Alternative Price [OmegaTools]The Alternative Price script is a sophisticated and flexible indicator designed to redefine how traders visualize and interpret price data. By offering multiple unique charting modes, robust customization options, and advanced features, this tool provides a comprehensive alternative to traditional price charts. It is particularly useful for identifying market trends, detecting patterns, and simplifying complex data into actionable insights.
This script is highly versatile, allowing users to choose from five distinct charting modes: Candles, Line, Channel, Renko, and Bubbles. Each mode serves a unique purpose and presents price information in an innovative way. When using this script, it is strongly recommended to hide the platform’s default price candles or chart data. Doing so will eliminate redundancy and provide a clearer and more focused view of the alternative price visualization.
The Candles mode offers a traditional candlestick charting style but with added flexibility. Users can choose to enable smoothed opens or smoothed closes, which adjust the way the open and close prices are calculated. When smoothed opens are enabled, the opening price is computed as the average of the actual open price and the closing prices of the previous two bars. This creates a more gradual representation of price transitions, particularly useful in markets prone to sudden spikes or irregularities. Similarly, smoothed closes modify the closing price by averaging it with the previous close, the high-low midpoint, and an exponential moving average of the high-low-close mean. This technique filters out noise, making trends and price momentum easier to identify.
In the Line mode, the script displays a simple line chart that connects the smoothed closing prices. This mode is ideal for traders who prefer minimalism or need to focus on the overall trend without the distraction of individual bar details. The Channel mode builds upon this by plotting additional lines representing the highs and lows of each bar. The resulting visualization resembles a price corridor that helps identify support and resistance zones or price compression areas.
The Renko mode introduces a more advanced and noise-filtering method of visualizing price movements. Renko charts, constructed using the ATR (Average True Range) as a baseline, display blocks that represent a specific price range. The script dynamically calculates the size of these blocks based on ATR, with separate thresholds for upward and downward movements. This makes Renko mode particularly effective for identifying sustained trends while ignoring minor price fluctuations. Additionally, the open and close values of Renko blocks can be smoothed to further refine the visualization.
The Bubbles mode represents price activity using circles or bubbles whose size corresponds to relative volume. This mode provides a quick and intuitive way to assess market participation at different price levels. Larger bubbles indicate higher trading volumes, while smaller bubbles highlight periods of lower activity. This visualization is particularly valuable in understanding the relationship between price movements and market liquidity.
The coloring of candles and other chart elements is a core feature of this script. Users can select between two color modes: Normal and Volume. In Normal mode, bullish candles are displayed in the user-defined bullish color, while bearish candles use the bearish color. Neutral elements, such as midpoints or undecided price movements, are shaded with a neutral color. In Volume mode, the candle colors are dynamically adjusted based on trading volume. A gradient color scale is applied, where the intensity of the bullish or bearish colors reflects the volume for that particular bar. This feature allows traders to visually identify periods of heightened activity and associate them with specific price movements.
Engulfing patterns, a popular technical analysis tool, are automatically detected and marked on the chart when the corresponding setting is enabled. The script identifies long engulfing patterns, where the current bar's range completely encompasses the previous bar’s range and indicates a potential bullish reversal. Similarly, short engulfing patterns are identified where the current bar fully engulfs the previous bar in the opposite direction, suggesting a bearish reversal. These patterns are visually highlighted with circular markers to draw the trader’s attention.
Each feature and mode is highly customizable. The colors for bullish, bearish, and neutral movements can be personalized, and the thresholds for patterns or smoothing can be fine-tuned to match specific trading strategies. The script's ability to toggle between various modes makes it adaptable to different market conditions and analysis preferences.
In summary, the Alternative Price script is a comprehensive tool that redefines the way traders view price charts. By offering multiple visualization modes, customizable features, and advanced detection algorithms, it provides a powerful way to uncover market trends, volume relationships, and significant patterns. The recommendation to hide default chart elements ensures that the focus remains on this innovative tool, enhancing its usability and clarity. This script empowers traders to gain deeper insights into market behavior and make informed trading decisions, all while maintaining a clean and visually appealing chart layout.
Keep in mind that some of the modes of this indicator might not reflect the actual closing price of the underlying asset, before opening a trade, check carefully the actual price!
Cari dalam skrip untuk "liquidity"
ICT Killzones with Market BehaviorIndicator Title: ICT Killzones with Market Behavior
Description:
Introducing the ICT Killzones with Market Behavior indicator, a powerful tool designed for traders looking to capitalize on key trading timeframes in the New York session.
Key Features:
Visual Timeframes: This indicator highlights essential trading periods, including Pre-Market, NY Open, NY Lunch, and NY PM sessions. These visual markers help you quickly identify when the market is likely to experience heightened activity and liquidity.
Real-Time Insights: Stay informed with dynamic text displayed at the top of your chart. The indicator updates in real-time, providing actionable insights on what to expect during each session—whether it’s volatility, consolidation, or potential trend continuation.
Custom Color Options: Tailor the color settings for each killzone to fit your personal trading style and enhance the visual clarity of your chart.
User-Friendly Design: Built with simplicity in mind, this indicator integrates seamlessly into TradingView, making it easy for traders of all experience levels to utilize.
How to Use:
Add to Your Chart: Integrate the ICT Killzones with Market Behavior indicator into your TradingView setup.
Monitor Market Conditions: Keep an eye on the highlighted timeframes and the real-time insights displayed at the top. This information can guide your trading strategy effectively.
Adjust Your Approach: Use the insights from the indicator to modify your trading decisions based on the expected market behavior during each session.
Note: This indicator is for educational purposes only and should not be interpreted as financial advice. Always perform your own research and assess risks before making any trading decisions.
Merged Conditional Horizontal Lines with TogglesThe ranges that have blue highs & orange lows have been broken out of & may get re-tested as "support".
Prefer this candle range to be an expansion with neutral wicks.
The ranges that have red highs and green lows have generated interest (inside-bars) in the market, where the first end will get turtle souped and the second will be the draw on liquidity.
Prefer this candle range has long wick(s).
This patch allows you to toggle either range off.
FVG Price & Volume Graph [LuxAlgo]The FVG Price & Volume Graph tool plot recently detected fair value gaps relative to the volume traded within their area during their formation. This allows us to effectively visualize significant fair value gaps caused by high liquidity.
The indicator also returns levels from the fair value gaps areas average with the highest associated volume.
Do note that the indicator can consider the chart's visible range when being computed, which will recalculate the indicator when the chart's visible range changes.
🔶 USAGE
Fair Value Gaps (FVG) are core price action concepts occurring when the disparity between supply and demand is significant. Price has a tendency to come back to those areas and mitigating them, that is filling them.
The provided tools allow for effective visualization of both FVG's area's height as well as the volume originating from their creation, which is defined by the total traded volume located within the FVG during its creation. FVG's with more associated volume are displayed to the rightmost of the chart.
Users can determine the amount of most recent FVG's to display from the "Display Amount" setting. Disabling the "Consider Mitigation" setting will return mitigated FVGs in the plot, which can be useful to know where most FVGs were located.
We can use the area average of the FVGs with the most associated volume as potential support/resistance levels. Users can extend more FVG's averages by increasing the "Highest Volume Averages" setting.
🔹 Visualizing Volume/Price Relationships of FVG's
A linear regression is fit between FVG's areas average and their associated volume, with this linear regression helping us see where FVG's with specific volume might be located in the future based on existing FVG's.
Note that FVG's do not tend to exhibit linear relationships with their associated volume, the provided linear regression can give a general sense of tendency, but nothing necessarily accurate.
🔶 DETAILS
🔹 Intrabar Data TF
Given a formation of three candles causing an FVG, the volume traded within that FVG area is obtained by looking at the lower timeframe intrabar candles located within the intermediary candle of the formation. The volume of the intrabar candles located within the FVG areas is added up to obtain the associated volume of the FVG.
Using a lower "Intrabar Data TF" allows obtaining more precise volume results, at the cost of computation time and data availability (if there is a high difference between the "Intrabar Data TF" and the chart TF then less FVG can have their associated volume calculated due to Tradingview limitations).
🔹 Display
Users have access to multiple graphical settings affecting how the indicator is displayed.
The "Graph Resolution" setting determines the length of the X axis, with higher values returning more precise results on the location of FVGs over the X axis. Users can also control the number of labels displayed on the X-axis using the numerical input to the right of "Show X-Axis Labels".
Additionally, users can color FVG areas using a gradient relative to the size of the area, or the volume associated with the FVG.
🔶 SETTINGS
Display Amount: Amount of most recent FVGs to display.
Highest Volume Averages: Amount of FVG averages levels with the highest volume to display and extend.
Consider Mitigation: Only display unmitigated FVGs.
Filter FVGs Outside Visible Range: Only display FVGs areas that are located within the user chart visible range.
Intrabar Data TF: Timeframe used to obtain intrabar data. Should be lower than the user chart timeframe.
Engulfing CandlesticksThe Engulfing Candlesticks indicator is a powerful tool for traders and investors to identify potential reversal patterns in financial markets. This indicator is based on the popular candlestick pattern recognition technique, which has been used for centuries to predict market trends and identify trading opportunities.
The Engulfing Candlesticks indicator specifically looks for two types of patterns: Bullish Engulfing and Bearish Engulfing. A Bullish Engulfing pattern occurs when a small bearish candle is followed by a larger bullish candle that completely engulfs the previous candle, indicating a potential reversal from a downtrend to an uptrend. On the other hand, a Bearish Engulfing pattern occurs when a small bullish candle is followed by a larger bearish candle that completely engulfs the previous candle, indicating a potential reversal from an uptrend to a downtrend.
The Engulfing Candlesticks indicator is designed to be easy to use and understand, even for traders who are new to candlestick pattern recognition. The indicator plots a yellow color for Bullish Engulfing patterns and a purple color for Bearish Engulfing patterns, making it easy to visualize and identify potential trading opportunities.
One of the key benefits of the Engulfing Candlesticks indicator is its ability to identify potential reversal patterns early, allowing traders to enter trades at the beginning of a new trend. This can be especially useful in markets that are highly volatile or subject to sudden changes in direction.
In addition to its ability to identify reversal patterns, the Engulfing Candlesticks indicator can also be used as a confirmation tool for other trading strategies. For example, a trader who is using a moving average crossover strategy may use the Engulfing Candlesticks indicator to confirm the validity of the signal.
The Engulfing Candlesticks indicator is also highly customizable, allowing traders to adjust the sensitivity of the indicator to suit their individual trading style. This can be especially useful for traders who are looking to trade in specific market conditions, such as during times of high volatility or in markets with low liquidity.
Overall, the Engulfing Candlesticks indicator is a powerful tool for traders and investors who are looking to identify potential reversal patterns and trading opportunities in financial markets. Its ease of use, customization options, and ability to identify early reversal patterns make it a valuable addition to any trading strategy.
Here are the settings for the Engulfing Candlesticks indicator:
Length: This setting determines the number of bars used to calculate the engulfing pattern. A higher value will result in more precise signals, but may also reduce the number of signals generated.
Color 0: This setting determines the color used for Bullish Engulfing patterns.
Color 1: This setting determines the color used for Bearish Engulfing patterns.
Bar Color: This setting determines whether the indicator will color the bars based on the engulfing pattern.
Alerts: This setting determines whether the indicator will generate alerts when an engulfing pattern is detected.
Note: These settings may vary depending on the specific trading platform or software being used.
OrderFlow Absorption IndicatorWhat it Does
The OrderFlow Absorption Indicator marks areas where the price absorbs a large volume of aggressive market trades. This indicates areas where price may bounce back due to large limit (resting) orders absorbing significant aggressor volume (market orders). Absorption can also be seen as "preventing" or "stopping" the other side from breaking through a price level (e.g. bids stopping an influx of sell market orders). Absorption may signal a change in sentiment, potentially leading to a pullback or reversal.
An Example of Absorption
Of course, it is not always the case that such bullish absorption will initiate a trend as the example above. The OrderFlow Absorption Indicator merely serves as a tool for spotting possible absorption points in the market which you can incorporate into your trading arsenal.
How it Works
The indicator actively monitors price changes and records volume accumulated at a price level. If the price bounces back to at least where it was before the current price move, the indicator records this as absorption, provided it meets the Volume Requirement and optional Time Requirement.
How to Use it
1. Set Parameters
Choose your desired tick size and volume filter value. If unsure, refer to the table on the top right of the chart for recommended values. An automatic volume limit filter mode is also available.
Automatic Limit Mode : Enable this mode to have the indicator automatically select a volume filter value. It calculates the standard deviation of the last n minutes of volume and multiplies it by a volume multiplier. You can adjust these parameters.
Higher Volume Filter : Setting a higher volume filter value results in fewer, but higher quality detections, reducing noise.
2. Enabling the Time Limit
Enabling the time limit further improves detection quality by filtering out price levels that can defend against quick, sudden aggressive orders, acting as confirmation and indicating strong sentiment and resilient liquidity.
3. Enabling Historical Data Absorption
The indicator can also detect absorption in historical data, though less accurately than in real-time due to OHLCV aggregation.
You can select the granularity of historical data.
Lower granularity (e.g., 1 second) : Provides more accurate detections but may slow down the indicator.
Higher granularity : Improves speed but reduces detection accuracy.
Other Features
Hovering : When hovering over an absorption point, the interface reveals the price where the absorption occurred, along with the volume absorbed by the bids and asks, as well as the volume filter value used.
Delta Mode : In Delta mode, the system calculates the difference between the volume absorbed by bids and asks, revealing points only when the absolute value of this difference exceeds the volume filter value. Especially useful for larger tick sizes.
Troubleshooting
If the indicator doesn't mark anything, it means the traded volume hasn't exceeded the set volume filter value within the specified price intervals(tick size) and time limit. Adjust these settings as necessary.
Uptrick: MultiMA_VolumePurpose:
The "Uptrick: MultiMA_Volume" indicator, identified by its abbreviated title 'MMAV,' is meticulously designed to provide traders with a comprehensive view of market dynamics by incorporating multiple moving averages (MAs) and volume analysis. With adjustable inputs and customizable visibility options, traders can tailor the indicator to their specific trading preferences and strategies, thereby enhancing its utility and usability.
Explanation:
Input Variables and Customization:
Traders have the flexibility to adjust various parameters, including the lengths of different moving averages (SMA, EMA, WMA, HMA, and KAMA), as well as the option to show or hide each moving average and volume-related components.
Customization options empower traders to fine-tune the indicator according to their trading styles and market preferences, enhancing its adaptability across different market conditions.
Moving Averages and Trend Identification:
The script computes multiple types of moving averages, including Simple (SMA), Exponential (EMA), Weighted (WMA), Hull (HMA), and Kaufman's Adaptive (KAMA), allowing traders to assess trend directionality and strength from various perspectives.
Traders can determine potential price movements by observing the relationship between the current price and the plotted moving averages. For example, prices above the moving averages may suggest bullish sentiment, while prices below could indicate bearish sentiment.
Volume Analysis:
Volume analysis is integrated into the indicator, enabling traders to evaluate volume dynamics alongside trend analysis.
Traders can identify significant volume spikes using a customizable threshold, with bars exceeding the threshold highlighted to signify potential shifts in market activity and liquidity.
Determining Potential Price Movements:
By analyzing the relationship between price and the plotted moving averages, traders can infer potential price movements.
Bullish biases may be suggested when prices are above the moving averages, accompanied by rising volume, while bearish biases may be indicated when prices are below the moving averages, with declining volume reinforcing the potential for downward price movements.
Utility and Potential Usage:
The "Uptrick: MultiMA_Volume" indicator serves as a comprehensive tool for traders, offering insights into trend directionality, strength, and volume dynamics.
Traders can utilize the indicator to identify potential trading opportunities, confirm trend signals, and manage risk effectively.
By consolidating multiple indicators into a single chart, the indicator streamlines the analytical process, providing traders with a concise overview of market conditions and facilitating informed decision-making.
Through its customizable features and comprehensive analysis, the "Uptrick: MultiMA_Volume" indicator equips traders with actionable insights into market trends and volume dynamics. By integrating trend analysis and volume assessment into their trading strategies, traders can navigate the markets with confidence and precision, thereby enhancing their trading outcomes.
WRESBAL PlusWRESBAL Plus is an improved way of looking at the same data that drives WRESBAL, which is a commonly used series on FRED.
WRESBAL is a weekly average of combined balances on FRED using inputs that are weekly averages in some cases. For example the Treasury General Account has multiple FRED series including WDTGAL (wednesday level) and WTREGEN (wednesday weekly average) There are data sets that are tracking the same metrics which are updated daily such as RRPONTSYD as opposed to WLRRAL.
This situation leads to an opportunity to create a new and improved WRESBAL with the data that is updated more frequently. WRESBAL Plus solves the problem of waiting for weekly averages to update trends.
WRESBAL plus combines data sets from FRED that are updated more frequently and are the basis for the original WRESBAL equation. WRESBAL Plus offers a signal that predicts where WRESBAL will go, and this is important when determining the direction of asset prices as they relate to liquidity. One example of an asset that closely follows WRESBAL is Bitcoin.
TASC 2023.10 COT Commercials Indicator█ OVERVIEW
This script implements the COT Commercials Indicator introduced by Alfred François Tagher in an article featured in TASC's October 2023 edition of Traders' Tips . The indicator is designed for use in futures markets and represents a fast stochastic (%K) calculated based on the commercial open interest values of an asset derived from the weekly Commitments Of Traders (COT) report .
█ CONCEPTS
The COT report, issued by the Commodity Futures Trading Commission (CFTC) , presents a breakdown of reportable open interest positions held by various trader groups—commercial, noncommercial, and nonreportable (small traders). Open interest reflects the total number of derivative contracts entered by market participants but not yet settled. Consequently, it can serve as a measure of market activity and liquidity.
The indicator showcased here aims to analyze changes in the reported net values of open interest for commercial traders/hedgers (often referred to as 'smart money', as they deal directly in underlying commodities). The net values are positive when the commercial traders have more long positions than short ones and negative when they hold more short positions than long ones. Positive net values indicate that commercial traders hold more long positions than short ones, while negative values indicate the opposite. Thus, overbought and oversold conditions of the COT Commercials Indicator potentially suggest collective bullish and bearish sentiments, respectively.
█ CALCULATIONS
The calculations involve these steps:
1. Net open interest values are extracted from COT data using the LibraryCOT library provided by TradingView.
2. A fast stochastic indicator (%K) is then applied to normalize these net values.
The script also provides an option of calculating and plotting the indicator curve for noncommercial (speculators) open interest.
TraderJoe TickMarket sentiment and market breadth are important factors for traders to consider when making trading decisions.
The TICK index , which reflects the buying and selling activity of an entire index, can provide valuable insights into market sentiment and breadth.
1. Assessing Market Sentiment:
- Positive TICK: When the TICK index is consistently positive (indicating more stocks are being bought at or above the asking price), it suggests overall bullish sentiment in the market.
- Negative TICK: Conversely, a consistently negative TICK indicates bearish sentiment, where more stocks are being sold at or below the asking price.
2. Market Breadth:
- Look at the TICK readings for various market indexes, not just one. If all major market indexes are experiencing the same sentiment (e.g., all have aggressive buyers), it's a stronger signal of a broader market trend.
3. Using the TICK for Entry and Exit:
- Positive TICK can be an entry signal for long positions. Traders might consider going long when the TICK index is consistently positive, indicating strong buying pressure in the market.
- Negative TICK can be an entry signal for short positions. When the TICK is consistently negative, it suggests selling pressure, making shorting more attractive.
- Exit positions or take profits when the TICK starts to show signs of reversing from its extreme levels. An excessively positive TICK might indicate overbought conditions, while an overly negative TICK may signal oversold conditions.
4. Combining TICK with Other Indicators:
- It's often beneficial to combine TICK analysis with other technical and fundamental indicators to increase the accuracy of your trading decisions. For example, you could use moving averages, RSI, or support and resistance levels to confirm your entry and exit points.
5. Low Float Stocks and TICK:
- Low float stocks can be more volatile, making TICK analysis even more crucial. In these cases, watch for extreme TICK readings, as they can trigger rapid price movements.
- Be cautious when trading low float stocks, as they can be susceptible to price manipulation due to limited liquidity. Use proper risk management techniques, like setting stop-loss orders.
6. Stay Informed:
- Keep an eye on news and events that might explain sudden shifts in market sentiment. Unexpected news, economic releases, or geopolitical events can quickly change market dynamics.
Scalping The Bull - BullBot for Stock and ForexName : Scalping The Bull - BullBot for Stock and Forex.
Category : Scalping tool for the Stock and Forex market, optimized for trend following and mean reversal operations.
Timeframe : Recommended to be used on timeframe 1m, 5m, 30m, 1h, 1D, depending on the specific technique.
Technical Analysis : The indicator implements the trading techniques of Scalping The Bull. Even if the techniques are exquisitely discretionary, we have written the indicator with our interpretation of the them to identify the patterns automatically. We also codified a the process described below, giving the possibility to trade mechanically. The indicator is designed for a short-term (intraday or scalping) trend-following approach, where the entry is made on the breakout of significant price levels. Entry setups are calculated by price action analysis using 5-10-50-200 EMAs as reference. According to the interaction of price action with the EMAs, patterns are generated, and the indicator finds them independently.
Suggested use :
The process follows:
1. Selection of the instrument to trade on
Given the short duration of the trades, there must be sufficient volatility and liquidity. You can add the different instruments to a TradingView watch list and then make a first skim, considering the daily percentage change. Once an instrument has been selected, the indicator allows you to check at a glance things like the Rate of Change (ROC) and the detection of opening gaps.
2. Identification of the main trend
Once in the instrument, the main trend of the underlying is identified through the 50-200 EMAs: if the fast EMA is greater than the slow one, we have a bullish trend, otherwise a bearish trend on a higher timeframe than the one in which the trade is executed. The rule is to trade in favor of the main trend. For this reason, the indicator prints to chart reference averages. It is also recommended to check the daily timeframe, always using the averages, preferring those instruments where the price follows an average without touching it too often.
3. Identification of breakout levels
Breakout levels favoring the trend are identified near the daily high/minimum, the previous day's high/minimum, or the weekly high/minimum. The indicator prints such levels on the chart with lines.
4. Identification of secondary trends and entry levels
Patterns can be divided into three categories. Trend-based ones have the function of identifying the secondary trend and then confirming the trade. Breakout patterns are used to find entry levels.
a. Trend Continuation Patterns (Price/EMA) : are used to get confirmation that the minor trend is in agreement with the major trend (i.e., Shimano, Dive, Dip, Anti-crossing).
b. Trend Change Patterns (Price/EMA) : are used to get confirmation that a minor trend in the opposite direction of the main trend is ending and therefore are used to get further confirmation of the trade (i.e., Bounce, Viagra, Return to Highs/Minimums).
c. Breakout Patterns : identify the optimal price levels for trade entry (i.e., Third Touch, Bud, Maxi Bud, Color Change).
Details on how the specific patterns are computed can be found below in the description of this indicator.
5. Definition of Stop Loss and Take Profit
Once the entry price is calculated, different types of Stop Loss (e.g., relative minimum of Tot candles, first or second average below price, price levels) and Take Profit (relative maximum of Tot candles, first or second average above price, price levels) can be considered. A risk/reward ratio of at least 1.2 should always be considered, depending on one's risk appetite.
6. Define the position management strategy
Normally, when a mock breakout is executed, the advice is to exit the trade as soon as possible or at least to bring the stop-loss to break even. A trailing stop on relative lows or the nearest average is also possible.
7. Enter the position
Usually, with a stop order, follow the rules defined in the previous steps.
Configuration :
Graphical configuration :
Show ROC filter : turns the background green when the asset has enough volatility to activate patterns.
Show Legend : enables the description on the lines of the trigger points.
Show Table : displays the volatility table where the GAP and ROC (Rate of Change) information is present.
Show Take Profit % - Automatic : shows the percentage scale that automatically adjusts to the volatility of the chart to identify the Take Profit and Stop Loss at a glance.
Label color : adjusts the color of the labels.
Trigger Point Colors : Adjust the colors of the trigger points.
Trigger Points
Today's highs and lows : draw on the chart the closing price of Yesterday, the opening price of the daily candlestick, and the highs and lows of the day (high in purple, low in red, Yesterday's close in green, and open in orange).
Yesterday's highs and lows : draw on the chart the highs and lows of the previous day (high in yellow, low in red). An alert can be activated with a % offset.
Weekly highs and lows : draw the highs and lows of the previous week on the chart (high in white, low in red). An alert can be activated with a % offset.
Price Patterns :
Gap : a label and an alert can be activated.
Pay..!! : a label and an alert can be activated.
Third Touch and Third Touch on EMA : a label and an alert can be activated.
Return to Highs : a label and an alarm can be activated.
Return to Lows : a label and an alarm can be activated.
Long and Short Color Change : a label and an alert can be activated.
EMA Patterns :
Viagra long and short : a label and an alert can be activated for the specific market and EMA situations.
Shimano : a label and an alert can be activated with a configurable candlestick threshold.
Emergence - Dive : a label and an alert can be activated.
Anti-crossing - Anti-crossing short : a label and an alarm can be activated.
Bounce on EMA long and short : a label and an alert can be activated with a configurable tick tolerance.
Bud - Maxi Bud - Bud Short : a label and an alert can be activated on all timeframes.
Alerts :
Activate all alerts : you can activate alerts by clicking on Alerts, then on the condition "BullBot" and then on "Any alert() function.". So that you know, creating alerts on the price-EMA crossing is also possible.
Patterns :
The list of patterns that are automatically recognized by the indicator follows.
Gap : indicates when a Gap Up or Gap Down occurs at the opening of a session, particularly useful in the equity markets.
Pay : signals when the stock has risen by a sufficient percentage to be tradable, adjusts the triggering of many bounce and breakout patterns.
Viagra : signaled for both a long and short setup, indicates an overbought or oversold situation by the appearance of gaps and a series of consecutive candles contained by the fast EMAs (5 -10) depending on the market of reference.
Third touch and third touch EMA : signals a breakout conformation, indicated when three touches occur at the same price level or below one of the slow EMAs (50 - 200).
Shimano : detects lateralization between Ema 50 and Ema 200, indicating that we are in a retracement phase and that a new impulsive move may soon develop.
Emergence - Dive : these are the main patterns for identifying the trend. The indicator signals when the EMA 50 and 200 are approaching to anticipate their crossing to the top or bottom.
Anti-crossing long - Anti-crossing short : occurs when the price, after an important impulse, makes a retracement to the EMA 200, bringing the EMA 50 to approach it without ever touching it, but before the cross, an opposite impulse determines the end of the retracement and the resumption of the trend in place.
Rebound : signals in the first rebound on the EMA 50 and EMA 200 after an impulse that recorded new highs or lows.
Return to the Highs - Return to the Lows : this pattern is closely related to rebounds. It indicates that the price is approaching the highs or lows again after a rebound on a slow EMA.
Change Color : is based on the previous session's closing price. It indicates when the price is approaching either from above or below this level, which determines the Color of the daily candlestick.
Bud - Maxi Bud - Bud Short : Bud occurs when the price breaks above EMA 10, and there are at least two consecutive candles between the high and low of the breakout impulse. Maxi Bud signals near the day's highs when this situation occurs, and Bud Short signals the breakout levels for a short trade.
Fundamental Strength IndicatorName of the indicator: Fundamental Strength Indicator
A brief description of the indicator:
Using this indicator, you can evaluate a company in terms of the strength of its financial performance and see how that score has changed over time.
The background to the creation of the indicator:
The main idea that inspired me to create this indicator is: " Even if you buy just 1 share of a company, treat it like buying the whole business ". However, when I need to evaluate the business of thousands of public companies traded on exchanges, there is an objective difficulty: it is very time-consuming. To solve this problem, I had to create a scoring system of the fundamental analysis of the company, embodied in this indicator.
What the indicator looks like:
- First, it is a Histogram with bars of three colors: green, orange, and red. The width of the histogram depends on the depth of data from the company statements. The more historical data, the wider the histogram over time.
The green color of the bars means that the company has been showing excellent financial results by the sum of the factors in that time period. According to my terminology, the company has a " strong foundation " during this period. Green corresponds to values between 8 and 15 (where 15 is the maximum possible positive value on the sum of the factors).
The orange color of the bars means that according to the sum of factors during this period the company demonstrated mediocre financial results, i.e. it has a " mediocre foundation ". Orange color corresponds to values from 1 to 7.
The red color of the bars means that according to the sum of factors in this period of time, the company demonstrated weak financial results, i.e. it has a " weak foundation ". The red color corresponds to values from -15 to 0 (where -15 is the maximum possible negative value on the sum of factors).
- Second, this is the Blue Line , which is the moving average of the Histogram bars over the last year (*). Averaging over the year is necessary in order to obtain a weighted estimate that is not subject to medium-term fluctuations. It is by the last value of the blue line that the actual Fundamental Strength of the company is determined.
(*) The last year means the last 252 trading days, including the current trading day.
- Third, these are operating, investing, and financing Cash Flows expressed in Diluted net income. These flows look like thick green, orange, and red lines, respectively.
- Fourth, this is the Table on the left, which shows the latest actual value of the Fundamental Strength and Cash Flows.
Indicator settings:
In the indicator settings, I can disable the visibility of the Histogram, Blue Line, Cash Flows (each separately), and Table. It helps to study each of the parameters separately. It is also possible to change the color, transparency, and thickness of lines.
Mandatory requirements for using the indicator:
- works only on a daily timeframe;
- only applies to shares of public companies;
- company financial statements for the last 4 quarters and more are required;
- it is necessary to have the data from the Balance sheet, Income statement, and Cash flow statement, required for the calculation.
If at least one component required for calculating the Fundamental Strength is missing, the message " no data to calculate the Fundamental Strength correctly " is displayed. In the same case, but for the operating cash flow, the message " no data to calculate the Operating Cash Flow correctly " is shown, and similarly for other flows.
What is the value of the Fundamental Strength Indicator:
- allows for a quantitative assessment of a company's financial performance in points (from -15 to 15 points);
- allows you to visually track how the company's financial performance has changed (positively/negatively) over time;
- allows to visually trace the movement of main cash flows over time;
- speeds up the process of selecting companies for your shortlist (if you are focused on financial results when selecting companies);
- allows you to protect yourself from investing in companies with weak and mediocre fundamentals.
Indicator calculation methodology:
Guided by the "Treat stock investments as buying the whole business" approach, you can imagine what kind of business an investor is interested in owning and simultaneously determine the input parameters for calculating the indicator.
(!) Here it is important to emphasize that the idea of a benchmark business for investment is a subjective notion, so be sure to check whether it coincides with your own opinion.
For me, a benchmark business is:
- A business that operates efficiently without diminishing the return on shareholders' investment. To assess the efficiency and profitability of a business, I use the following financial ratios (*): Diluted EPS and Return on Equity (ROE). The first two parameters for calculating the indicator are there.
- A business that scales sales and optimizes its costs. From this point of view, the following financial ratios are suitable: Gross margin, Operating expense ratio, and Total revenue. Plus three other metrics.
- A business that turns goods/services into cash quickly and does not fall behind on payments to suppliers. The following financial ratios will fit here: Days payable, Days sales outstanding, and Inventory to revenue ratio. These are three more metrics.
- A business that does not resort to significant accounts payable and shows financial strength. Here I use the following financial ratios: Current ratio, Interest coverage, and Debt to revenue ratio. These are the last three parameters.
(*) If you want to learn more about these financial ratios, I suggest reading my two articles on TradingView:
Financial ratios: digesting them together
What can financial ratios tell us?
Next, each of the parameters is assigned a certain number of points based on its last value or the position of that value relative to the annual maximum and minimum.
For example, if the Current ratio:
- greater than or equal to 2 (+1 point);
- less than or equal to 1 (-1 point);
- more than 1 but less than 2 (0 points).
Or for example, if Diluted EPS:
- near or above the annual high (+2 points);
- near the annual minimum and below (-2 points);
- between the annual maximum and minimum (0 points).
And so on with each of the parameters.
As a result, the maximum number of points a company can score is 15 points. The minimum number of points a company can score is -15 points. These levels are marked with horizontal dotted lines: the green line is for the maximum value, and the red line is for the minimum.
I track the number of points for each day of a company's life on a three-color Histogram. The resulting average value for the last year is on the Blue Line. For me, it is the last value of the Blue Line that determines - this is the actual Fundamental Strength of the company.
The business valuation model I created is more suitable for companies that produce goods or services, and where tangible assets play a significant role in the business. For example, when analyzing companies in the financial sector, you may see the message "no data to calculate the Fundamental Strength correctly". Many of them may simply be missing data that is used as input for the calculation: Inventory to revenue ratio, Days sales outstanding, etc.
Examples:
Below I will evaluate various companies using the Fundamental Strength Indicator.
Tesla, Inc.
The indicator shows that since 2020, Tesla Inc. has been steadily increasing its Fundamental Strength (from 3.27 in Q1 2020 to 12.79 in Q1 2023). This is noticeable both by the color change of the Histogram from orange to green and by the rising Blue Line. If you look in detail at what has been happening with the financials during this time, it's clear what meaningful work the company has done. Revenues have almost quadrupled. Earnings per share have increased 134 times. At the same time, total debt to revenue fell almost 10 times.
Keurig Dr Pepper Inc.
The company, formed in 2018 by the merger of Keurig Green Mountain and Dr Pepper Snapple Group, has failed to deliver outstanding financial results, causing its Fundamental Strength to fall from 4.63 in Q1 2018 to -0.53 in Q1 2023. During this period, the drop in diluted earnings per share was accompanied by higher debt and deteriorating liquidity.
Costco Wholesale Corporation
Wholesaler Costco has been surprisingly stable in its financial performance and with steady growth in both earnings and revenue. This is the reason why the Histogram bars are exceptionally green throughout the calculation of the indicator. The Fundamental Strength has not changed in three years and is high at 11 points.
As an additional filter, for example, when comparing two companies where all other conditions are equal - I use the dynamics of Cash Flows expressed in Diluted net income (*). These are the thick green, orange, and red lines over the Histogram.
Why do I use income as a unit of measure of Cash Flows? Because it is a good way to make the scale of indicator values the same for companies from different countries, with different currencies. It also allows you to use a single value scale for both Cash Flows and Fundamental Strength.
(*) If you want to learn more about Cash Flows, I suggest reading my two articles on TradingView:
Cash flow statement or Three great rivers
Cash flow vibrations
So, an additional filter shows the dynamics of Cash Flows over time.
To interpret the dynamics of Cash Flows, I pay attention to the following patterns:
- How the cash flows are positioned in relation to each other;
- In which zone each of the cash flows is located - in the positive or negative;
- What is the trend of each of the cash flows;
- How volatile each of the cash flows is.
As an example, let's look at several companies in order to interpret the dynamics of their Cash Flows.
John B. Sanfilippo & Son, Inc.
This is the most ideal situation for me: operating cash flow (green line) is above the other cash flows, investment cash flow (orange line) is near zero and practically unchanged, and financial cash flow (red line) is consistently below zero. This picture shows that the company lives off its operating cash flow, does not increase its debt, does not spend a substantial amount of money on expensive purchases, and retains (does not sell off) assets.
Parker Hannifin Corporation
With stable operating cash flow (green line), the company implements investment programs by raising additional funding. This is noticeable due to an increase in financial cash flow (red line) and a simultaneous decrease in investment cash flow (orange line) with a significant deepening into negative areas. Apparently, there is not enough operating cash flow to realize the planned investments. One has to wonder how sustainable a company can be if it invests in its development using borrowed funds without a subsequent increase in operating cash flow.
Schlumberger N. V.
The chaotic intertwining of cash flows outside of the Fundamental Strength range (-15 to 15) is indicative of the company's rich life, but to me, it is an indicator of high riskiness of its actions. And as we can see, Fundamental Strength has only begun to strengthen in the last year, when the external appearance of cash flow has normalized.
Risk disclaimer:
When working with the Fundamental Strength Indicator and the additional filter in the form of Cash Flows, you should understand that the publication of the Balance sheet, Income statement, and Cash flow statement takes place sometime after the end of the financial quarter. This means that new relevant data for the calculation will only appear after the publication of the new statements. In this regard, there may be a significant change in the values of the Indicator after the publication of new statements. The magnitude of this change will depend both on the content of the new statements and on the number of days between the end of the financial quarter and the publication date of the statements. Until the date of publication of the new statements, the latest relevant data will be used for calculations.
I would like to draw your attention to the fact that the calculation of Fundamental Strength and Cash Flows requires the availability of data for all parameters of the valuation model . It uses data that is exclusively available on TradingView (there is no reconciliation with other sources). If at least one parameter is missing, I switch to another company's analysis to continue using the indicator.
Thus, the Fundamental Strength Indicator and an additional filter in the form of Cash Flows make it possible to evaluate the financial results of the company based on the available data and the methodology I created. A simple visualization in the form of a three-color Histogram, a Blue line, and three thick Cash Flow lines significantly reduces the time for selecting fundamentally strong companies that fit the criteria of the selected model. However, this Indicator and/or its description and/or examples cannot be used as the sole reason for buying or selling stocks or for any other action or inaction related to stocks.
Wick Delta vs Body/Wick BiasThe top and bottom of this indicator use the same logic as my Wick Delta script, but it displays differently, visualising the rejection or buy/sell pressure that wicks can represent. Outliers are highlighted in darker colours and often show inflection points, particular if they've just wicked into liquidity. So the start or end of moves, or a trend change. They can also happen for no reason, or just be a stop hunt. It's all about context, like everything in technical analysis.
The new addition is the centre line which shows whether wicks or bodies or in charge. Kinda like Average True Range (ATR) this script calculates Average True Bodies (ATBs) and compares it with Average True Wicks (ATWs) and shows when one or the other is in charge. So if candle wicks are bigger (>50%) than bodies, you'll see skinny, wick-like columns, and if the bodies are bigger you'll seen thicker, body-like columns. These can show inflection points too.
Keen to hear how people use this, and I intend to add a volume weighting feature when I get to it.
Goertzel Cycle Composite Wave [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Cycle Composite Wave indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading.
*** To decrease the load time of this indicator, only XX many bars back will render to the chart. You can control this value with the setting "Number of Bars to Render". This doesn't have anything to do with repainting or the indicator being endpointed***
█ Brief Overview of the Goertzel Cycle Composite Wave
The Goertzel Cycle Composite Wave is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions.
The Goertzel Cycle Composite Wave is considered a non-repainting and endpointed indicator. This means that once a value has been calculated for a specific bar, that value will not change in subsequent bars, and the indicator is designed to have a clear start and end point. This is an important characteristic for indicators used in technical analysis, as it allows traders to make informed decisions based on historical data without the risk of hindsight bias or future changes in the indicator's values. This means traders can use this indicator trading purposes.
The repainting version of this indicator with forecasting, cycle selection/elimination options, and data output table can be found here:
Goertzel Browser
The primary purpose of this indicator is to:
1. Detect and analyze the dominant cycles present in the price data.
2. Reconstruct and visualize the composite wave based on the detected cycles.
To achieve this, the indicator performs several tasks:
1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components.
2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength.
3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength.
4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the cycles. The color of the lines indicates whether the wave is increasing or decreasing.
This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements, the indicator aims to assist traders in making more informed decisions.
█ What is the Goertzel Algorithm?
The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics.
The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as:
1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed.
2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments.
3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear.
4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues.
The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT:
1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases.
2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing.
3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest.
4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential.
The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics.
█ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications
The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis.
Unveiling Hidden Market Cycles:
Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies.
Developing Quantitative Trading Strategies:
The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets.
For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction.
Enhancing Risk Management:
The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies.
By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy.
Expanding Quantitative Toolkits:
Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately.
Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals.
The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies.
█ Indicator Inputs
src: This is the source data for the analysis, typically the closing price of the financial instrument.
detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components.
The available options are:
hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average.
zlagsmthdt: Detrend using zero-lag moving average centered moving average.
logZlagRegression: Detrend using logarithmic zero-lag linear regression.
hpsmth: Detrend using Hodrick-Prescott filter.
zlagsmth: Detrend using zero-lag moving average.
DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt.
DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt.
DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression.
HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth.
ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth.
MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles.
squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm.
useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles.
useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves.
UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude.
WindowSizePast: These inputs define the window size for the composite wave.
FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles.
BartNoCycles: This input sets the number of cycles to be used in Bartel's test.
BartSmoothPer: This input sets the period for the moving average used in Bartel's test.
BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant.
SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results.
StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles.
UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input.
SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles.
█ Exploring Auxiliary Functions
The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool.
Zero-Lag Moving Average:
The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator.
The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator.
Bartels Probability:
The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets.
The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies.
Detrend Logarithmic Zero-Lag Regression:
The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics.
The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding.
Bartels Cycle Significance Test:
The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding.
The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis.
Hodrick-Prescott Filter:
The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends.
The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive.
Detrending Options: Detrend Centered Moving Average:
The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences.
The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants.
The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average,
█ In-Depth Analysis of the Goertzel Cycle Composite Wave Code
The Goertzel Cycle Composite Wave code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose.
Function signature and input parameters:
The Goertzel Cycle Composite Wave function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past sizes (WindowSizePast), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer).
Initializing variables and arrays:
The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm.
Preprocessing input data:
The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample.
Iterative calculation of Goertzel coefficients:
The core of the Goertzel Cycle Composite Wave algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure.
Cycle strength computation:
The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values.
Phase calculation:
The Goertzel Cycle Composite Wave code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients.
Peak detection and cycle extraction:
The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively.
Sorting cycles by amplitude or cycle strength:
The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data.
Bartels cycle significance test:
If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values.
Waveform calculation:
The Goertzel Cycle Composite Wave code calculates the waveform of the significant cycles for specified time windows. The windows are defined by the WindowSizePast parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted.
Storing waveforms in a matrix:
The calculated waveforms for the cycle is stored in the matrix - goeWorkPast. This matrix holds the waveforms for the specified time windows. Each row in the matrix represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Cycle Composite Wave function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles.
The Goertzel Cycle Composite Wave code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Cycle Composite Wave's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data.
█ Generating and Visualizing Composite Waveform
The indicator calculates and visualizes the composite waveform for specified time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast:
The WindowSizePast is updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
The matrix goeWorkPast is initialized to store the Goertzel results for specified time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information.
Preparing the source data (srcVal) array:
The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth).
Goertzel function call:
The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles.
Initializing arrays for waveforms:
The goertzel array is initialized to store the endpoint Goertzel.
Calculating composite waveform (goertzel array):
The composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component.
Drawing composite waveform (pvlines):
The composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward).
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms and visualizes them on the chart using colored lines.
█ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading
The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading.
Enhancements for Financial Modeling
Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results.
Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data.
Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models.
Enhancements for General and Advanced Trading
Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies.
Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size.
Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions.
Enhancements for High-Frequency Finance Trading
Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity.
Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations.
Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies.
While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike.
█ Understanding the Limitations of the Goertzel Algorithm
While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are:
Lagging nature:
As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses.
Parameter sensitivity:
The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance.
Complexity:
The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions.
Overfitting risk:
As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance.
Limited applicability:
The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity.
While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions.
█ Interpreting Results
The Goertzel Cycle Composite Wave indicator can be interpreted by analyzing the plotted lines. The indicator plots two lines: composite waves. The composite wave represents the composite wave of the price data.
The composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend.
Interpreting the Goertzel Cycle Composite Wave indicator involves identifying the trend of the composite wave lines and matching them with the corresponding bullish or bearish color.
█ Conclusion
The Goertzel Cycle Composite Wave indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Cycle Composite Wave indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Cycle Composite Wave indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development.
█ Footnotes
What is the Bartels Test for Cycle Significance?
The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century.
The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series.
The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges.
If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component.
The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles.
Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Goertzel Browser [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Browser indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading.
█ Brief Overview of the Goertzel Browser
The Goertzel Browser is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions.
The primary purpose of this indicator is to:
1. Detect and analyze the dominant cycles present in the price data.
2. Reconstruct and visualize the composite wave based on the detected cycles.
3. Project the composite wave into the future, providing a potential roadmap for upcoming price movements.
To achieve this, the indicator performs several tasks:
1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components.
2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength.
3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength.
4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the past and dotted lines for the future projections. The color of the lines indicates whether the wave is increasing or decreasing.
5. Displaying cycle information: The indicator provides a table that displays detailed information about the detected cycles, including their rank, period, Bartel's test results, amplitude, and phase.
This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements and their potential future trajectory, the indicator aims to assist traders in making more informed decisions.
█ What is the Goertzel Algorithm?
The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics.
The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as:
1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed.
2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments.
3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear.
4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues.
The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT:
1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases.
2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing.
3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest.
4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential.
The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics.
█ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications
The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis.
Unveiling Hidden Market Cycles:
Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies.
Developing Quantitative Trading Strategies:
The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets.
For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction.
Enhancing Risk Management:
The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies.
By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy.
Expanding Quantitative Toolkits:
Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately.
Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals.
The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies.
█ Indicator Inputs
src: This is the source data for the analysis, typically the closing price of the financial instrument.
detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components.
The available options are:
hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average.
zlagsmthdt: Detrend using zero-lag moving average centered moving average.
logZlagRegression: Detrend using logarithmic zero-lag linear regression.
hpsmth: Detrend using Hodrick-Prescott filter.
zlagsmth: Detrend using zero-lag moving average.
DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt.
DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt.
DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression.
HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth.
ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth.
MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles.
squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm.
useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles.
useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves.
UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude.
WindowSizePast and WindowSizeFuture: These inputs define the window size for past and future projections of the composite wave.
FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles.
BartNoCycles: This input sets the number of cycles to be used in Bartel's test.
BartSmoothPer: This input sets the period for the moving average used in Bartel's test.
BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant.
SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results.
UseCycleList: This boolean input determines whether a user-defined list of cycles should be used for constructing the composite wave. If set to false, the top N cycles will be used.
Cycle1, Cycle2, Cycle3, Cycle4, and Cycle5: These inputs define the user-defined list of cycles when 'UseCycleList' is set to true. If using a user-defined list, each of these inputs represents the period of a specific cycle to include in the composite wave.
StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles.
UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input.
SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles.
█ Exploring Auxiliary Functions
The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool.
Zero-Lag Moving Average:
The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator.
The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator.
Bartels Probability:
The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets.
The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies.
Detrend Logarithmic Zero-Lag Regression:
The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics.
The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding.
Bartels Cycle Significance Test:
The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding.
The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis.
Hodrick-Prescott Filter:
The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends.
The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive.
Detrending Options: Detrend Centered Moving Average:
The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences.
The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants.
The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average,
█ In-Depth Analysis of the Goertzel Browser Code
The Goertzel Browser code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose.
Function signature and input parameters:
The Goertzel Browser function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past and future window sizes (WindowSizePast, WindowSizeFuture), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, goeWorkFuture, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer).
Initializing variables and arrays:
The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm.
Preprocessing input data:
The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample.
Iterative calculation of Goertzel coefficients:
The core of the Goertzel Browser algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure.
Cycle strength computation:
The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values.
Phase calculation:
The Goertzel Browser code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients.
Peak detection and cycle extraction:
The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively.
Sorting cycles by amplitude or cycle strength:
The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data.
Bartels cycle significance test:
If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values.
Waveform calculation:
The Goertzel Browser code calculates the waveform of the significant cycles for both past and future time windows. The past and future windows are defined by the WindowSizePast and WindowSizeFuture parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted.
Storing waveforms in matrices:
The calculated waveforms for each cycle are stored in two matrices - goeWorkPast and goeWorkFuture. These matrices hold the waveforms for the past and future time windows, respectively. Each row in the matrices represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Browser function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles.
The Goertzel Browser code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Browser's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data.
█ Generating and Visualizing Composite Waveform
The indicator calculates and visualizes the composite waveform for both past and future time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast and WindowSizeFuture:
The WindowSizePast and WindowSizeFuture are updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
Two matrices, goeWorkPast and goeWorkFuture, are initialized to store the Goertzel results for past and future time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information.
Preparing the source data (srcVal) array:
The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth).
Goertzel function call:
The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles.
Initializing arrays for past and future waveforms:
Three arrays, epgoertzel, goertzel, and goertzelFuture, are initialized to store the endpoint Goertzel, non-endpoint Goertzel, and future Goertzel projections, respectively.
Calculating composite waveform for past bars (goertzel array):
The past composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component.
Calculating composite waveform for future bars (goertzelFuture array):
The future composite waveform is calculated in a similar way as the past composite waveform.
Drawing past composite waveform (pvlines):
The past composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward).
Drawing future composite waveform (fvlines):
The future composite waveform is drawn on the chart using dotted lines. The color of the lines is determined by the direction of the waveform (fuchsia for upward, yellow for downward).
Displaying cycle information in a table (table3):
A table is created to display the cycle information, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
Filling the table with cycle information:
The indicator iterates through the detected cycles and retrieves the relevant information (period, amplitude, phase, and Bartel value) from the corresponding arrays. It then fills the table with this information, displaying the values up to six decimal places.
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms for both past and future time windows and visualizes them on the chart using colored lines. Additionally, it displays detailed cycle information in a table, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
█ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading
The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles and potential future impact. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading.
Enhancements for Financial Modeling
Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results.
Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data.
Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models.
Enhancements for General and Advanced Trading
Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies.
Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size.
Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions.
Enhancements for High-Frequency Finance Trading
Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity.
Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations.
Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies.
While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike.
█ Understanding the Limitations of the Goertzel Algorithm
While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are:
Lagging nature:
As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses.
Parameter sensitivity:
The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance.
Complexity:
The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions.
Overfitting risk:
As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance.
No guarantee of future performance: While the script can provide insights into past cycles and potential future trends, it is important to remember that past performance does not guarantee future results. Market conditions can change, and relying solely on the script's predictions without considering other factors may lead to poor trading decisions.
Limited applicability: The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity.
While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions.
█ Interpreting Results
The Goertzel Browser indicator can be interpreted by analyzing the plotted lines and the table presented alongside them. The indicator plots two lines: past and future composite waves. The past composite wave represents the composite wave of the past price data, and the future composite wave represents the projected composite wave for the next period.
The past composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. On the other hand, the future composite wave line is a dotted line with fuchsia indicating a bullish trend and yellow indicating a bearish trend.
The table presented alongside the indicator shows the top cycles with their corresponding rank, period, Bartels, amplitude or cycle strength, and phase. The amplitude is a measure of the strength of the cycle, while the phase is the position of the cycle within the data series.
Interpreting the Goertzel Browser indicator involves identifying the trend of the past and future composite wave lines and matching them with the corresponding bullish or bearish color. Additionally, traders can identify the top cycles with the highest amplitude or cycle strength and utilize them in conjunction with other technical indicators and fundamental analysis for trading decisions.
This indicator is considered a repainting indicator because the value of the indicator is calculated based on the past price data. As new price data becomes available, the indicator's value is recalculated, potentially causing the indicator's past values to change. This can create a false impression of the indicator's performance, as it may appear to have provided a profitable trading signal in the past when, in fact, that signal did not exist at the time.
The Goertzel indicator is also non-endpointed, meaning that it is not calculated up to the current bar or candle. Instead, it uses a fixed amount of historical data to calculate its values, which can make it difficult to use for real-time trading decisions. For example, if the indicator uses 100 bars of historical data to make its calculations, it cannot provide a signal until the current bar has closed and become part of the historical data. This can result in missed trading opportunities or delayed signals.
█ Conclusion
The Goertzel Browser indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Browser indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Browser indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development.
█ Footnotes
What is the Bartels Test for Cycle Significance?
The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century.
The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series.
The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges.
If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component.
The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles.
Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
The first term represents the deviation of the data from the trend.
The second term represents the smoothness of the trend.
λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Simple ICT Market Structure by toodegreesThis Simple ICT Market Structure is based on the teachings of ICT, specifically in his episode 12 of the Public 2022 Mentorship.
The only omission here is the peculiar calculation of Intermediate Term points, for which I am not using the concept of repricing imbalances – this can be added later!
Feel free to use this tool, however it is quite simple and market structure is something we all know very well how to spot. In my opinion it is helpful to display the long term swing points to identify more mature pools of liquidity.
The reason for coding this tool is to help new coders understand PineScript (I have a video tutorial where I code this from start to finish), as well as fostering some algorithmic thinking in your trading of ICT Concepts and Algorithmic Delivery.
If you have any questions about the code, shoot me a message!
Hope you learn something and GLGT!
Supply and Demand Visible Range [LuxAlgo]The Supply and Demand Visible Range indicator displays areas & levels on the user's chart for the visible range using a novel volume-based method. The script also makes use of intra-bar data to create precise Supply & Demand zones.
🔶 SETTINGS
Threshold %: Percentage of the total visible range volume used as a threshold to set supply/demand areas. Higher values return wider areas.
Resolution: Determines the number of bins used to find each area. Higher values will return more precise results.
Intra-bar TF: Timeframe used to obtain intra-bar data.
🔶 USAGE
The supply/demand areas and levels displayed by the script are aimed at providing potential supports/resistances for users. The script's behavior makes it recalculate each time the visible chart interval/range changes, as such this script is more suited as a descriptive tool.
Price reaching a supply (upper) area that might have been tested a few times might be indicative of a potential reversal down, while price reaching a demand (lower) area that might have been tested a few times could be indicative of a potential reversal up.
The width of each area can also indicate which areas are more liquid, with thinner areas indicating more significant liquidity.
The user can control the width of each area using the Threshold % setting, with a higher setting returning wider areas. The precision setting can also return wider supply/demand areas if very low values are used and has the benefit of improving the script execution time at the cost of precision.
The Supply and Demand Zones indicator returns various levels. The solid-colored levels display the average of each area, while dashed colored lines display the weighted averages of each area. These weighted averages can highlight more liquid price levels within the supply/demand areas.
Central solid/dashed lines display the average between the areas' averages and weighted averages.
🔶 DETAILS
Each supply/demand area is constructed from volume data. The calculation is done as follows:
The accumulated volume within the chart visible range is calculated.
The chart visible range is divided into N bins of equal width (where N is the resolution setting)
Calculation start from the highest visible range price value for the supply area, and lowest value for the demand area.
The volume within each bin after the starting calculation level is accumulated, once this accumulated volume is equal or exceed the threshold value ( p % of the total visible range volume) the area is set.
Each bin volume accumulation within an area is displayed on the left, this can help indicate how fast volume accumulates within an area.
🔶 LIMITATIONS
The script execution time is dependent on all of the script's settings, using more demanding settings might return errors so make sure to be aware of the potential scenarios that might make the script exceed the allowed execution time:
Having a chart's visible range including a high number of bars.
Using a high number of bins (high resolution value) will increase computation time, this can be worsened by using a high threshold %.
Using very low intra-bar timeframe can drastically increase computation time but can also simply throw an error if the chart timeframe is high.
Users facing issues can lower the resolution value or use the chart timeframe for intra-bar data.
ICT Algorithmic Macro Tracker° (Open-Source) by toodegreesDescription:
The ICT Algorithmic Macro Tracker° Indicator is a powerful tool designed to enhance your trading experience by clearly and efficiently plotting the known ICT Macro Times on your chart.
Based on the teachings of the Inner Circle Trader , these Time windows correspond to periods when the Interbank Price Delivery Algorithm undergoes a series of checks ( Macros ) and is probable to move towards Liquidity.
The indicator allows traders to visualize and analyze these crucial moments in NY Time:
- 2:33-3:00
- 4:03-4:30
- 8:50-9:10
- 9:50-10:10
- 10:50-11:10
- 11:50-12:10
- 13:10-13:50
- 15:15-15:45
By providing a clean and clutter-free representation of ICT Macros, this indicator empowers traders to make more informed decisions, optimize and build their strategies based on Time.
Massive shoutout to @reastruth for his ICT Macros Indicator , and for allowing to create one of my own, go check him out!
Indicator Features:
– Track ongoing ICT Macros to aid your Live analysis.
- Gain valuable insights by hovering over the plotted ICT Macros to reveal tooltips with interval information.
– Plot the ICT Macros in one of two ways:
"On Chart": visualize ICT Macro timeframes directly on your chart, with automatic adjustments as Price moves.
Pro Tip: toggle Projections to see exactly where Macros begin and end without difficulty.
"New Pane": move the indicator two a New Pane to see both Live and Upcoming Macro events with ease in a dedicated section
Pro Tip: this section can be collapsed by double-clicking on the main chart, allowing for seamless trading preparation.
This indicator is available only on the TradingView platform.
⚠️ Open Source ⚠️
Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated.
⚠️ Terms and Conditions ⚠️
This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms.
ICT Killzone by JeawThis is an indicator script for TradingView called "ICT Killzone". It is a useful tool for identifying the London and New York open and close sessions, as well as the Asian range on the chart. The appearance of the "killzones" can be customized by selecting colors and transparencies for each session. Boxes can also be displayed around each session and labels with additional information can be added. This script is compatible with intraday charts and time multipliers up to 60 minutes. It was created by Jeaw and is based on the ideas of the ICT (Institutional Cash Trades) methodology. This script can help traders avoid entering the market during high impact news events and periods of low liquidity. By identifying these potentially volatile times, traders can better manage their risk and improve their overall trading strategy.
Weight Gain 4000 - (Adjustable Volume Weighted MA) - [mutantdog]Short Version:
This is a fairly self-contained system based upon a moving average crossover with several unique features. The most significant of these is the adjustable volume weighting system, allowing for transformations between standard and weighted versions of each included MA. With this feature it is possible to apply partial weighting which can help to improve responsiveness without dramatically altering shape. Included types are SMA, EMA, WMA, RMA, hSMA, DEMA and TEMA. Potentially more will be added in future (check updates below).
In addition there are a selection of alternative 'weighted' inputs, a pair of Bollinger-style deviation bands, a separate price tracker and a bunch of alert presets.
This can be used out-of-the-box or tweaked in multiple ways for unusual results. Default settings are a basic 8/21 EMA cross with partial volume weighting. Dev bands apply to MA2 and are based upon the type and the volume weighting. For standard Bollinger bands use SMA with length 20 and try adding a small amount of volume weighting.
A more detailed breakdown of the functionality follows.
Long Version:
ADJUSTABLE VOLUME WEIGHTING
In principle any moving average should have a volume weighted analogue, the standard VWMA is just an SMA with volume weighting for example. Actually, we can consider the SMA to be a special case where volume is a constant 1 per bar (the value is somewhat arbitrary, the important part is that it's constant). Similar principles apply to the 'elastic' EVWMA which is the volume weighted analogue of an RMA. In any case though, where we have standard and weighted variants it is possible to transform one into the other by gradually increasing or decreasing the weighting, which forms the basis of this system. This is not just a simple multiplier however, that would not work due to the relative proportions being the same when set at any non zero value. In order to create a meaningful transformation we need to use an exponent instead, eg: volume^x , where x is a variable determined in this case by the 'volume' parameter. When x=1, the full volume weighting applies and when x=0, the volume will be reduced to a constant 1. Values in between will result in the respective partial weighting, for example 0.5 will give the square root of the volume.
The obvious question here though is why would you want to do this? To answer that really it is best to actually try it. The advantages that volume weighting can bring to a moving average can sometimes come at the cost of unwanted or erratic behaviour. While it can tend towards much closer price tracking which may be desirable, sometimes it needs moderating especially in markets with lower liquidity. Here the adjustability can be useful, in many cases i have found that adding a small amount of volume weighting to a chosen MA can help to improve its responsiveness without overpowering it. Another possible use case would be to have two instances of the same MA with the same length but different weightings, the extent to which these diverge from each other can be a useful indicator of trend strength. Other uses will become apparent with experimentation and can vary from one market to another.
THE INCLUDED MODES
At the time of publication, there are 7 included moving average types with plans to add more in future. For now here is a brief explainer of what's on offer (continuing to use x as shorthand for the volume parameter), starting with the two most common types.
SMA: As mentioned above this is essentially a standard VWMA, calculated here as sma(source*volume^x,length)/sma(volume^x,length). In this case when x=0 then volume=1 and it reduces to a standard SMA.
RMA: Again mentioned above, this is an EVWMA (where E stands for elastic) with constant weighting. Without going into detail, this method takes the 1/length factor of an RMA and replaces it with volume^x/sum(volume^x,length). In this case again we can see that when x=0 then volume=1 and the original 1/length factor is restored.
EMA: This follows the same principle as the RMA where the standard 2/(length+1) factor is replaced with (2*volume^x)/(sum(volume^x,length)+volume^x). As with an RMA, when x=0 then volume=1 and this reduces back to the standard 2/(length+1).
DEMA: Just a standard Double EMA using the above.
TEMA: Likewise, a standard Triple EMA using the above.
hSMA: This is the same as the SMA except it uses harmonic mean calculations instead of arithmetic. In most cases the differences are negligible however they can become more pronounced when volume weighting is introduced. Furthermore, an argument can be made that harmonic mean calculations are better suited to downtrends or bear markets, in principle at least.
WMA: Probably the most contentious one included. Follows the same basic calculations as for the SMA except uses a WMA instead. Honestly, it makes little sense to combine both linear and volume weighting in this manner, included only for completeness and because it can easily be done. It may be the case that a superior composite could be created with some more complex calculations, in which case i may add that later. For now though this will do.
An additional 'volume filter' option is included, which applies a basic filter to the volume prior to calculation. For types based around the SMA/VWMA system, the volume filter is a WMA-4, for types based around the RMA/EVWMA system the filter is a RMA-2.
As and when i add more they will be listed in the updates at the bottom.
WEIGHTED INPUTS
The ohlc method of source calculations is really a leftover from a time when data was far more limited. Nevertheless it is still the method used in charting and for the most part is sufficient. Often the only important value is 'close' although sometimes 'high' and 'low' can be relevant also. Since we are volume weighting however, it can be useful to incorporate as much information as possible. To that end either 'hlc3' or 'hlcc4' tend to be the best of the defaults (in the case of 24/7 charting like crypto or intraday trading, 'ohlc4' should be avoided as it is effectively the same as a lagging version of 'hlcc4'). There are many other (infinitely many, in fact) possible combinations that can be created, i have included a few here.
The premise is fairly straightforward, by subtracting one value from another, the remaining difference can act as a kind of weight. In a simple case consider 'hl2' as simply the midrange ((high+low)/2), instead of this using 'high+low-open' would give more weight to the value furthest from the open, providing a good estimate of the median. An even better estimate can be achieved by combining that with 'high+low-close' to give the included result 'hl-oc2'. Similarly, 'hlc3' can be considered the basic mean of the three significant values, an included weighted version 'hlc2-o2' combines a sum with subtraction of open to give an estimated mean that may be more accurate. Finally we can apply a similar principle to the close, by subtracting the other values, this one potentially gets more complex so the included 'cc-ohlc4' is really the simplest. The result here is an overbias of the close in relation to the open and the midrange, while in most cases not as useful it can provide an estimate for the next bar assuming that the trend continues.
Of the three i've included, hlc2-o2 is in my opinion the most useful especially in this context, although it is perhaps best considered to be experimental in nature. For that reason, i've kept 'hlcc4' as the default for both MAs.
Additionally included is an 'aux input' which is the standard TV source menu and, where possible, can be set as outputs of other indicators.
THE SYSTEM
This one is fairly obvious and straightforward. It's just a moving average crossover with additional deviation (bollinger) bands. Not a lot to explain here as it should be apparent how it works.
Of the two, MA1 is considered to be the fast and MA2 is considered to be the slow. Both can be set with independent inputs, types and weighting. When MA1 is above, the colour of both is green and when it's below the colour of both is red. An additional gradient based fill is there and can be adjusted along with everything else in the visuals section at the bottom. Default alerts are available for crossover/crossunder conditions along with optional marker plots.
MA2 has the option for deviation bands, these are calculated based upon the MA type used and volume weighted according to the main parameter. In the case of a unweighted SMA being used they will be standard Bollinger bands.
An additional 'source direct' price tracker is included which can be used as the basis for an alert system for price crossings of bands or MAs, while taking advantage of the available weighted inputs. This is displayed as a stepped line on the chart so is also a good way to visualise the differences between input types.
That just about covers it then. The likelihood is that you've used some sort of moving average cross system before and are probably still using one or more. If so, then perhaps the additional functionality here will be of benefit.
Thanks for looking, I welcome any feedack
MTF fair value gap v2 thigh gaps yumwell load in 2 FVG indicators one for current chart then one for MTF of interest.
Higher timeframe FVGs are more important and can be used for bias or even targets for internal liquidity.
big thanks @shanxia for basically re-doing the FVGs into arrays hehehe..
Can now delete mitigated or change mitigated color..
I dont know who uses extensions but if you want to suffer in your private time then go ahead...
pre sure this is the sexiest FVG indicator validate me in the description pls
Relative Volume Force IndexThis indicator can anticipate the market movements. Its posible because it calculates how much force (volume) it's necessary to move the price up or down. If it's necessary a lot of volume to move the price a little it's a reversion signal, but if a little volume could change the price whit elevate volatility, it's signal of reversion too. The indicator plots red if the market is down, and green if it's up, the size and the color of the bars cand demonstrate the movement relative force. Does it by the configurable averages. Not works well whit poor liquidity.
Swing Failure Pattern by EmreKbThe indicator detect to swing failure pattern and shows it.
Swing Failure Pattern (or SFP) is a type of reversal pattern in which (swing) traders target stop-losses above a key swing low or below a key swing high to push the price in the other direction by generating enough liquidity.
