: 30
Display Variant: Heikin-Ashi
Bar Coloring: Off
Choose Mode for Coloring: Signal
Notes
Quantra Standard Value Contents:
The Heikin-Ashi (HA) candle visualization smoothes out the signal line to provide more informative insights into momentum and trends. This allows earlier entries and exits by observing the indicator values transformed by the HA.
Various visualization options are available to adjust the indicator to the user’s preference: Aside from HA, a classic line, or a hybrid of both.
A special feature of Quantra’s indicators is that they are probabilistically built - therefore they work well as confluence and can easily be stacked to increase signal accuracy.
To add to Quantra's indicators’ utility we have added the option to change the price bars colors based on different signals:
Settings: TEMA and DEMA length settings should be longer compared to other Moving Averages (MAs). Due to its complex calculations, the indicator requires a larger amount of historical data for accurate computation.
Sensitivity to Divergences: The Wave Pendulum Trend is particularly sensitive to divergences, making it a useful tool in spotting potential trend reversals or continuations.
Trend Following and Reversions: While it is primarily used for trend following, it also excels in identifying market reversions.
Momentum and Acceleration: The interaction between momentum and acceleration is a key feature of this indicator.
Visualization: The indicator offers various visualization options, including bar coloring based on HA Candles and extremes and trends. It also introduces a novel approach to visualizing the oscillator in the "Classic" mode and provides an adjustable Standard Deviation (SD) measure for reversal signals in non-classic modes.
Choose Mode for Coloring
Trend Following (Indicator above mid line counts as uptrend, below is downtrend)
Extremes (Everything beyond the SD bands is highlighted to signal mean reversion)
Candles (Color of HA candles as barcolor)
Reversions (Only for HA) (Reversion Signals via the triangles if HA candles change trend while beyond the SD bands, high probability entries/exits)
Methodology
The methodology behind the Wave Pendulum Trend is inspired by wave and pendulum theories to extrapolate market moves. By calculating the momentum and its acceleration from price data, it provides a nuanced view of the market trend.
Traders should observe the color coding, which reflects the interplay between momentum, acceleration, and set thresholds for acceleration. The Signal Mode is particularly useful for quickly identifying trend, momentum, and acceleration exhaustions.
Additionally, the indicator can help filter out ranges with insufficient momentum acceleration. Traders are encouraged to experiment with this mode and adjust the threshold settings to suit their strategies.
RSI Volatility Bands [QuantraSystems]RSI Volatility Bands
Introduction
The RSI Volatility Bands indicator introduces a unique approach to market analysis by combining the traditional Relative Strength Index (RSI) with dynamic, volatility adjusted deviation bands. It is designed to provide a highly customizable method of trend analysis, enabling investors to analyze potential entry and exit points in a new and profound way.
The deviation bands are calculated and drawn in a manner which allows investors to view them as areas of dynamic support and resistance.
Legend
Upper and Lower Bands - A dynamic plot of the volatility-adjusted range around the current price.
Signals - Generated when the RSI volatility bands indicate a trend shift.
Case Study
The chart highlights the occurrence of false signals, emphasizing the need for caution when the bands are contracted and market volatility is low.
Juxtaposing this, during volatile market phases as shown, the indicator can effectively adapt to strong trends. This keeps an investor in a position even through a minor drawdown in order to exploit the entire price movement.
Recommended Settings
The RSI Volatility Bands are highly customisable and can be adapted to many assets with diverse behaviors.
The calibrations used in the above screenshots are as follows:
Source = close
RSI Length = 8
RSI Smoothing MA = DEMA
Bandwidth Type = DEMA
Bandwidth Length = 24
Bandwidth Smooth = 25
Methodology
The indicator first calculates the RSI of the price data, and applies a custom moving average.
The deviation bands are then calculated based upon the absolute difference between the RSI and its moving average - providing a unique volatility insight.
The deviation bands are then adjusted with another smoothing function, providing clear visuals of the RSI’s trend within a volatility-adjusted context.
rsiVal = ta.rsi(close, rsiLength)
rsiEma = ma(rsiMA, rsiVal, bandLength)
bandwidth = ma(bandMA, math.abs(rsiVal - rsiEma), bandLength)
upperBand = ma(bandMA, rsiEma + bandwidth, smooth)
lowerBand = ma(bandMA, rsiEma - bandwidth, smooth)
long = upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50)
short= not (upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50))
By dynamically adjusting to market conditions, the RSI trend bands offer a unique perspective on market trends, and reversal zones.
EPS GridIntroduction:
This simple indicator offers insights into the relationship between stock prices and earnings, aiding in the assessment of valuation dynamics during different periods.
Understanding Price-to-Earnings (P/E) Ratio:
The commonly used Price to Earnings (P/E) ratio, calculated as Current Price divided by Earnings Per Share (EPS) over the trailing 12 months (TTM), serves as a fundamental metric. Here, we use this formula to estimate a stock's price. For instance, multiplying EPS by 10 provides an approximation of the stock price with a P/E ratio of 10.
The Grid Concept:
Utilizing this principle, a visual grid is constructed to illustrate how stock prices correlate with earnings. This grid facilitates the identification of both potential bargains and overvalued stocks.
How to Utilize:
This indicator is pre-configured with earnings multiples of 10, 15, 20, and 25. Simply add it to your chart and observe whether earnings demonstrate consistent growth. If prices lag behind earnings, a potential catch-up phase may ensue in the future.
Happy Investing!
Embark on your investment journey armed with this indicator, and may it guide you towards informed decisions and successful ventures.
Smart DCA StrategyINSPIRATION
While Dollar Cost Averaging (DCA) is a popular and stress-free investment approach, I noticed an opportunity for enhancement. Standard DCA involves buying consistently, regardless of market conditions, which can sometimes mean missing out on optimal investment opportunities. This led me to develop the Smart DCA Strategy – a 'set and forget' method like traditional DCA, but with an intelligent twist to boost its effectiveness.
The goal was to build something more profitable than a standard DCA strategy so it was equally important that this indicator could backtest its own results in an A/B test manner against the regular DCA strategy.
WHY IS IT SMART?
The key to this strategy is its dynamic approach: buying aggressively when the market shows signs of being oversold, and sitting on the sidelines when it's not. This approach aims to optimize entry points, enhancing the potential for better returns while maintaining the simplicity and low stress of DCA.
WHAT THIS STRATEGY IS, AND IS NOT
This is an investment style strategy. It is designed to improve upon the common standard DCA investment strategy. It is therefore NOT a day trading strategy. Feel free to experiment with various timeframes, but it was designed to be used on a daily timeframe and that's how I recommend it to be used.
You may also go months without any buy signals during bull markets, but remember that is exactly the point of the strategy - to keep your buying power on the sidelines until the markets have significantly pulled back. You need to be patient and trust in the historical backtesting you have performed.
HOW IT WORKS
The Smart DCA Strategy leverages a creative approach to using Moving Averages to identify the most opportune moments to buy. A trigger occurs when a daily candle, in its entirety including the high wick, closes below the threshold line or box plotted on the chart. The indicator is designed to facilitate both backtesting and live trading.
HOW TO USE
Settings:
The input parameters for tuning have been intentionally simplified in an effort to prevent users falling into the overfitting trap.
The main control is the Buying strictness scale setting. Setting this to a lower value will provide more buying days (less strict) while higher values mean less buying days (more strict). In my testing I've found level 9 to provide good all round results.
Validation days is a setting to prevent triggering entries until the asset has spent a given number of days (candles) in the overbought state. Increasing this makes entries stricter. I've found 0 to give the best results across most assets.
In the backtest settings you can also configure how much to buy for each day an entry triggers. Blind buy size is the amount you would buy every day in a standard DCA strategy. Smart buy size is the amount you would buy each day a Smart DCA entry is triggered.
You can also experiment with backtesting your strategy over different historical datasets by using the Start date and End date settings. The results table will not calculate for any trades outside what you've set in the date range settings.
Backtesting:
When backtesting you should use the results table on the top right to tune and optimise the results of your strategy. As with all backtests, be careful to avoid overfitting the parameters. It's better to have a setup which works well across many currencies and historical periods than a setup which is excellent on one dataset but bad on most others. This gives a much higher probability that it will be effective when you move to live trading.
The results table provides a clear visual representation as to which strategy, standard or smart, is more profitable for the given dataset. You will notice the columns are dynamically coloured red and green. Their colour changes based on which strategy is more profitable in the A/B style backtest - green wins, red loses. The key metrics to focus on are GOA (Gain on Account) and Avg Cost .
Live Trading:
After you've finished backtesting you can proceed with configuring your alerts for live trading.
But first, you need to estimate the amount you should buy on each Smart DCA entry. We can use the Total invested row in the results table to calculate this. Assuming we're looking to trade on BITSTAMP:BTCUSD
Decide how much USD you would spend each day to buy BTC if you were using a standard DCA strategy. Lets say that is $5 per day
Enter that USD amount in the Blind buy size settings box
Check the Blind Buy column in the results table. If we set the backtest date range to the last 10 years, we would expect the amount spent on blind buys over 10 years to be $18,250 given $5 each day
Next we need to tweak the value of the Smart buy size parameter in setting to get it as close as we can to the Total Invested amount for Blind Buy
By following this approach it means we will invest roughly the same amount into our Smart DCA strategy as we would have into a standard DCA strategy over any given time period.
After you have calculated the Smart buy size , you can go ahead and set up alerts on Smart DCA buy triggers.
BOT AUTOMATION
In an effort to maintain the 'set and forget' stress-free benefits of a standard DCA strategy, I have set my personal Smart DCA Strategy up to be automated. The bot runs on AWS and I have a fully functional project for the bot on my GitHub account. Just reach out if you would like me to point you towards it. You can also hook this into any other 3rd party trade automation system of your choice using the pre-configured alerts within the indicator.
PLANNED FUTURE DEVELOPMENTS
Currently this is purely an accumulation strategy. It does not have any sell signals right now but I have ideas on how I will build upon it to incorporate an algorithm for selling. The strategy should gradually offload profits in bull markets which generates more USD which gives more buying power to rinse and repeat the same process in the next cycle only with a bigger starting capital. Watch this space!
MARKETS
Crypto:
This strategy has been specifically built to work on the crypto markets. It has been developed, backtested and tuned against crypto markets and I personally only run it on crypto markets to accumulate more of the coins I believe in for the long term. In the section below I will provide some backtest results from some of the top crypto assets.
Stocks:
I've found it is generally more profitable than a standard DCA strategy on the majority of stocks, however the results proved to be a lot more impressive on crypto. This is mainly due to the volatility and cycles found in crypto markets. The strategy makes its profits from capitalising on pullbacks in price. Good stocks on the other hand tend to move up and to the right with less significant pullbacks, therefore giving this strategy less opportunity to flourish.
Forex:
As this is an accumulation style investment strategy, I do not recommend that you use it to trade Forex.
STRATEGY IN ACTION
Here you see the indicator running on the BITSTAMP:BTCUSD pair. You can read the indicator as follows:
Vertical green bands on historical candles represents where buy signals triggered in the past
Table on the top right represents the results of the A/B backtest against a standard DCA strategy
Green Smart Buy column shows that Smart DCA was more profitable than standard DCA on this backtest. That is shown by the percentage GOA (Gain on Account) and the Avg Cost
Smart Buy Zone label marks the threshold which the entire candle must be below to trigger a buy signal (line can be changed to a box under plotting settings)
Green color of Smart Buy Zone label represents that the open candle is still valid for a buy signal. A signal will only be generated if the candle closes while this label is still green
Below is the same BITSTAMP:BTCUSD chart a couple of days later. Notice how the threshold has been broken and the Smart Buy Zone label has turned from green to red. No buy signal can be triggered for this day - even if the candle retraced and closed below the threshold before daily candle close.
Notice how the green vertical bands tend to be present after significant pullbacks in price. This is the reason the strategy works! Below is the same BITSTAMP:BTCUSD chart, but this time zoomed out to present a clearer picture of the times it would invest vs times it would sit out of the market. You will notice it invests heavily in bear markets and significant pullbacks, and does not buy anything during bull markets.
Finally, to visually demonstrate the indicator on an asset other than BTC, here is an example on CRYPTO:ETHUSD . In this case the current daily high has not touched the threshold so it is still possible for this to be a valid buy trigger on daily candle close. The vertical green band will not print until the buy trigger is confirmed.
BACKTEST RESULTS
Now for some backtest results to demonstrate the improved performance over a standard DCA strategy using all non-stablecoin assets in the top 30 cryptos by marketcap.
I've used the TradingView ticker (exchange name denoted as CRYPTO in the symbol search) for every symbol tested with the exception of BTCUSD because there was some dodgy data at the beginning of the TradingView BTCUSD chart which overinflated the effectiveness of the Smart DCA strategy on that ticker. For BTCUSD I've used the BITSTAMP exchange data. The symbol links below will take you to the correct chart and exchange used for the test.
I'm using the GOA (Gain on Account) values to present how each strategy performed.
The value on the left side is the standard DCA result and the right is the Smart DCA result.
✅ means Smart DCA strategy outperformed the standard DCA strategy
❌ means standard DCA strategy outperformed the Smart DCA strategy
To avoid overfitting, and to prove that this strategy does not suffer from overfitting, I've used the exact same input parameters for every symbol tested below. The settings used in these backtests are:
Buying strictness scale: 9
Validation days: 0
You can absolutely tweak the values per symbol to further improve the results of each, however I think using identical settings on every pair tested demonstrates a higher likelihood that the results will be similar in the live markets.
I'm presenting results for two time periods:
First price data available for trading pair -> closing candle on Friday 26th Jan 2024 (ALL TIME)
Opening candle on Sunday 1st Jan 2023 -> closing candle on Friday 26th Jan 2024 (JAN 2023 -> JAN 2024)
ALL TIME:
BITSTAMP:BTCUSD 80,884% / 133,582% ✅
CRYPTO:ETHUSD 17,231% / 36,146% ✅
CRYPTO:BNBUSD 5,314% / 2,702% ❌
CRYPTO:SOLUSD 1,745% / 1,171% ❌
CRYPTO:XRPUSD 2,585% / 4,544% ✅
CRYPTO:ADAUSD 338% / 353% ✅
CRYPTO:AVAXUSD 130% / 160% ✅
CRYPTO:DOGEUSD 13,690% / 16,432% ✅
CRYPTO:TRXUSD 414% / 466% ✅
CRYPTO:DOTUSD -16% / -7% ✅
CRYPTO:LINKUSD 1,161% / 2,164% ✅
CRYPTO:TONUSD 25% / 47% ✅
CRYPTO:MATICUSD 1,769% / 1,587% ❌
CRYPTO:ICPUSD 70% / 50% ❌
CRYPTO:SHIBUSD -20% / -19% ✅
CRYPTO:LTCUSD 486% / 718% ✅
CRYPTO:BCHUSD -4% / 3% ✅
CRYPTO:LEOUSD 102% / 151% ✅
CRYPTO:ATOMUSD 46% / 91% ✅
CRYPTO:UNIUSD -16% / 1% ✅
CRYPTO:ETCUSD 283% / 414% ✅
CRYPTO:OKBUSD 1,286% / 1,935% ✅
CRYPTO:XLMUSD 1,471% / 1,592% ✅
CRYPTO:INJUSD 830% / 1,035% ✅
CRYPTO:OPUSD 138% / 195% ✅
CRYPTO:NEARUSD 23% / 44% ✅
Backtest result analysis:
Assuming we have an initial investment amount of $10,000 spread evenly across each asset since the creation of each asset, it would have provided the following results.
Standard DCA Strategy results:
Average percent return: 4,998.65%
Profit: $499,865
Closing balance: $509,865
Smart DCA Strategy results:
Average percent return: 7,906.03%
Profit: $790,603
Closing balance: $800,603
JAN 2023 -> JAN 2024:
BITSTAMP:BTCUSD 47% / 66% ✅
CRYPTO:ETHUSD 26% / 33% ✅
CRYPTO:BNBUSD 15% / 17% ✅
CRYPTO:SOLUSD 272% / 394% ✅
CRYPTO:XRPUSD 7% / 12% ✅
CRYPTO:ADAUSD 43% / 59% ✅
CRYPTO:AVAXUSD 116% / 151% ✅
CRYPTO:DOGEUSD 8% / 14% ✅
CRYPTO:TRXUSD 48% / 65% ✅
CRYPTO:DOTUSD 24% / 35% ✅
CRYPTO:LINKUSD 83% / 124% ✅
CRYPTO:TONUSD 7% / 21% ✅
CRYPTO:MATICUSD -3% / 7% ✅
CRYPTO:ICPUSD 161% / 196% ✅
CRYPTO:SHIBUSD 1% / 8% ✅
CRYPTO:LTCUSD -15% / -7% ✅
CRYPTO:BCHUSD 47% / 68% ✅
CRYPTO:LEOUSD 9% / 11% ✅
CRYPTO:ATOMUSD 1% / 15% ✅
CRYPTO:UNIUSD 9% / 23% ✅
CRYPTO:ETCUSD 27% / 40% ✅
CRYPTO:OKBUSD 21% / 30% ✅
CRYPTO:XLMUSD 11% / 19% ✅
CRYPTO:INJUSD 477% / 446% ❌
CRYPTO:OPUSD 77% / 91% ✅
CRYPTO:NEARUSD 78% / 95% ✅
Backtest result analysis:
Assuming we have an initial investment amount of $10,000 spread evenly across each asset for the duration of 2023, it would have provided the following results.
Standard DCA Strategy results:
Average percent return: 61.42%
Profit: $6,142
Closing balance: $16,142
Smart DCA Strategy results:
Average percent return: 78.19%
Profit: $7,819
Closing balance: $17,819
Simple Neural Network Transformed RSI [QuantraSystems]Simple Neural Network Transformed RSI
Introduction
The Simple Neural Network Transformed RSI (ɴɴᴛ ʀsɪ) stands out as a formidable tool for traders who specialize in lower timeframe trading.
It is an innovative enhancement of the traditional RSI readings with simple neural network smoothing techniques.
This unique blend results in fairly accurate signals, tailored for swift market movements. The ɴɴᴛ ʀsɪ is particularly resistant to the usual market noise found in lower timeframes, ensuring a clearer view of short-term trends.
Furthermore, its diverse range of visualization options adds versatility, making it a valuable tool for traders seeking to capitalize on short-duration market dynamics.
Legend
In the Image you can see the BTCUSD 1D Chart with the ɴɴᴛ ʀsɪ in Trend Following Mode to display the current trend. This is visualized with the barcoloring.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 2), which can indicate extremely rare situations which can lead to either a softening momentum in the trend or even a mean reversion situation.
Here you can also see the original Indicator line and the Heikin Ashi transformed Indicator bars - more on that now.
Notes
Quantra Standard Value Contents:
To draw out all the information from the indicator calculation we have added a Heikin-Ashi (HA) Candle Visualization.
This HA transformation smoothens out the indicator values and gives a more informative look into Momentum and Trend of the Indicator itself.
This allows early entries and exits by observing the HA transformed Indicator values.
To diversify, different visualization options are available, either a classic line, HA transformed or Hybrid, which contains both of the previous.
To make Quantra's Indicators as useful and versatile as possible we have created options
to change the barcoloring and thus the derived signal from the indicator based on different modes.
Option to choose different Modes:
Trend Following (Indicator above mid line counts as uptrend, below is downtrend)
Extremities (Everything going beyond the Deviation Bands in a Mean Reversion manner is highlighted)
Candles (Color of HA candles as barcolor)
Reversion (HA ONLY) (Reversion Signals via the triangles if HA candles change state outside of the Deviation Bands)
- Reversion Signals are indicated by the triangles in the Heikin-Ashi or Hybrid visualization when the HA Candles revert
from downwards to upwards or the other way around OUTSIDE of the SD Bands.
Depending on the Indicator they signal OB/OS areas and can either work as high probability entries and exits for Mean Reversion trades or
indicate Momentum slow downs and potential ranges.
Please use another indicator to confirm this.
Case Study
To effectively utilize the NNT-RSI, traders should know their style and familiarize themselves with the available options.
As stated above, you have multiple modes available that you can combine as you need and see fit.
In the given example mostly only the mode was used in an isolated fashion.
Trend Following:
Purely relied on State Change - Midline crossover
Could be combined with Momentum or Reversion analysis for better entries/exits.
Extremities:
Ideal entry/exit is in the accordingly colored OS/OB Area, the Reversion signaled the latest possible entry/exit.
HA Candles:
Specifically applicable for strong trends. Powerful and fast tool.
Can whip if used as sole condition.
Reversions:
Shows the single entry and exit bars which have a positive expected value outcome.
Can also be used as confirmation or as last signal.
Please note that we always advise to find more confluence by additional indicators.
Traders are encouraged to test and determine the most suitable settings for their specific trading strategies and timeframes.
In the showcased trades the default settings were used.
Methodology
The Simple Neural Network Transformed RSI uses a simple neural network logic to process RSI values, smoothing them for more accurate trend analysis.
This is achieved through a linear combination of RSI values over a specified input length, weighted evenly to produce a neural network output.
// Simple neural network logic (linear combination with weighted aggregation)
var float inputs = array.new_float(nnLength, na)
for i = 0 to nnLength - 1
array.set(inputs, i, rsi1 )
nnOutput = 0.0
for i = 0 to nnLength - 1
nnOutput := nnOutput + array.get(inputs, i) * (1 / nnLength)
nnOutput
This output is then compared against a standard or dynamic mean line to generate trend following signals.
Mean = ta.sma(nnOutput, sdLook)
cross = useMean? 50 : Mean
The indicator also incorporates Heikin Ashi candlestick calculations to provide additional insights into market dynamics, such as trend strength and potential reversals.
// Calculate Heikin Ashi representation
ha = ha(
na(nnOutput ) ? nnOutput : nnOutput ,
math.max(nnOutput, nnOutput ),
math.min(nnOutput, nnOutput ),
nnOutput)
Standard deviation bands are used to create dynamic overbought and oversold zones, further enhancing the tool's analytical capabilities.
// Calculate Dynamic OB/OS Zones
stdv_bands(_src, _length, _mult) =>
float basis = ta.sma(_src, _length)
float dev = _mult * ta.stdev(_src, _length)
= stdv_bands(nnOutput, sdLook,sdMult/2)
= stdv_bands(nnOutput, sdLook, sdMult)
The Standard Deviation bands take defined parameters from the user, in this case sigma of ideally between 2 to 3,
to help the indicator detect extremely improbable conditions and thus take an inversely probable signal from it to forward to the user.
The parameter settings and also the visualizations allow for ample customizations by the trader.
For questions or recommendations, please feel free to seek contact in the comments.
Rolling VWAP [QuantraSystems]Rolling VWAP
Introduction
The Rolling VWAP (R͜͡oll-VWAP) indicator modernizes the traditional VWAP by recalculating continuously on a rolling window, making it adept at pinpointing market trends and breakout points.
Its dual functionality includes both the dynamic rolling VWAP and a customizable anchored VWAP, enhanced by color-coded visual cues, thereby offering traders valuable flexibility and insight for their market analysis.
Legend
In the Image you can see the BTCUSD 1D Chart with the R͜͡oll-VWAP overlay.
You can see the individually activatable Standard Deviation (SD) Bands and the main VWAP Line.
It also features a Trend Signal which is deactivated by default and can be enabled if required.
Furthermore you can find the coloring of the VWAP line to represent the Trend.
In this case the trend itself is defined as:
Close being greater than the VWAP line -> Uptrend
Close below the VWAP line -> Downtrend
Notes
The R͜͡oll-VWAP can be used in a variety of ways.
Volatility adjusted expected range
This aims to identify in which range the asset is likely to move - according to the historical values the SD Bands are calculated and thus their according probabilities displayed.
Trend analysis
Trending above or below the VWAP shows up or down trends accordingly.
S/R Levels
Based on the probability distribution the 2. SD often works as a Resistance level and either mid line or 1. SD lines can act as S/R levels
Unsustainable levels
Based on the probability distributions a SD level of beyond 2.5, especially 3 and higher is hit very seldom and highly unsustainable.
This can either mean a mean reversion state or a momentum slowdown is necessary to get back to a sustainable level.
Please note that we always advise to find more confluence by additional indicators.
Traders are encouraged to test and determine the most suitable settings for their specific trading strategies and timeframes.
Methodology
The R͜͡oll-VWAP is based on the inbuilt TV VWAP.
It expands upon the limitations of having an anchored timeframe and thus a limited data set that is being reset constantly.
Instead we have integrated a rolling nature that continuously calculates the VWAP over a customizable lookback.
To also keep the base utility it is possible to use the anchored timeframes as well.
Furthermore the visualization has been improved and we added the coloring of the main VWAP line according to the Trend as stated above.
The applicable Trend signals are also part of that.
The parameter settings and also the visualizations allow for ample customizations by the trader.
For questions or recommendations, please feel free to seek contact in the comments.
Triple Confirmation Kernel Regression Base [QuantraSystems]Kernel Regression Oscillator - BASE
Introduction
The Kernel Regression Oscillator (ᏦᏒᎧ) represents an advanced tool for traders looking to capitalize on market trends.
This Indicator is valuable in identifying and confirming trend directions, as well as probabilistic and dynamic oversold and overbought zones.
It achieves this through a unique composite approach using three distinct Kernel Regressions combined in an Oscillator. The additional Chart Overlay Indicator adds confidence to the signal.
This methodology helps the trader to significantly reduce false signals and offers a more reliable indication of market movements than more widely used indicators can.
Legend
The upper section is the Overlay. It features the Signal Wave to display the current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 3), which can indicate extremely rare situations which can lead to either a softening momentum in the trend or even a mean reversion situation.
The lower one is the Base Chart - This Indicator.
It features the Kernel Regression Oscillator to display a composite of three distinct regressions, also displaying current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 2), which can indicate extremely rare situations.
Case Study
To effectively utilize the ᏦᏒᎧ, traders should use both the additional Overlay and the Base
Chart at the same time. Then focus on capturing the confluence in signals, for example:
If the 𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮 on the Overlay and the ᏦᏒᎧ on the Base Chart both reside near the extreme of an Oversold zone the probability is higher than normal that momentum in trend may soften or the token may even experience a reversion soon.
If a bar is characterized by an Oversold Shading in both the Overlay and the Base Chart, then the probability is very high to experience a reversion soon.
In this case the trader may want to look for appropriate entries into a long position, as displayed here.
If a bar is characterized by an Overbought Shading in either Overlay or Base Chart, then the probability is high for momentum weakening or a mean reversion.
In this case the trade may have taken profit and closed his long position, as displayed here.
Please note that we always advise to find more confluence by additional indicators.
Recommended Settings
Swing Trading (1D chart)
Overlay
Bandwith: 45
Width: 2
SD Lookback: 150
SD Multiplier: 2
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Fast-paced, Scalping (4min chart)
Overlay
Bandwith: 75
Width: 2
SD Lookback: 150
SD Multiplier: 3
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Notes
The Kernel Regression Oscillator on the Base Chart is also sensitive to divergences if that is something you are keen on using.
For maximum confluence, it is recommended to use the indicator both as a chart overlay and in its Base Chart.
Please pay attention to shaded areas with Standard Deviation settings of 2 or 3 at their outer borders, and consider action only with high confidence when both parts of the indicator align on the same signal.
This tool shows its best performance on timeframes lower than 4 hours.
Traders are encouraged to test and determine the most suitable settings for their specific trading strategies and timeframes.
The trend following functionality is indicated through the "𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮" Line, with optional "Up" and "Down" arrows to denote trend directions only (toggle “Show Trend Signals”).
Methodology
The Kernel Regression Oscillator takes three distinct kernel regression functions,
used at similar weight, in order to calculate a balanced and smooth composite of the regressions. Part of it are:
The Epanechnikov Kernel Regression: Known for its efficiency in smoothing data by assigning less weight to data points further away from the target point than closer data points, effectively reducing variance.
The Wave Kernel Regression: Similarly assigning weight to the data points based on distance, it captures repetitive and thus wave-like patterns within the data to smoothen out and reduce the effect of underlying cyclical trends.
The Logistic Kernel Regression: This uses the logistic function in order to assign weights by probability distribution on the distance between data points and target points. It thus avoids both bias and variance to a certain level.
kernel(source, bandwidth, kernel_type) =>
switch kernel_type
"Epanechnikov" => math.abs(source) <= 1 ? 0.75 * (1 - math.pow(source, 2)) : 0.0
"Logistic" => 1/math.exp(source + 2 + math.exp(-source))
"Wave" => math.abs(source) <= 1 ? (1 - math.abs(source)) * math.cos(math.pi * source) : 0.
kernelRegression(src, bandwidth, kernel_type) =>
sumWeightedY = 0.
sumKernels = 0.
for i = 0 to bandwidth - 1
base = i*i/math.pow(bandwidth, 2)
kernel = kernel(base, 1, kernel_type)
sumWeightedY += kernel * src
sumKernels += kernel
(src - sumWeightedY/sumKernels)/src
// Triple Confirmations
Ep = kernelRegression(source, bandwidth, 'Epanechnikov' )
Lo = kernelRegression(source, bandwidth, 'Logistic' )
Wa = kernelRegression(source, bandwidth, 'Wave' )
By combining these regressions in an unbiased average, we follow our principle of achieving confluence for a signal or a decision, by stacking several edges to increase the probability that we are correct.
// Average
AV = math.avg(Ep, Lo, Wa)
The Standard Deviation bands take defined parameters from the user, in this case sigma of ideally between 2 to 3,
to help the indicator detect extremely improbable conditions and thus take an inversely probable signal from it to forward to the user.
The parameter settings and also the visualizations allow for ample customizations by the trader. The indicator comes with default and recommended settings.
For questions or recommendations, please feel free to seek contact in the comments.
White NoiseThe "White Noise" indicator is designed to visualize the dispersion of price movements around a moving average, providing insights into market noise and potential trend changes. It highlights periods of increased volatility or noise compared to the underlying trend.
Code Explanation:
Inputs:
mlen: Input for the length of the noise calculation.
hlen: Input for the length of the Hull moving average.
col_up: Input for the color of the up movement.
col_dn: Input for the color of the down movement.
Calculations:
ma: Calculate the simple moving average of the high, low, and close prices (hlc3) over the specified mlen period.
dist: Calculate the percentage distance between the hlc3 and the moving average ma, then scale it by 850. This quantifies the deviation from the moving average as a value.
sm: Smooth the calculated dist values using a weighted moving average (WMA) twice, with different weights, and subtract one from the other. This provides a smoothed representation of the dispersion.
Coloring:
col_wn: Determine the color of the bars based on whether dist is positive or negative and whether it's greater or less than the smoothed sm value. This creates color-coded columns indicating upward or downward movements with varying opacity.
col_switch: Define the color for the current trend state. It switches color when the smoothed sm crosses above or below its previous value, indicating potential trend changes.
col_switch2: Define the color for the horizontal line that separates the two trend states. It switches color based on the same crossover and crossunder conditions as col_switch.
Plots:
plot(dist): Plot the dispersion values as columns with color defined by col_wn.
plot(sm): Plot the smoothed dispersion line with a white color and thicker linewidth.
plot(sm ): Plot the previous smoothed dispersion value with a lighter white color to create a visual distinction.
Usage:
This indicator can help traders identify periods of increased market noise, visualize potential trend reversals, and assess the strength of price movements around the moving average. The colored columns and smoothed line offer insights into the ebb and flow of market sentiment, aiding in decision-making.
ps. This can be used as a long-term TPI component if you dabble in Modern Portfolio Theory (MPT)
Recommended for timeframes on the 1D or above:
Shiller PE Ratio (CAPE Ratio) [WhaleCrew]Our Implementation of the famous Shiller PE Ratio (aka C yclically A djusted P rice-to- E arnings Ratio) a long-term valuation indicator for the S&P 500.
Calculation: Share price divided by 10 - year average, inflation - adjusted earnings
The indicator works on the M and 12M timeframe and has a built-in moving average that supports an upper and lower bollinger band.
Bitcoin Miner Sell PressureBitcoin miners are in pain and now (November 2022) selling more than they have in almost 5 years!
Introducing: Bitcoin Miner Sell Pressure.
A free, open-source indicator which tracks on-chain data to highlight when Bitcoin miners are selling more of their reserves than usual.
The indicator tracks the ratio of on-chain miner Bitcoin outflows to miner Bitcoin reserves.
- Higher = more selling than usual
- Lower = less selling than usual
- Red = extraordinary sell pressure
Today , it's red.
What can we see now ?
Miners are not great at treasury management. They tend to sell most when they are losing money (like today). But there have been times when they sold well into high profit, such as into the 2017 $20K top and in early 2021 when Bitcoin breached $40K.
Bitcoin Miner Sell Pressure identifies industry stress, excess and miner capitulation.
Unsurprisingly, there is a high correlation with Bitcoin Production Cost; giving strong confluence to both.
In some instances, BMSP spots capitulation before Hash Ribbons. Such as today!
Balance of Power Heikin Ashi Investing Strategy Balance of Power Heikin Ashi Investing Strategy
This is a swing strategy designed for investment help.
Its made around the Balace of Power indicator, but has been adapted on using the Monthly Heikin Ashi candle from the SPY asset in order to be used with correlation for US Stock/ETF/Index Markets.
The BOP acts as an oscilallator showing the power of a bull trend when its positive and a bearish trend when its in negative. At the same time we can spot reversals, based on the percentiles ( 99/1)
The rules for entry :
For long : The 99 percentile is ascending, and we are either in a positive value (>0), or we crossed the bottom place ( -0.35)
For short : the 99 and 1 percentile are descending, and we are either in a negative value(<0), or we crossed down the top place ( 0.6)
If you have any questions please let me know !
Krugman's Dynamic DCAThis script helps you create a DCA (dollar-cost averaging) strategy for your favorite markets and calculates the DCA value for each bar. This can be used to DCA daily, weekly, bi-weekly, etc.
Configuring the indicator:
- DCA Starting Price : the price you want to begin DCA'ing
- DCA Base Amount : the $ amount you will DCA when price is half of your starting price
- DCA Max Amount : the maximum amount you want to DCA regardless of how low price gets
The DCA scaling works exactly like the formula used to calculated the gain needed to recover from a given % loss. In this case it's calculated from the DCA Starting Price . The idea is to increase the DCA amount linearly with the increased upside potential.
Buffett Indicator: Wilshire 5000 to GDP Ratio [WhaleCrew]Our Implementation of the famous Buffett Indicator a long-term valuation indicator for stocks.
Calculation: Wilshire 5000 Index divided by US GDP (Gross Domestic Product)
CanslimHey folks, I hope you are doing well!
I made a simple script to determine if a company met the CANSLIM criteria. Some of the criteria are not quantifiable so I left those in olive (you have to do research on those). The rest are quantifiable, which include earnings growth, whether it's a laggard, etc.
CANSLIM is a system developed by William O'Neil for selecting growth stocks by using a combination of fundamental and technical analysis techniques. The stocks that meets the criteria are usually outperformers and return really high gains.
C: Current eps have increased sharply from the same quarter in the prior year. Generally, investors using CANSLIM want EPS growth of over 20%, but the higher the better.
A: Annual earnings increases over the last three (some people use 5 but I prefer 3) years. Annual EPS growth should ideally be in excess of 20% over the last three years.
N: New products, management, or positive new events that push the company's stock to new highs. This type of headline news can cause short-term excitement, propelling a surge of optimism within the market and subsequent. This is also known as a catalyst.
S: Scarce supply coupled with a strong appetite for a stock creates excess demand and an environment in which share prices can soar. Generally, company buying back their own shares, reducing market supply and can indicate an expectation of increased demand along with insider confidence in the firm.
L: Laggard stocks are preferred within the same industry. We can use the RSI to determine whether the company is a laggard or not. An RSI reading below 30 suggests that the stock is oversold and could be undervalued—creating a buying opportunity (bullish). An RSI reading of above 70 signifies that a stock could be overbought or overvalued and could be a chance to sell (bearish). Some people prefer to use "Leader" for the L instead of "Laggard" and I personally think it's a good idea to use both. "Leader" suggests that a stock is a leader in its industry or sector
I: Institutions own the stock (mainly recent above-average performing institutions). For example, this could be a recently public company, still supported by a small handful of well-known private equity firms. Be cautious of stocks that are over-owned by institutions as you want to get in before the big money is fully invested.
M: Market average measures the overall price level of a given market, as defined by a specified group of stocks, such as the Dow Jones Industrial Average. CANSLIM stocks tend to be over-performers in bull markets. Determine the market direction for this one.
The colors:
Green = good
Blue = Neutral/Mediocre
Red = bad
Olive = none/requires own research
Automated Bitcoin (BTC) Investment Strategy from Wunderbit Automated Bitcoin (BTC) Investment Strategy from Wunderbit Trading
This strategy is designed for the automated long-term investment in Bitcoin. The BTC investment strategy is primarily suitable for long-term investors who want to increase the percentage of their investments through timely trading long-term transactions. The main feature is the difference from the indicator of long-term investment. Based on their statistics, this figure is 2 times less. That is, if we just bought Bitcoin and held it, we would receive 2 times less than if we applied the BTC Investment strategy.
This strategy uses the intersection of the triple exponential moving average and the least squares moving average. We also control the profit you will make during an uptrend by implementing a trailing stop based on the ATR indicator.
This is a spot market-only strategy and can be used primarily for long-term investors. The strategy is designed to create an automatic version of investing using a webhook.
Automation allows you to safely ignore the state of your portfolio and exclude emotions.
In order to create a cryptocurrency bot for this strategy, you need to:
1. Create alerts and link the URL to the webhook.
2. Connect the TradingView strategy with automated trading service.
Drawdown VisualisationAn indicator that let's you visualize the current drawdown and maximum drawdown from an All-Time High
Volume Extractor By CryptoScriptsThe Volume Extractor is an indicator I've been working on for awhile that involves a Volume Oscillator derived from various volume metrics combined with Bollinger Bands and Overbought/Oversold levels. This indicator is unique because it not only measures the standard deviations whenever the oscillator crosses outside the BBs but it does so at ranges that are most advantageous for the trader to identify KEY buy/sell levels (as shown above). I'll break down each signal below and how to best take advantage of them so you can get the best entries and capture the most profit per trade.
*This indicator works best on the Binance or Bybit exchange for crypto but also works for stocks and forex. It's best used on small-medium timeframes such as the 15m, 1h, 4h, 8hr, or 12h. It tends to give more false signals on the 1D timeframes and higher.
Red Alarm - this signal indicates that the volume oscillator is overbought AND is crossing outside of the bollinger bands . This is a STRONG sell signal but should still be combined with support/resistance levels and confirmed with other indicators.
Red Diamond - this signal indicates that the volume oscillator is crossing outside of the bollinger bands above the 20 level but is not yet overbought. This is a potential sell signal but should still be combined with support/resistance levels and confirmed with other indicators.
Red Shaded Area - this indicates the volume oscillator is overbought. This is a potential sell signal but should still be combined with support/resistance levels and confirmed with other indicators.
Rocket - this signal indicates that the volume oscillator is oversold AND is crossing outside of the bollinger bands . This is a STRONG buy signal but should still be combined with support/resistance levels and confirmed with other indicators.
Green Diamond - this signal indicates that the volume oscillator is crossing outside of the bollinger bands below the 20 level but is not yet oversold. This is a potential buy signal but should still be combined with support/resistance levels and confirmed with other indicators.
Green Shaded Area - this indicates the volume oscillator is oversold. This is a potential buy signal but should still be combined with support/resistance levels and confirmed with other indicators.
Input Options
Show 80 Levels - This checkbox will create a red zone and green zone for the 60-80 levels on the indicator. This is useful if the volume oscillator reaches one of these levels, you can be sure it's going to reverse soon and can have more confidence if it crosses outside of the BBs in addition to that.
VEO Length - This changes the height of the oscillator and will change how your signals flash (more or less often). Use this if you find you're getting too many signals or not enough. I find this is best at 21 but feel free to test out what works for you depending on your timeframe.
Moving Average Volume Source - This is currently set to None but you can change it to Exponential, Hull, or Simple moving average . I found that None works best but feel free to test out the different options.
Moving Average Length - Changing this length will do nothing to your chart if None is selected for the Moving Average Volume Source, therefore you will most likely keep this setting default at 9.
Alerts
I've set alerts on this indicator for each icon (Red Alarm, Red Diamond, Overbought, Rocket, Green Diamond, Oversold). I HIGHLY recommend setting the alerts for Candle Close so that you can be sure the signal is confirmed.
You may notice that the indicator can give multiple signals back-to-back or be overbought/oversold for multiple candles. When this happens, it's best to look at other indicators such as the RSI , MFI Pro, etc to nail the best entry and have confluence with your decision. With that said, having multiple signals back-to-back can also be an indication that the move is close to happening.
This indicator is a strong indicator by itself but works best when paired with my Price Extractor indicator i.e taking a trade when both indicators are displaying overbought/oversold or crossing outside of the bollinger bands . Measuring price action is an extremely important aspect of trading and one I believe should not be overlooked. I hope I made everything as clear as possible and please let me know if I didn't.
PM me to obtain access and please let me know if you have any questions!
Financial Highlights [Fundamentals]█ OVERVIEW
This indicator plot basic key financial data to imitate the presentation format of several popular finance site, make it easier for a quick glance of overall company financial health without switching tabs for every single stocks.
█ Financial Data Available:
- Revenue & PAT (Profit after Tax)
- Net Profit Margin (%)
- Gross Profit Margin (%)
- Earnings Per Share (EPS)
- Dividend
█ Features:
- Toggle between Quarter/Annual Financial Data (Notes: For Dividends, it will always be plotted based on Annual data, at Financial Year ending period)
- Options to plot at either at Quarter/Yearly ending period OR Financial Data published date
█ Limitation
- The accuracy of the data subject to Tradingview's source, but from my observation it's accurate 95% of the time
- Recently published data might not be available immediately. e.g. MYX exchange tends to have 1-3 days lag
- More information on Tradingview's financial data can be read here -> www.tradingview.com
█ Disclaimer
Past performance is not an indicator of future results.
My opinions and research are my own and do not constitute financial advice in any way whatsoever.
Nothing published by me constitutes an investment recommendation, nor should any data or Content published by me be relied upon for any investment/trading activities.
I strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.
Any ideas to further improve this indicator are welcome :)
