Multi-Timeframe Liquidity Zones V6 (Table)Multi-Timeframe Liquidity Zones V6 (Table) Indicator: Functionality and Uses
Overview: The Multi-Timeframe Liquidity Zones V6 (Table) indicator is a technical analysis tool that highlights key volume-based support and resistance levels across multiple timeframes. It leverages volume profile concepts – specifically the Point of Control (POC) and Value Area High/Low (VAH/VAL) – to identify “liquidity zones” where trading activity was heaviest . Unlike a standard single-timeframe volume profile, this indicator compiles data from several timeframes (e.g. monthly, weekly, daily, intraday) and displays the results in a convenient table format on the chart. The goal is to give traders a consolidated view of important price levels (derived from volume concentrations) across different horizons, helping them plan trades with a broader market perspective.
Purpose and Functionality of the Indicator
Multi-Timeframe Analysis: The primary objective of this indicator is to simplify multi-timeframe analysis of volume distribution. Rather than manually checking volume profiles on separate charts for each timeframe, the tool automatically calculates the key levels for each selected timeframe and presents them together. This includes higher-level perspectives (like monthly or weekly volume hotspots) alongside shorter-term levels (daily or hourly), ensuring that traders don’t miss significant zones from any timeframe . By offering a broader perspective on support and resistance levels, multi-timeframe tools help improve risk management and signal confirmation , and this indicator is designed to provide that volume-based perspective at a glance.
Table Format Display: Multi-Timeframe Liquidity Zones V6 (Table) specifically presents the information as a table (as opposed to plotting lines on the chart). Each row in the table typically corresponds to a timeframe (for example, Monthly, Weekly, Daily, 4H, 1H, 30M, 15M), and the columns list the calculated POC, VAH, VAL, and possibly the average volume for that timeframe’s look-back period. By structuring the data in a table, traders can quickly read off the exact price levels of these liquidity zones without having to visually trace lines. This format makes it easy to compare levels across timeframes or note where multiple timeframes’ levels cluster near the same price – a sign of especially strong support/resistance. The indicator uses a user-defined number of bars or length of history for each timeframe to calculate these values (so you can adjust how far back it looks to define the volume profile for each period).
Objective: In summary, the functionality is geared toward identifying high-liquidity price zones across multiple time scales and presenting them clearly. These high-liquidity zones often coincide with areas where price reacts (stalls, reverses, or accelerates) because a lot of trading activity (hence, orders and volume) took place there in the past. The indicator’s objective is to alert the trader to those areas in advance. It effectively answers questions like: “Where are the major volume concentration levels on the 1-hour, daily, and weekly charts right now?” and “Are there overlapping volume-based support/resistance levels from different timeframes around the current price?” By compiling this information, the indicator helps traders incorporate context from multiple timeframes in their decision-making, without needing to flip through numerous charts.
Identifying Liquidity Zones with POC, VAH, and VAL
Liquidity Zones Defined: In market terms, a “liquidity zone” is an area of the chart where a significant amount of trading occurred, meaning high liquidity (many buyers and sellers exchanged volume there). These zones often act as support or resistance because past heavy trading indicates consensus or interest around those price levels. This indicator identifies liquidity zones through volume profile analysis on each timeframe’s recent price action. Essentially, it looks at the distribution of trading volume at different prices over the specified period and finds the value area – the range of prices that encompassed the majority of that volume (commonly around 70% of the total volume ). Within that value area, it pinpoints the Point of Control (POC), which is the single price level that had the highest traded volume (the peak of the volume profile) . The upper and lower boundaries of that high-volume range are marked as Value Area High (VAH) and Value Area Low (VAL) respectively . Together, the VAH and VAL define the liquidity zone where the market spent most of its time and volume, and POC highlights the most traded price in that zone.
• Point of Control (POC): The POC is the price level with the greatest volume traded for the given period. It represents the price at which the most liquidity was exchanged – effectively the market’s “center of gravity” for that timeframe’s trading activity . The indicator calculates the POC for each selected timeframe by scanning the volume at each price; the price with maximum volume is flagged as that timeframe’s POC. In the table, the POC might be highlighted or listed as a key level (sometimes traders color-code it or mark it for emphasis). Because so many positions were opened or closed at the POC, it often serves as a strong support/resistance. For example, if price falls to a major POC from above, traders expect buyers may step in there (since it was a popular buy/sell level historically), potentially causing a bounce. Conversely, if price breaks through a POC decisively, it may signal a significant shift in market acceptance.
• Value Area High (VAH) and Low (VAL): The VAH and VAL are the price boundaries of the value area, which is typically defined to contain about 70% of the total traded volume for the period . In other words, between VAH and VAL is where the “bulk” of trading occurred, and outside this range is where relatively less volume traded. The indicator derives VAH/VAL by accumulating volume from the highest-volume price (POC) outward until ~70% of volume is covered (this is a common method for volume profile value area). VAH is the top of this high-volume region and VAL is the bottom. These levels are important because they often act like support/resistance boundaries: when price is inside the value area, it’s in a high-liquidity zone and tends to oscillate between VAH and VAL; when price moves above VAH or below VAL, it’s leaving the high-volume zone, which can indicate a potential trend or imbalance (price entering a lower-liquidity area where it might move faster until finding the next liquidity zone). Traders watch VAH/VAL for signs of rejection or acceptance: for instance, a price rally that falters at VAH suggests that level is acting as resistance (sellers defending that high-volume area), whereas if price pushes above VAH, it may continue until the next timeframe’s zone or until it finds new interest. The Multi-Timeframe Liquidity Zones V6 indicator gives the VAH and VAL for each timeframe, essentially mapping out the upper and lower bounds of key liquidity zones at those scales.
How the Indicator Identifies These: Under the hood, the indicator likely uses historical price and volume data for each timeframe’s lookback window. For each timeframe (say the last 20 weekly bars for a weekly profile, last 100 daily bars for a daily profile, etc.), it constructs a volume profile (a histogram of volume at each price). From that distribution, it finds the POC (highest volume bin) and calculates VAH/VAL around it. The output is a set of numbers (price levels) that mark where those zones lie. In practice, if using the Lines version of this indicator, those levels are drawn as horizontal lines on the chart and labeled by timeframe (e.g., a line at 1.2345 labeled “D POC” for Daily POC) . In the Table version, those values are instead listed in text form. Either way, the identification process is the same – it’s finding the high-volume price regions on each timeframe and calling them out. By doing this for multiple timeframes concurrently, the indicator reveals how these liquidity zones from different periods relate to each other. For example, you might discover that a daily-chart value area overlaps with a weekly-chart POC, creating a particularly strong zone of interest. This kind of insight is hard to get from a single timeframe analysis alone.
Volume Profile Data Across Multiple Timeframes
Multiple Timeframes in One View: One of the biggest advantages of this indicator is the ability to see volume profile information from various timeframes side by side. Traders often perform multiple timeframe analysis to get a fuller picture — for instance, checking monthly or weekly levels for long-term context while planning a trade on a 4-hour chart. This indicator automates that process for volume-based levels. The table will typically list each chosen timeframe (which could be preset or user-selected). For each timeframe, you get the POC, VAH, VAL, and possibly an average volume metric. The “average volume” likely refers to the average volume per bar or the average volume traded over the profile’s duration for that timeframe, which gives a sense of how significant that period’s activity is. For example, a weekly profile might show an average volume of say 500k per week, versus a daily profile average of 80k per day – indicating the scale of trading on weekly vs daily. High average volume on a timeframe means its liquidity zones were formed with a lot of participation, possibly making them more reliable support/resistance. By comparing these, traders can gauge which timeframes had unusually high or low activity recently. The table format makes such comparisons straightforward.
Identification of Confluence: Because all the data is presented together, traders can quickly spot confluence or overlaps between timeframes. If two different timeframes show liquidity zones at similar price levels, that price becomes extremely noteworthy. For instance, suppose the indicator shows: a 1-hour POC at 1.1300, a 4-hour VAL at 1.1280, and a daily VAL at 1.1290. These are all in a tight range – effectively indicating a multi-timeframe liquidity zone around 1.1280–1.1300. A trader seeing this cluster in the table will recognize that as a strong support area, since multiple profiles from intraday to daily all suggest heavy trading interest there. Similarly, overlaps of VAH (resistance zone) from different timeframes could signal a strong ceiling. The multi-timeframe view prevents a trader from, say, going long into a major weekly POC above, or shorting when there’s a huge monthly value-area low just below – situations where awareness of higher timeframe volume structure can make the difference between a good and bad trade.
User Customization: The indicator is flexible in that you can typically adjust which timeframes to include and how many bars to use for each timeframe’s calculation. For example, one might configure it to calculate monthly levels using the past 12 monthly bars (1 year of data), weekly levels using the past 20 weeks, daily using 100 days, etc., depending on preference. By tuning the “bars count” or period length , the trader can focus on recent liquidity zones or incorporate more history if desired. Shorter lookback might catch more recent shifts in volume distribution (important if the market structure changed recently), while longer lookback gives more established levels. This customization ensures the indicator’s output can be tailored to different trading styles (short-term vs swing vs long-term investing). Regardless of settings, the multi-timeframe table allows simultaneous visibility of the chosen timeframes’ volume landscape. This comprehensive view is the core strength: it consolidates data that normally requires flipping through multiple charts.
Using the Liquidity Zones Data for Trading Decisions
Traders can use the information from the MTF Liquidity Zones V6 (Table) indicator in several practical ways to enhance their decision-making:
• Identify Support and Resistance: Each liquidity zone acts as a potential support or resistance area. For example, if the table shows a daily VAH at a certain level above the current price, that level might serve as resistance if the price rallies up to it (since it marks the top of a high-volume region where sellers might step in). Conversely, a weekly VAL below current price could act as support on a dip. By noting these levels in the table, a trader planning an entry or exit can anticipate where the price might stall or reverse. Essentially, you get a map of high-interest price levels from different timeframes, which you can mark on your trading chart for guidance.
• Plan Entries and Exits Around Key Levels: Many traders incorporate volume profile levels into their strategies, for instance: buying near VAL (betting that the value area will hold and price will revert upward), or selling/shorting near VAH (expecting the top of value to hold as resistance), or trading breakouts when price moves outside the value area. With the multi-timeframe table, one can refine these tactics by also considering higher timeframe levels. Suppose you see that on the 1-hour chart the price is just above its 1H POC, but the table indicates that just slightly above, there’s also the daily POC. You might delay a long entry until price clears that daily POC, because that could be a stronger intraday barrier. Or if you intend to take profit on a long trade, you might choose a target just below a weekly VAH since price may struggle to climb past that on the first attempt. The indicator thus acts as a guide for precision in entry/exit decisions, aligning them with where liquidity is high.
• Gauge Trend Strength and Directional Bias: By observing where current price is relative to these volume zones, traders can infer certain market conditions. For instance, if price is trading above the VAH of multiple timeframes’ value areas, it suggests the market is in a more bullish or overextended territory (price accepted above prior value), whereas if price is below multiple VALs, it’s in bearish or undervalued territory relative to recent history. If the price stays around a POC, it indicates consolidation or equilibrium (market comfortable at that price). Traders can use this context for bias – e.g., if price is above the weekly VAH, you might lean bullish but watch for potential pullbacks to that VAH level (now a support). If price is below the monthly VAL, you might avoid longs until it re-enters that value area. In essence, the liquidity zones provide context of value vs. price: is price trading within the high-volume areas (implying range-bound behavior) or outside them (implying a breakout or trending move)? This can prevent chasing trades at poor locations.
• Combine with Other Indicators/Analysis: It’s generally advised to not use any single indicator in isolation, and this holds true here. The liquidity zones from this indicator are best used alongside price action or other technical signals for confirmation . For example, if a bullish candlestick reversal pattern forms right at a confluence of a 4H VAL and Daily POC, that’s a stronger buy signal than the pattern alone. Or if an oscillator shows overbought exactly as price hits a weekly VAH, it adds conviction to a possible short. The indicator’s table basically gives you a shortlist of critical price levels; you can then watch how price behaves at those levels (via candlesticks, order flow, etc.) to make the final trade decision. Traders might set alerts for when price approaches one of the listed levels, or they might drop down to a lower timeframe to fine-tune an entry once a key zone is reached. By integrating this volume-based insight with trend analysis, chart patterns, or momentum indicators, one can make more informed and high-probability decisions rather than trading in the dark.
• Risk Management and Stop Placement: High-liquidity zones can also inform stop-loss placement. Ideally, you want your stop on the other side of a strong support/resistance. If you go long near a VAL, you might place your stop just below the VAL (since a move beyond that suggests the high-volume zone didn’t hold). If you short near a VAH, a stop just above the VAH or POC could be logical. Moreover, if multiple timeframes show overlapping zones, a stop beyond all of them could be even safer (albeit at the cost of a wider stop). The indicator helps identify those spots. It also warns you of where not to put a stop – for example, placing a stop-loss right at a POC might be unwise because price could gravitate to that POC repeatedly (due to its magnetic effect as a high-volume price). Instead, a trader might choose a stop beyond the far side of the value area. By using the table’s information, you can align your risk management with areas of high liquidity, reducing the chance of being whipsawed by normal volatility around heavily traded levels .
Benefits of the Multi-Timeframe Liquidity Zones Indicator
Using the Multi-Timeframe Liquidity Zones V6 (Table) indicator offers several key benefits for traders, ultimately aiming to streamline analysis and improve decision quality:
• Consolidated Key Levels: It provides a clear, consolidated view of crucial volume-driven levels from multiple timeframes all at once . This saves time and ensures you always account for major support/resistance zones that come from higher or lower timeframe volume clusters. You won’t accidentally overlook a significant weekly level while focused on a 15-minute chart, for example.
• Enhanced Multi-Timeframe Insight: By aligning information from long-term and short-term periods, the indicator helps traders see the “bigger picture” while still operating on their preferred timeframe. This multi-scale awareness can improve trade timing and confidence. You’re effectively doing multi-timeframe analysis with volume profiles in an efficient manner, which can confirm or caution your trade ideas (e.g., a trend looks strong on the 1H, but the table shows a huge monthly VAH just overhead – a reason to be cautious or take profit early).
• Improved Decision Making and Precision: Knowing where liquidity zones lie allows for more precise entries, exits, and stop placements. Traders can make informed decisions such as waiting for a pullback to a value area before entering, or taking profits before price hits a major POC from a higher timeframe. These decisions are grounded in objectively important price levels, potentially leading to higher probability trades and better risk-reward setups. It essentially enhances your strategy by adding a layer of volume context – you’re trading with an awareness of where the market’s interest is heaviest.
• Volume-Based Confirmation: Price alone can sometimes be deceptive, but volume tells the true story of participation. The liquidity zones indicator provides volume-based confirmation of support/resistance. If a price level is identified by this tool, it’s because significant volume happened there – adding weight to that level’s importance. This can help filter out false support/resistance levels that aren’t backed by volume. In other words, it highlights high-quality levels that many traders (and possibly institutions) have shown interest in.
• Adaptable to Different Trading Styles: Whether one is a scalper looking at intraday (15M, 5M charts) or a swing trader focusing on daily/weekly, the indicator can be configured to those needs. You choose which timeframes and how much data to consider. This means the concept of liquidity zones can be applied universally – from spotting intraday pivot levels with volume, to seeing long-term value zones on an investment. The consistent methodology of POC/VAH/VAL across scales provides a common framework to analyze any market and timeframe.
• Informed Risk Management: As discussed, the knowledge of multi-timeframe volume zones aids in risk management. By placing stops beyond major liquidity areas or avoiding trades that run into strong volume walls, traders can reduce the likelihood of whipsaw losses. It’s an extra layer of defense to ensure your trade plan accounts for where the market has historically found lots of interest (hence likely friction). This level of informed planning can be the difference between a well-managed trade and an avoidable loss.
In conclusion, the Multi-Timeframe Liquidity Zones V6 (Table) indicator serves as a powerful analytical aid, giving traders a structured view of where price is likely to encounter support or resistance based on volume concentrations across timeframes. Its functionality centers on identifying those liquidity zones (via POC, VAH, VAL) and presenting them in an easy-to-read format, while its ultimate purpose is to help traders make more informed decisions. By integrating this tool into their workflow, traders can more confidently navigate price action, knowing the objective volume-based landmarks that lie ahead. Remember that while these volume levels often coincide with strong S/R zones, it’s best to use them in conjunction with other technical or fundamental analysis for confirmation . When used appropriately, the indicator can streamline multi-timeframe analysis and enhance your overall trading strategy , giving you an edge in identifying where the market’s liquidity (and opportunity) resides.
在腳本中搜尋"high low"
Advanced Session Profile Predictor with SR Boxes & ORAdvanced Session Profile Predictor with Momentum Arrows
Designed for intraday traders, this indicator analyzes price action across Asia, London, and New York sessions to predict market profiles and highlight key trading opportunities. By combining session-based profiling, Opening Range (OR) visualization, and momentum signals from Traders Dynamic Index (TDI), it offers a unique tool for anticipating trends, reversals, and breakouts. Ideal for forex, indices, and crypto on 15M–1H charts.
What Makes This Indicator Unique?
Unlike typical session indicators that only mark time zones or standard TDI scripts that focus on momentum, this tool:
Predicts market profiles (e.g., "Trend Continuation," "NY Manipulation") by analyzing session ranges and directional moves, offering actionable insights into how sessions interact.
Visualizes Opening Range (OR) boxes for the first 15 minutes of each session, helping traders spot early breakout levels.
Integrates TDI with momentum to generate precise bullish/bearish arrows, filtered by session context for improved reliability.
Simplifies decision-making with dynamic profile labels showing real-time long/short conditions based on price levels.
How Does It Work?
Session Tracking:
Asia (00:00–08:00 UTC, yellow), London (08:00–16:00 UTC, red), and New York (13:00–21:00 UTC, blue) sessions are highlighted with background colors and high/low lines (crosses).
OR boxes (first 15 minutes) are drawn for each session: yellow for Asia, red for London, blue for NY.
Profile Prediction:
Compares Asia and London session ranges and directions (e.g., trending if range > 1.5x 5-period SMA).
Examples:
Trend Continuation: Asia and London trend in the same direction—long above Asia high (uptrend) or short below Asia low (downtrend).
NY Manipulation: Asia trends, London consolidates—watch for NY breakouts at London high/low.
Displays the predicted profile and entry conditions in labels (e.g., "IF price hits 1.2000 LONG").
Momentum Arrows:
Uses TDI (RSI period 21, bands 34, fast MA 2) and 12-period momentum.
Green up arrow: Fast MA > upper band (>68) and momentum rising (bullish).
Red down arrow: Fast MA < lower band (<32) and momentum falling (bearish).
Support/Resistance (SR):
Plots dynamic SR boxes based on pivot highs/lows, filtered by volume (inspired by ChartPrime’s methodology, credited below).
How to Use It
Setup: Apply to a 15M–1H chart. Adjust time zone (default: UTC) and session times if needed. Customize TDI/momentum settings for sensitivity.
Trading:
Check the top-right labels for the current profile and entry conditions (e.g., "IF price hits LONG/SHORT").
Confirm entries with green up arrows (bullish) or red down arrows (bearish).
Use OR boxes and session high/low lines to identify breakout or reversal levels.
Example: In "NY Manipulation," wait for price to hit London high (long) or low (short) during NY session, confirmed by an arrow.
Best Markets: Forex (EUR/USD), indices (SPX500), crypto (BTC/USD) with sufficient intraday volatility.
Underlying Concepts
Session Profiling: Detects trends (range > SMA * threshold) and manipulation (e.g., London breaking Asia’s high/low) to predict NY behavior.
OR Boxes: Marks the first 15 minutes’ high/low as a breakout zone (time-based, 900,000 ms).
TDI + Momentum: Combines RSI-based bands with price change (close – close ) for momentum signals.
SR Boxes: Identifies pivots over a lookback period (default 20), scaled by ATR and filtered by volume thresholds.
Credits
The SR box logic is inspired by ChartPrime’s volume-filtered support/resistance methodology, adapted with custom breakout/hold detection. Original authors are credited for their foundational work.
Chart Setup
Displays session backgrounds, OR boxes, high/low lines, TDI arrows, and profile labels. Keep other indicators off for clarity.
Double Top/Bottom Fractals DetectorDouble Top/Bottom Detector with Williams Fractals (Extended + Early Signal)
This indicator combines the classic Williams Fractals methodology with an enhanced mechanism to detect potential reversal patterns—namely, double tops and double bottoms. It does so by using two separate detection schemes:
Confirmed Fractals for Pattern Formation:
The indicator calculates confirmed fractals using the traditional Williams Fractals rules. A fractal is confirmed if a bar’s high (for an up fractal) or low (for a down fractal) is the highest or lowest compared to a specified number of bars on both sides (default: 2 bars on the left and 2 on the right).
Once a confirmed fractal is identified, its price (high for tops, low for bottoms) and bar index are stored in an internal array (up to the 10 most recent confirmed fractals).
When a new confirmed fractal appears, the indicator compares it with previous confirmed fractals. If the new fractal is within a user-defined maximum bar distance (e.g., 20 bars) and the price difference is within a specified tolerance (default: 0.8%), the indicator assumes that a double top (if comparing highs) or a double bottom (if comparing lows) pattern is forming.
A signal is then generated by placing a label on the chart—SELL for a double top and BUY for a double bottom.
Early Signal Generation:
To capture potential reversals sooner, the indicator also includes an “early signal” mechanism. This uses asymmetric offsets different from the confirmed fractal calculation:
Signal Right Offset: Defines the candidate bar used for early signal detection (default is 1 bar).
Signal Left Offset: Defines the number of bars to the left of the candidate that must confirm the candidate’s price is the extreme (default is 2 bars).
For an early top candidate, the candidate bar’s high must be greater than the highs of the bars specified by the left offset and also higher than the bar immediately to its right. For an early bottom candidate, the corresponding condition applies for lows.
If the early candidate’s price level is within the acceptable tolerance when compared to any of the previously stored confirmed fractals (again, within the allowed bar distance), an early signal is generated—displayed as SELL_EARLY or BUY_EARLY.
The early signal block can be enabled or disabled via a checkbox input, allowing traders to choose whether to use these proactive signals.
Key Parameters:
n:
The number of bars used to confirm a fractal. The fractal is considered valid if the bar’s high (or low) is higher (or lower) than the highs (or lows) of the preceding and following n bars.
maxBarsApart:
The maximum number of bars allowed between two fractals for them to be considered part of the same double top or bottom pattern.
tolerancePercent:
The maximum allowed percentage difference (default: 0.8%) between the high (or low) values of two fractals to qualify them as matching for the pattern.
signalLeftOffset & signalRightOffset:
These parameters define the asymmetric offsets for early signal detection. The left offset (default: 2) specifies how many bars to look back, while the right offset (default: 1) specifies the candidate bar’s position.
earlySignalsEnabled:
A checkbox option that allows users to enable or disable early signal generation. When disabled, the indicator only uses confirmed fractal signals.
How It Works:
Fractal Calculation and Plotting:
The confirmed fractals are calculated using the traditional method, ensuring robust identification by verifying the pattern with a symmetrical offset. These confirmed fractals are plotted on the chart using triangle shapes (upwards for potential double bottoms and downwards for potential double tops).
Pattern Detection:
Upon detection of a new confirmed fractal, the indicator checks up to 10 previous fractals stored in internal arrays. If the new fractal’s high or low is within the tolerance range and close enough in terms of bars to one of the stored fractals, it signifies the formation of a double top or double bottom. A corresponding SELL or BUY label is then placed on the chart.
Early Signal Feature:
If enabled, the early signal block checks for candidate bars based on the defined asymmetric offsets. These candidates are evaluated to see if their high/low levels meet the early confirmation criteria relative to nearby bars. If they also match one of the confirmed fractal levels (within tolerance and bar distance), an early signal is issued with a label (SELL_EARLY or BUY_EARLY) on the chart.
Benefits for Traders:
Timely Alerts:
By combining both confirmed and early signals, the indicator offers a proactive approach to detect reversals sooner, potentially improving entry and exit timing.
Flexibility:
With adjustable parameters (including the option to disable early signals), traders can fine-tune the indicator to better suit different markets, timeframes, and trading styles.
Enhanced Pattern Recognition:
The dual-layered approach (confirmed fractals plus early detection) helps filter out false signals and captures the essential formation of double tops and bottoms more reliably.
AstroTrading_DragonCombine1. Table Setup and User Inputs
Table Position and Font Size:
The script begins by asking the user to select a table position (e.g. Top Right) and a font size (Small, Medium, Large, Huge) via input options.
pinescript
Kopyala
positionInput = input.string("Sağ Üst Köşe", title="Tablo Konumu", options= )
fontSizeInput = input.string("Orta", title="Yazı Punto Büyüklüğü", options= )
Table Creation:
A table is created using table.new with 6 rows and 4 columns. The location of the table is determined by the selected input. This table will later display the name, entry, target, and stop levels for each of the five strategies.
2. Variable Declarations
The script defines several persistent variables to store levels for each indicator. These include:
Entry, target, and stop levels for each of the five sub-indicators (labeled as _1, _2, _3, _4, and _5).
Examples include targetLevel_1, fibLow_1, lastEntry_1, lastTarget_1, etc.
3. Indicator 1 – AstroTrading_AlphaBalance
Logic:
This part examines the previous candle’s high and low to compute its range. It then defines two conditions:
conditionUp_1: When the current close exceeds the previous high by at least 50% of the previous range.
conditionDown_1: When the current close falls below the previous low by 50% of the previous range.
Action:
Depending on whether the move is upward or downward, the script sets:
For an upward move:
fibLow_1 is set to the current low.
The entry level is taken as the current high.
The target is computed by taking the high and subtracting –0.786 times the range (this negative multiplier inverts the move).
The stop is set at the previous low.
For a downward move, similar logic applies with reversed roles.
Purpose:
This module generates a primary signal (AlphaBalance) based on extreme candle movements relative to the prior candle’s range.
4. Indicator 2 – AstroTrading_CandleElongation
Higher Timeframe Data:
The script uses the request.security function to obtain high, low, close, and open values from a user-specified timeframe.
Fibonacci Extension Calculation:
A function fiboExtension calculates two Fibonacci extension levels (approximately 0.786 and 1.618 multipliers) based on three price points.
Signal Conditions:
It checks if the previous candle (two bars ago) meets certain criteria relative to its open, and if the current candle’s close confirms an elongation move.
Output:
If conditions are met, the script sets:
candleEntry_2 to the lower Fibonacci level,
candleTarget_2 to the higher Fibonacci extension,
candleStop_2 to the current low (for a bullish setup) or high (for bearish).
Purpose:
This sub-indicator looks to capture significant candle elongation moves by using Fibonacci extension levels to define entry, target, and stop.
5. Indicator 3 – AstroTrading_FlaGama
Similar to a Flag Formation:
Like the previous “FlaGama” indicator, it checks if the current close is more than 50% beyond the previous candle’s high (conditionUp_3) or below the previous low (conditionDown_3).
Bar Coloring:
If either condition is met, the bar is colored orange to signal an extreme move.
Signal Generation:
Depending on the move’s direction:
Bullish Setup:
Calculates a Fibonacci level at 78.6% from the current low to high.
Sets the entry at this Fibonacci level.
The target is computed by adding the difference between the current high and the Fibonacci level to the current high.
The stop is set at the current low.
Bearish Setup:
Mirrors the Fibonacci calculation to derive a level for short entry.
The target is set below the current low, and the stop is at the current high.
Purpose:
The FlaGama section provides confirmation signals when extreme moves occur, helping traders decide on potential reversals.
6. Indicator 4 – AstroTrading_HermDown
EMA Crossover:
An EMA (111-period) is calculated. A crossover of the EMA above the close triggers a “kesilme” (cutoff) event.
First Candle Identification:
Once a crossover is detected, the next candle’s close is monitored. If that candle’s close remains below the cutoff level, it is considered the “first candle” of the HermDown setup.
Fibonacci Retracement:
It then calculates the highest high over the last 30 bars and derives a target level (fibNeg0618_4) at about 48.6% retracement from that high.
Signal Levels:
The entry is the cutoff close, the target is the calculated Fibonacci level, and the stop is the low of the cutoff candle.
Purpose:
This module aims to capture bearish reversals (HermDown) when the price drops sharply below an EMA, using Fibonacci retracement as a guide.
7. Indicator 5 – AstroTrading_HermUp
EMA Crossunder:
Similarly, an EMA (111-period) is used. A crossunder (EMA crossing below the close) signals a potential bullish reversal.
First Candle Confirmation:
The next candle’s close is checked to confirm the move.
Fibonacci Level:
A Fibonacci extension (approximately 61.8% of the distance from the cutoff close to the high) is computed to serve as the target.
Signal Levels:
The entry is set at the cutoff close, the target is the Fibonacci level, and the stop is set at the low.
Purpose:
This section captures bullish reversal signals (HermUp) when the price moves above an EMA.
8. Displaying Levels in a Table
Aggregating Data:
The script gathers the entry, target, and stop levels from all five sub-indicators.
Table Layout:
The table displays five rows (one for each indicator) with four columns:
Indicator name (e.g., “AlphaBalance”, “CandleElongation”, “FlaGama”, “HermDown”, “HermUp”)
Entry level
Target level
Stop level
Color Coding:
Entry cells have a blue background.
Target cells are colored green if above the current close or red if below.
Stop cells are given a gray background.
Purpose:
This consolidated view allows traders to quickly assess all key levels from different strategies on the chart.
Summary
The “AstroTrading_DragonCombine” indicator is a multi-faceted tool that merges five distinct trading setups into one comprehensive display. Each sub-indicator utilizes a unique method—ranging from extreme candle moves and Fibonacci extensions to EMA crossovers—to determine entry, target, and stop levels. These levels are then neatly summarized in a table overlay on the chart. By combining these approaches, traders can gain a broader perspective on market conditions and potential reversal points, enhancing their decision-making process while adhering to sound risk management principles.
This explanation is written to meet TradingView’s script publication standards, providing a clear, objective, and detailed overview of the indicator’s functionality and logic.
SSL Channel MTFSSL Channel with MTF support, This eliminates the noise of a basic SSL Channel script which is based on ErwinBeckers SSL Channel. So i have used a Multi Time Frame approach to have a clear confirmation of trend and reduce Noise and False signals unlike basic SSL Channel.
This script can be used to determine.
Support/Resistance
High/Low Breakout
Trend Direction
MA candles for Entry
The high and low sma are plotted as SSL CHANNEL when ever the high and low sma cross each other a direction change is observed.
The direction of SSL channel determines the trend of the price. The length of the channel can be changed as required a low value has a high noise and direction can be determined with low accuracy. Increasing the length of SSL channel has high accuracy trend confirmation.
The MTF SSL Channel uses plot from higher timeframe this helps in using SSL Channel as a Price Action Tool. Price when ever crosses over or below the channel determines a breakout. Price tries to move between the High SMA line and Low SMA Line of the SSL Channel rejection, breakouts can be easily observed on a lower timeframe using SSL Channel Plot from a higher timeframe.
I have used 5min/15min chart with MTF SSL from a 1Hr/4Hr and a length of 5 instead of 10. This helps quick direction changes over a period of 1hr to 4hr. Price is trapped within the High SMA and Low SMA lines of SSL Channel. In addition to SSL High Low and average mid line is plotted to additional reference.
Buy Sell Signals are plotted based on crossover of SMA High and Low.
Candle are Plotted Using a SMA with length of 5. This Candle Plot can be used to make an entry based on direction confirmation of SSL. keep in mind the direction of SSL Plot and the candle must be same. Preferably Entry can made above or below the midline of SSL Channel. The Candle Plot eliminates the Noise of traditional Japanese Candlesticks.
Additionally MACD Crossover and MACD Trend line confirmations can be used to confirm a Buy Sell and Entry signals
Alerts are also plotted accordingly.
6 Band Parametric EQThis indicator implements a complete parametric equalizer on any data source using high-pass and low-pass filters, high and low shelving filters, and six fully configurable bell filters. Each filter stage features standard audio DSP controls including frequency, Q factor, and gain where applicable. While parametric EQ is typically used for audio processing, this implementation raises questions about the nature of filtering in technical analysis. Why stop at simple moving averages when you can shape your signal's frequency response with surgical precision? The answer may reveal more about our assumptions than our indicators.
Filter Types and Parameters
High-Pass Filter:
A high-pass filter attenuates frequency components below its cutoff frequency while passing higher frequencies. The Q parameter controls resonance at the cutoff point, with higher values creating more pronounced peaks.
Low-Pass Filter:
The low-pass filter does the opposite - it attenuates frequencies above the cutoff while passing lower frequencies. Like the high-pass, its Q parameter affects the resonance at the cutoff frequency.
High/Low Shelf Filters:
Shelf filters boost or cut all frequencies above (high shelf) or below (low shelf) the target frequency. The slope parameter determines the steepness of the transition around the target frequency , with a value of 1.0 creating a gentle slope and lower values making the transition more abrupt. The gain parameter sets the amount of boost or cut in decibels.
Bell Filters:
Bell (or peaking) filters create a boost or cut centered around a specific frequency. A bell filter's frequency parameter determines the center point of the effect, while Q controls the width of the affected frequency range - higher Q values create a narrower bandwidth. The gain parameter defines the amount of boost or cut in decibels.
All filters run in series, processing the signal in this order: high-pass → low shelf → bell filters → high shelf → low-pass. Each stage can be independently enabled or bypassed.
The frequency parameter for all filters represents the period length of the targeted frequency component. Lower values target higher frequencies and vice versa. All gain values are in decibels, where positive values boost and negative values cut.
The 6-Band Parametric EQ combines these filters into a comprehensive frequency shaping tool. Just as audio engineers use parametric EQs to sculpt sound, this indicator lets you shape market data's frequency components with surgical precision. But beyond its technical implementation, this indicator serves as a thought experiment about the nature of filtering in technical analysis. While traditional indicators often rely on simple moving averages or single-frequency filters, the parametric EQ takes this concept to its logical extreme - offering complete control over the frequency domain of price action. Whether this level of filtering precision is useful for analysis is perhaps less important than what it reveals about our assumptions regarding market data and its frequency components.
Asia Sessions AutoPlotting**Asia Sessions AutoPlotting**
This script is designed to automatically detect and plot the Asia session high and low levels directly on your chart, providing key session data for trading analysis. It is highly customizable, making it an essential tool for traders who rely on session data for decision-making.
### Key Features:
- **Asia Session Detection**: Automatically identifies the Asia session based on user-defined time settings (default: 0000-0845 UTC).
- **High/Low Line Plotting**: Displays high and low price levels for the session with customizable colors and line styles.
- **Line Extensions**: Option to extend session high/low lines for future price action reference.
- **Session Background Fill**: Adds an optional colored background to highlight the Asia session period.
- **Day Labels**: Includes labels for the session high/low levels with the corresponding day of the week.
- **Dynamic Session History**: Limits the display to a user-specified number of past sessions (default: 7) to keep the chart clean and focused.
- **Customizable Colors**: Highlights Mondays with unique colors for easy identification, while other weekdays use a different scheme.
### Use Cases:
- Identify key session levels for trading strategies.
- Monitor Asia session dynamics and their impact on subsequent sessions.
- Spot significant price reactions around session highs/lows.
### Inputs:
- **Session Time**: Adjust the session time to match your preferred Asia trading hours.
- **Toggle High/Low Lines**: Enable or disable the plotting of session highs and lows.
- **Line Extensions**: Extend the session high/low lines into future bars for better visualization.
- **Background Highlight**: Toggle a colored background for the Asia session.
- **Maximum Sessions**: Define how many past sessions to display for clarity.
This script is perfect for intraday traders, scalpers, and swing traders looking to gain insight into the Asia session and its influence on global markets. Fully adjustable and easy to use, it enhances your chart with critical information at a glance.
Simply add it to your TradingView chart, configure your settings, and let it do the work for you!
Katalyst's Opening Range BreakoutKatalyst's Opening Range Breakout + No Trade Zone
📜 Overview:
This indicator allows traders to visualize the high and low of the opening range for a user-selected timeframe (e.g., 30s, 1m, 5m, 15m). It features fully customizable lines, labels, and an optional **No Trade Zone** fill to help you identify breakout levels with ease.
---
🎯 Key Features:
1. **Customizable Opening Range**:
- Select your preferred opening range duration: **30 seconds, 1 minute, 2 minutes, 5 minutes, 10 minutes, or 15 minutes**.
- The indicator calculates and plots the **high** and **low** of the selected opening range.
2. **Dynamic Line Styling**:
- Choose the **line color**, **transparency**, and **style**: **Solid, Dashed, or Dotted**.
- Lines extend to the right of the chart for clarity.
3. **No Trade Zone** *(Optional / Disabled by default)*:
- When enabled, fills the area between the high and low lines with a customizable **color and transparency**.
- Helps visually identify consolidation areas where trading might be avoided.
4. **Labels for Precision**:
- Clearly displays the **Opening Range High** and **Low** values.
- Labels are color-coded and positioned dynamically for easy interpretation.
5. **Clean and Efficient Updates**:
- The indicator deletes old lines, labels, and fills before creating new ones, ensuring a clutter-free chart.
---
⚙️ How to Use:
1. **Select Your Timeframe**:
- From the settings, choose your desired opening range duration: 30s, 1m, 2m, 5m, 10m, or 15m.
2. **Customize the Visuals**:
- Adjust line color, style, and transparency.
- Enable the **No Trade Zone** for a transparent background fill between the high and low lines.
3. **Interpret the Breakout**:
- Watch for price movements above or below the **opening range** to identify potential breakout opportunities.
---
🛠 Settings:
Opening Range Duration: Select the timeframe for the opening range (30s, 1m, 2m, 5m, 10m, 15m).
Line Color: Set the color of the range lines.
Line Transparency: Adjust the transparency of the lines (0 = solid, 100 = invisible).
Line Style: Choose line style: Solid, Dashed, or Dotted.
Label Colors: Customize the label colors for the high and low values.
Enable No Trade Zone: Fill the area between high and low lines with a transparent color.
No Trade Zone Color: Set the fill color for the no trade zone.
No Trade Zone Transparency: Adjust the transparency of the no trade zone fill.
---
📈 Ideal For
Day traders and scalpers looking to trade **breakouts**.
Traders who want to identify areas of consolidation visually.
Anyone who relies on the **opening range** for their trading strategy.
---
🔍 Example Usage:
Set the opening range to **5 minutes** and enable the **No Trade Zone** with a light red fill.
Watch for price to break above or below the high/low lines to signal potential trade opportunities.
---
✨ Why Use This Indicator?
This script simplifies your breakout strategy by providing a clear, visually appealing representation of the opening range. The flexible customization options and the optional **No Trade Zone** make it a powerful tool for identifying high-probability trades.
---
Let me know if you need any additional tweaks or clarifications for this description. It's all set to help traders understand and use your powerful script! 🚀📈
Ensemble Alerts█ OVERVIEW
This indicator creates highly customizable alert conditions and messages by combining several technical conditions into groups , which users can specify directly from the "Settings/Inputs" tab. It offers a flexible framework for building and testing complex alert conditions without requiring code modifications for each adjustment.
█ CONCEPTS
Ensemble analysis
Ensemble analysis is a form of data analysis that combines several "weaker" models to produce a potentially more robust model. In a trading context, one of the most prevalent forms of ensemble analysis is the aggregation (grouping) of several indicators to derive market insights and reinforce trading decisions. With this analysis, traders typically inspect multiple indicators, signaling trade actions when specific conditions or groups of conditions align.
Simplifying ensemble creation
Combining indicators into one or more ensembles can be challenging, especially for users without programming knowledge. It usually involves writing custom scripts to aggregate the indicators and trigger trading alerts based on the confluence of specific conditions. Making such scripts customizable via inputs poses an additional challenge, as it often involves complicated input menus and conditional logic.
This indicator addresses these challenges by providing a simple, flexible input menu where users can easily define alert criteria by listing groups of conditions from various technical indicators in simple text boxes . With this script, you can create complex alert conditions intuitively from the "Settings/Inputs" tab without ever writing or modifying a single line of code. This framework makes advanced alert setups more accessible to non-coders. Additionally, it can help Pine programmers save time and effort when testing various condition combinations.
█ FEATURES
Configurable alert direction
The "Direction" dropdown at the top of the "Settings/Inputs" tab specifies the allowed direction for the alert conditions. There are four possible options:
• Up only : The indicator only evaluates upward conditions.
• Down only : The indicator only evaluates downward conditions.
• Up and down (default): The indicator evaluates upward and downward conditions, creating alert triggers for both.
• Alternating : The indicator prevents alert triggers for consecutive conditions in the same direction. An upward condition must be the first occurrence after a downward condition to trigger an alert, and vice versa for downward conditions.
Flexible condition groups
This script features six text inputs where users can define distinct condition groups (ensembles) for their alerts. An alert trigger occurs if all the conditions in at least one group occur.
Each input accepts a comma-separated list of numbers with optional spaces (e.g., "1, 4, 8"). Each listed number, from 1 to 35, corresponds to a specific individual condition. Below are the conditions that the numbers represent:
1 — RSI above/below threshold
2 — RSI below/above threshold
3 — Stoch above/below threshold
4 — Stoch below/above threshold
5 — Stoch K over/under D
6 — Stoch K under/over D
7 — AO above/below threshold
8 — AO below/above threshold
9 — AO rising/falling
10 — AO falling/rising
11 — Supertrend up/down
12 — Supertrend down/up
13 — Close above/below MA
14 — Close below/above MA
15 — Close above/below open
16 — Close below/above open
17 — Close increase/decrease
18 — Close decrease/increase
19 — Close near Donchian top/bottom (Close > (Mid + HH) / 2)
20 — Close near Donchian bottom/top (Close < (Mid + LL) / 2)
21 — New Donchian high/low
22 — New Donchian low/high
23 — Rising volume
24 — Falling volume
25 — Volume above average (Volume > SMA(Volume, 20))
26 — Volume below average (Volume < SMA(Volume, 20))
27 — High body to range ratio (Abs(Close - Open) / (High - Low) > 0.5)
28 — Low body to range ratio (Abs(Close - Open) / (High - Low) < 0.5)
29 — High relative volatility (ATR(7) > ATR(40))
30 — Low relative volatility (ATR(7) < ATR(40))
31 — External condition 1
32 — External condition 2
33 — External condition 3
34 — External condition 4
35 — External condition 5
These constituent conditions fall into three distinct categories:
• Directional pairs : The numbers 1-22 correspond to pairs of opposing upward and downward conditions. For example, if one of the inputs includes "1" in the comma-separated list, that group uses the "RSI above/below threshold" condition pair. In this case, the RSI must be above a high threshold for the group to trigger an upward alert, and the RSI must be below a defined low threshold to trigger a downward alert.
• Non-directional filters : The numbers 23-30 correspond to conditions that do not represent directional information. These conditions act as filters for both upward and downward alerts. Traders often use non-directional conditions to refine trending or mean reversion signals. For instance, if one of the input lists includes "30", that group uses the "Low relative volatility" condition. The group can trigger an upward or downward alert only if the 7-period Average True Range (ATR) is below the 40-period ATR.
• External conditions : The numbers 31-35 correspond to external conditions based on the plots from other indicators on the chart. To set these conditions, use the source inputs in the "External conditions" section near the bottom of the "Settings/Inputs" tab. The external value can represent an upward, downward, or non-directional condition based on the following logic:
▫ Any value above 0 represents an upward condition.
▫ Any value below 0 represents a downward condition.
▫ If the checkbox next to the source input is selected, the condition becomes non-directional . Any group that uses the condition can trigger upward or downward alerts only if the source value is not 0.
To learn more about using plotted values from other indicators, see this article in our Help Center and the Source input section of our Pine Script™ User Manual.
Group markers
Each comma-separated list represents a distinct group , where all the listed conditions must occur to trigger an alert. This script assigns preset markers (names) to each condition group to make the active ensembles easily identifiable in the generated alert messages and labels. The markers assigned to each group use the format "M", where "M" is short for "Marker" and "x" is the group number. The titles of the inputs at the top of the "Settings/Inputs" tab show these markers for convenience.
For upward conditions, the labels and alert messages show group markers with upward triangles (e.g., "M1▲"). For downward conditions, they show markers with downward triangles (e.g., "M1▼").
NOTE: By default, this script populates the "M1" field with a pre-configured list for a mean reversion group ("2,18,24,28"). The other fields are empty. If any "M*" input does not contain a value, the indicator ignores it in the alert calculations.
Custom alert messages
By default, the indicator's alert message text contains the activated markers and their direction as a comma-separated list. Users can override this message for upward or downward alerts with the two text fields at the bottom of the "Settings/Inputs" tab. When the fields are not empty , the alerts use that text instead of the default marker list.
NOTE: This script generates alert triggers, not the alerts themselves. To set up an alert based on this script's conditions, open the "Create Alert" dialog box, then select the "Ensemble Alerts" and "Any alert() function call" options in the "Condition" tabs. See the Alerts FAQ in our Pine Script™ User Manual for more information.
Condition visualization
This script offers organized visualizations of its conditions, allowing users to inspect the behaviors of each condition alongside the specified groups. The key visual features include:
1) Conditional plots
• The indicator plots the history of each individual condition, excluding the external conditions, as circles at different levels. Opposite conditions appear at positive and negative levels with the same absolute value. The plots for each condition show values only on the bars where they occur.
• Each condition's plot is color-coded based on its type. Aqua and orange plots represent opposing directional conditions, and purple plots represent non-directional conditions. The titles of the plots also contain the condition numbers to which they apply.
• The plots in the separate pane can be turned on or off with the "Show plots in pane" checkbox near the top of the "Settings/Inputs" tab. This input only toggles the color-coded circles, which reduces the graphical load. If you deactivate these visuals, you can still inspect each condition from the script's status line and the Data Window.
• As a bonus, the indicator includes "Up alert" and "Down alert" plots in the Data Window, representing the combined upward and downward ensemble alert conditions. These plots are also usable in additional indicator-on-indicator calculations.
2) Dynamic labels
• The indicator draws a label on the main chart pane displaying the activated group markers (e.g., "M1▲") each time an alert condition occurs.
• The labels for upward alerts appear below chart bars. The labels for downward alerts appear above the bars.
NOTE: This indicator can display up to 500 labels because that is the maximum allowed for a single Pine script.
3) Background highlighting
• The indicator can highlight the main chart's background on bars where upward or downward condition groups activate. Use the "Highlight background" inputs in the "Settings/Inputs" tab to enable these highlights and customize their colors.
• Unlike the dynamic labels, these background highlights are available for all chart bars, irrespective of the number of condition occurrences.
█ NOTES
• This script uses Pine Script™ v6, the latest version of TradingView's programming language. See the Release notes and Migration guide to learn what's new in v6 and how to convert your scripts to this version.
• This script imports our new Alerts library, which features functions that provide high-level simplicity for working with complex compound conditions and alerts. We used the library's `compoundAlertMessage()` function in this indicator. It evaluates items from "bool" arrays in groups specified by an array of strings containing comma-separated index lists , returning a tuple of "string" values containing the marker of each activated group.
• The script imports the latest version of the ta library to calculate several technical indicators not included in the built-in `ta.*` namespace, including Double Exponential Moving Average (DEMA), Triple Exponential Moving Average (TEMA), Fractal Adaptive Moving Average (FRAMA), Tilson T3, Awesome Oscillator (AO), Full Stochastic (%K and %D), SuperTrend, and Donchian Channels.
• The script uses the `force_overlay` parameter in the label.new() and bgcolor() calls to display the drawings and background colors in the main chart pane.
• The plots and hlines use the available `display.*` constants to determine whether the visuals appear in the separate pane.
Look first. Then leap.
Market structureHi all!
This script shows you the market structure. You can choose to show internal market structure (with pivots of a default length of 5) and swing market structure (with pivots of a default length of 50). For these two trends it will show you:
• Break of structure (BOS)
• Change of character (CHoCH) (mandatory)
• Equal high/low (EQH/EQL)
It's inspired by "Smart Money Concepts (SMC) " by LuxAlgo that will also show you the market structure.
It will create the two market structures depending on the pivots found. Both of these market structures can be enabled/disabled. The pivots length can be configured separately. The pivots found will be the 'base' of this indicator and will show you when price breaks it. When that happens a break of structure or a change of character will be created. The latest 5 pivots found within the current trends will be kept to take action on. The internal market structure is shown with dashed lines and swing market structure is shown with solid lines.
A break of structure is removed if an earlier pivots within the same trend is broken. Like in the images below, the first pivot (in the first image) is removed when an earlier pivot's higher price within the same trend is broken (the second image):
Equal high/lows have a pink zone (by default but can be changed by the user). These zones can be configured to be extended to the right (off by default). Equal high/lows are only possible if it's not been broken by price and if a later bar has a high/low within the limit it's added to the zone (without it being more 'extreme' (high or low) then the previous price). A factor (percentage of width) of the Average True Length (of length 14) that the pivot must be within to to be considered an Equal high/low. This is configurable and sets this 'limit' and is 10 by default.
You are able to show the pivots that are used. "HH" (higher high), "HL" (higher low), "LH" (lower high), "LL" (lower low) and "H"/"L" (for pivots (high/low) when the trend has changed) are the labels used.
This script has proven itself useful for me to quickly see how the current market is. You can see the pivots (price and bar) where break of structure or change of character happens to see the current trends. I hope that you will find this useful for you.
When programming I focused on simplicity and ease of read. I did not focus on performance, I will do so if it's a problem (haven't noticed it is one yet).
You can set alerts for when a change of character happens. You can configure it to fire on when it happens (all or once per bar) but it defaults to 'once_per_bar_close' to avoid repainting. This has the drawback to alert you when the bar closes.
TLDR: this is an indicator showing you the market structure (break of structures and change of characters) using swing points/pivots. Two trends can be shown, internal (with pivots of length of 5) and swing (with pivots of the length of 50).
Best of trading luck!
Flashtrader´s Statistical BandwidthsThe vast majority of traders exclusively concern
themselves with trend-following in all its facets. Scoring
points with trends on a regular basis is a difficult task
since prices do not constantly move in one direction
or another. In the case of the DAX future, for example,
only about 30 per cent of all trading days in a year are
trend days. And of these, there are x percent long ones
and x per cent short ones. Catching the very days when
prices rise or fall from the opening to the close is a major
challenge for a trader who also needs to have previously
recognised the corresponding direction.
However, there are also other ways of profit-taking
every day – for example, by using the mean reversion
strategy. The idea behind this is the fact that prices reach
a high and a low every day – but very rarely close at the
high or the low. This means that prices always move
away from these extreme points and the closing price is
somewhere in between. A profitable trading strategy can
be developed out of this.
But how can you know where the high and the low
will be tomorrow? Is it possible for you to know this in
advance? No – because no one can predict the future. Or
can they? At least it can be statistically determined how
high or low prices could go tomorrow. There is a high
degree of probability that one of the two possibilities
will materialise. It will then be necessary to act.
Calculation
Classic pivot points for the following day are calculated
from the high, low and closing price. But does it really
make sense to use such a mix? I don’t think so and
use a different calculation for this strategy. In a first step,
only the differences between the start and the high or low
are calculated on a daily basis. To avoid being dependent
on individual days and outliers, it is advisable to calculate,
in a second step, the average of these differences over
the past five days. Finally, this average will then be added
at the opening price of the current trading day for the
upper statistical bandwidth and subtracted for the lower
bandwidth.
upper bandwidth = oSTB (violet dashed line in the chart)
lower bandwidth = uSTB (violet dashedline in the chart)
The second interesting question is, if the previous day's high has been exceeded, how much further can the price rise from a mathematical/statistical point of view?
These calculated previous day highs expansions are shown as red dashed lines
Previous day's high expansion = VTHA
Previous day's low expansion = VTTA
For further orientation, the previous day's high (VTH) and the previous day's low (VTT) are shown in light blue dashed lines
And as a supplement, the previous day's close in the DAX Future at 10:00 p.m. VTSA in violet solid lines and the previous day's close in the cash register at 5:30 p.m. VTSN in yellow solid lines
Reaching the calculated extreme values does not mean that the trend has to change immediately, but there is at least temporary exhaustion potential with which you can earn a few points every day in the area of scalping.
Example for cheap entry long:
Example for cheap entry short:
Deutsch:
Die Masse der Trader beschäftigt sich ausschließlich mit Trendfolge in all ihren Facetten. Mit Trends regelmäßig zu punkten ist ein schwieriges Unterfangen, da die Kurse nicht ständig in die eine oder andere Richtung laufen. Beim DAX-Future zum Beispiel sind von allen Börsentagen im Jahr lediglich zirka 30 Prozent Trendtage. Davon sind dann auch noch x Prozent Long und x Prozent Short. Hier genau die Tage abzupassen, an denen die Kurse von Börsenbeginn bis zum Schluss steigen beziehungsweise fallen, ist eine große Herausforderung – wobei der Trader zuvor noch die entsprechende Richtung erkannt haben muss. Es gibt jedoch auch noch andere Methoden täglich Gewinne mitzunehmen, zum Beispiel mit der Mean-Reversion-Strategie (Mittelwertumkehr).
Hintergrund ist die Tatsache, dass die Kurse jeden Tag ein Hoch und ein Tief erreichen – aber sehr selten am Hoch oder am Tief schließen. Das bedeutet, dass die Preise sich immer wie der von diesen Extrempunkten wegbewegen und der Schlusskurs irgendwo dazwischen liegt. Hieraus lässt sich eine profitable Handelsstrategie entwickeln. Aber woher kannst Du wissen, wo morgen das Hoch und das Tief sein wird? Kannst Du das vorher schon wissen? Nein – denn niemand kann die Zukunft vorhersagen. Oder doch? Statistisch lässt sich zumindest bestimmen, wie hoch und wie tief die Kurse morgen steigen oder fallen könnten. Eine Seite wird mit sehr hoher Wahrscheinlichkeit ein treffen. Dann gilt es zu handeln.
Berechnung Klassischer Pivot-Punkte für den folgenden Tag werden aus Hoch, Tief und Schlusskurs berechnet. Aber ist es wirklich sinnvoll, einen solchen Mix zu verwenden? Ich finde das nicht und verwenden für diese Strategie eine andere Berechnung. Im ersten Schritt werden täglich die Differenzen nur vom Start bis zum Hoch beziehungsweise Tief errechnet. Um nicht von einzelnen Tagen und Ausreißern abhängig zu sein, empfiehlt es sich, in einem zweiten Schritt den Durchschnitt dieser Differenzen über die letzten fünf Tage zu errechnen. Zuletzt wird dann dieser Durchschnitt zum Eröffnungskurs des aktuellen Handelstages für die obere statistische Bandbreite addiert und für die untere Bandbreite subtrahiert.
Obere statistische Bandbreite = oSTB (violette gestrichelte Linie im Chart)
Untere statistische Bandbreite = uSTB (violette gestrichelte Linie im Chart)
Die zweite interessante Frage ist, wenn das Vortageshoch überschritten wurde, wie weit kann der Kurs dann noch steigen aus mathematisch/statistischer Sicht?
Diese berechneten Vortagesextremausdehnungen sind als rote gestrichelte Linien dargestellt
Vortageshochausdehnung = VTHA
Vortagestiefausdehnung = VTTA
Für die weitere Orientierung sind die Vortageshochs (VTH) und die Vortagestiefs (VTT) als hellblaue gestrichelte Linien abgebildet.
Als Ergänzung wird noch der Vortages Schluss im Dax Future um 22:00 Uhr VTSA mit einer violetten durchgezogenen Linie und der Kassamarktschluss um 17:30 Uhr mit einer gelben durchgezogenen Linie gezeigt.
Das Erreichen der berechneten Extremwerte bedeutet nicht, das der Trend sofort drehen muss, aber es sind zumindest temporäre Erschöpfungspotentiale mit denen sich im Bereich scalping täglich einige Punkte verdienen lassen.
Beispiel für günstigen Einstieg Long:
Beispiel für günstigen Einstieg Short:
Market Structure & Session Alerts### Market Structure & Session Alerts Indicator
#### Overview
The "Market Structure & Session Alerts" indicator is a comprehensive tool designed to assist traders in identifying key market structure levels, detecting liquidity sweeps, and receiving alerts for specific trading sessions. This indicator is particularly useful for traders who want to keep an eye on previous high and low levels and be alerted during pre-London and pre-New York sessions.
#### Features
1. **Previous High/Low Levels:**
- **Daily, Weekly, and Monthly Highs and Lows:** The indicator plots the previous day, week, and month high and low levels on the chart. These levels can be crucial for identifying support and resistance zones.
- **Toggle Display:** Users can choose to show or hide these levels using the "Show Previous Day/Week/Month High/Low" option.
2. **Liquidity Sweep Detection:**
- **Liquidity Sweep Identification:** The indicator detects liquidity sweeps when the current price closes above the previous day's high. This can signal potential reversals or continuations in the market.
- **Visual Alerts:** When a liquidity sweep is detected, a green triangle is plotted below the bar.
3. **Session Alerts:**
- **Session Timings:** Users can set specific start and end times for the pre-London and pre-New York sessions to match their timezone.
- **Visual Background Highlight:** The background of the chart is highlighted in yellow during the defined session times to provide a visual cue.
- **Alert Messages:** The indicator can generate alerts to notify traders when the market enters the pre-London or pre-New York session.
4. **Current Price Line:**
- The current price is plotted as a black line, providing a clear visual reference for the current market price.
#### How to Use
1. **Input Parameters:**
- `Show Previous Day/Week/Month High/Low`: Enable or disable the display of previous high/low levels.
- `Show Liquidity Sweep`: Enable or disable the detection and display of liquidity sweeps.
- `Show Session Alerts`: Enable or disable session alerts and background highlights.
2. **Session Timing Adjustments:**
- Set the `Pre-London Start`, `Pre-London End`, `Pre-New York Start`, and `Pre-New York End` times according to your timezone to ensure accurate session alerts.
3. **Alerts:**
- Make sure alerts are enabled in your TradingView settings to receive notifications when the market enters the pre-London or pre-New York sessions.
#### Example Use Cases
- **Day Traders:** Identify potential support and resistance levels using the previous day's high and low.
- **Swing Traders:** Use weekly and monthly high and low levels to determine significant market structure points.
- **Scalpers:** Detect liquidity sweeps to identify potential quick trades.
- **Session Traders:** Be alerted when the market enters key trading sessions to align your trading strategy with major market activities.
This indicator combines multiple market analysis tools into one, providing a robust system for traders to enhance their trading decisions and market awareness.
GKD-BT Multi-Ticker Baseline Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker Baseline Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Multi-Ticker Baseline Backtest
The Multi-Ticker SCSC Backtest is a Solo Confirmation Super Complex backtest that allows traders to test GKD-B Multi-Ticker Baseline series baselines indicators filtered. The purpose of this backtest is to enable traders to quickly evaluate the viability of a Baseline across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting threshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import 1-10 tickers into the GKD-B Multi-Ticker Baseline indicator
2. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-B Multi-Ticker Baseline indicator (Volatility-Adaptive, Stepped, etc.) into the GKD-BT Multi-Ticker Baseline Backtest.
3. Import the same 1-10 tickers from number step 1 above into the GKD-BT Multi-Ticker Baseline Backtest indicator into the text area field "Input Tickers separated by commas".
3. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
4. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying add-ons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolated per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, and the Average Directional Index (ADX).
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker CC Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Advance Trend Pressure as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
ka66: Swing/Pivot Point LinesThis indicator draws swing-highs and swing-lows, also called pivot highs and lows.
A swing high is a bar which has a higher-high than its surrounding bars (to the left and the right).
A swing low is a bar which has a lower-low than its surrounding bars (to the left and the right).
A common example of a pivot is Bill Williams' Fractal, which specifies that the centre bar must have a higher high than 2 bars to its left, and 2 bars to its right for a swing high, taking into account 5 bars at a time. Similarly, for a swing low, the centre bar must have a lower low than the 2 bars to its left and right.
This indicator allows configurable adjacent bars as input. Entering 2, means it essentially picks out a Williams Fractal. But you can select 1 (say for higher timeframes), using one 1 bar to the left and right of the centre bar.
The indicator will draw Swing/Pivot High/Low as circles at the same price level as the centre bar, till the next one shows up. Drawing is offset so it starts at the centre bar (the swing bar), showing exactly where the pivot bar is.
There are 2 main uses of pivot points, in various strategies:
Market Structure: to objectively define higher-highs/lows and lower-highs/lows in Trend Analysis.
More generally, to then determine if a trend might reverse, or continue as pivot levels are broken.
Messy pivot structures easily point out ranging markets.
There are a few of these, some closed source, which I don't like, since I think people should generally know what they are trading with, and I want to make sure I understand the logic exactly.
ATR Bands with Optional Risk/Reward Colors█ OVERVIEW
This indicator projects ATR bands and, optionally, colors them based on a risk/reward advantage for those who trade breakouts/breakdowns using moving averages as partial or full exit points.
█ DEFINITIONS
► True Range
The True Range is a measure of the volatility of a financial asset and is defined as the maximum difference among one of the following values:
- The high of the current period minus the low of the current period.
- The absolute value of the high of the current period minus the closing price of the previous period.
- The absolute value of the low of the current period minus the closing price of the previous period.
► Average True Range
The Average True Range was developed by J. Welles Wilder Jr. and was introduced in his 1978 book titled "New Concepts in Technical Trading Systems". It is calculated as an average of the true range values over a certain number of periods (usually 14) and is commonly used to measure volatility and set stop-loss and profit targets (1).
For example, if you are looking at a daily chart and you want to calculate the 14-day ATR, you would take the True Range of the previous 14 days, calculate their average, and this would be the ATR for that day. The process is then repeated every day to obtain a series of ATR values over time.
The ATR can be smoothed using different methods, such as the Simple Moving Average (SMA), the Exponential Moving Average (EMA), or others, depending on the user's preferences or analysis needs.
► ATR Bands
The ATR bands are created by adding or subtracting the ATR from a reference point (usually the closing price). This process generates bands around the central point that expand and contract based on market volatility, allowing traders to assess dynamic support and resistance levels and to adapt their trading strategies to current market conditions.
█ INDICATOR
► ATR Bands
The indicator provides all the essential parameters for calculating the ATR: period length, time frame, smoothing method, and multiplier.
It is then possible to choose the reference point from which to create the bands. The most commonly used reference points are Open, High, Low, and Close, but you can also choose the commonly used candle averages: HL2, HLC3, HLCC4, OHLC4. Among these, there is also a less common "OC2", which represents the average of the candle body. Additionally, two parameters have been specifically created for this indicator: Open/Close and High/Low.
With the "Open/Close" parameter, the upper band is calculated from the higher value between Open and Close, while the lower one is calculated from the lower value between Open and Close. In the case of bullish candles, therefore, the Close value is taken as the starting point for the upper band and the Open value for the lower one; conversely, in bearish candles, the Open value is used for the upper band and the Close value for the lower band. This setting can be useful for precautionally generating broader bands when trading with candlesticks like hammers or inverted hammers.
The "High/Low" parameter calculates the upper band starting from the High and the lower band starting from the Low. Among all the available options, this one allows drawing the widest bands.
Other possible options to improve the drawing of ATR bands, aligning them with the price action, are:
• Doji Smoothing: When the current candle is a doji (having the same Open and Close price), the bands assume the values they had on the previous candle. This can be useful to avoid steep fluctuations of the bands themselves.
• Extend to High/Low: Extends the bands to the High or Low values when they exceed the value of the band.
• Round Last Cent: Expands the upper band by one cent if the price ends with x.x9, and the lower band if the price ends with x.x1. This function only works when the asset's tick is 0.01.
► Risk/Reward Advantage
The indicator optionally colors the ATR bands after setting a breakpoint, one or two risk/reward ratios, and a series of moving averages. This function allows you to know in advance whether entering a trade can provide an advantage over the risk. The band is colored when the ratio between the distance from the break point to the band and the distance from the break point to the first available moving average reaches at least the set ratio value. It is possible to set two colorings, one for a minimum risk/reward ratio and one for an optimal risk/reward ratio.
The break point can be chosen between High/Low (High in case of breakout, Low in case of breakdown) or Open/Close (on breakouts, Close with bullish candles or Open with bearish candles; on breakdowns, Close with bearish candles or Open with bullish candles).
It is possible to choose up to 10 moving averages of various types, including the VWAP with the Anchor Period (2).
Depending on the "Price to MA" setting, the bands can be individually or simultaneously colored.
By selecting "Single Direction," the risk/reward calculation is performed only when all moving averages are above or below the break point, resulting in only one band being colored at a time. For this reason, when the break point is in between the moving averages, the calculation is not executed. This setting can be useful for strategies involving price movement from a level towards a series of specific moving averages (for example, in reversals starting from a certain level towards the VWAP with possible partial take profits on some previous moving averages, or simply in trend following towards one or more moving averages).
Choosing "Both Directions" the risk/reward ratio is calculated based on the first available moving averages both above and below the price. This setting is useful for those who operate in range bound markets or simply take advantage of movements between moving averages.
█ NOTE
This script may not be suitable for scalping strategies that require immediate entries due to the inability to know the ATR of a candle in advance until its closure. Once the candle is closed, you should have time to place a stop or stop-limit order, so your strategy should not anticipate an immediate start with the next candle. Even more conveniently, if your strategy involves an entry on a pullback, you can place a limit order at the breakout level.
(1) www.tradingview.com
(2) For convenience, the code for the Anchor Period has been entirely copied from the VWAP code provided by TradingView.
Market Pivot Levels [Past & Live]Market Levels provide a robust view of daily pivot points of markets such as high/low/close with both past and live values shown at the same time using the recently updated system of polylines of pinescript.
The main need for this script arose from not being able to use plots for daily points because plots are inherently once drawn can't be erased and because we can't plot stuff for previous bars after values are determined we can't use them reliably. And while we can use traditional lines, because we would have extremely high amount of lines and we would have to keep removing the previous ones it wouldn't be that effective way for us. So we try to do it with the new method of polylines .
Features of this script:
- Daily High/Low Points
- Yesterday High/Low/Close Points
- Pre-Market High-Low points.
Now let's preview some of the important points of code and see how we achieve this:
With the code below we make sure no matter which chart we are using we are getting the extended hours version of sessions so our calculations are made safely for viewing pre-market conditions.
// Let's get ticker extended no matter what the current chart is
tc = ticker.new(syminfo.prefix, syminfo.ticker, session.extended)
Coding our own function to calculate high's and low's because inbuilt pinescript function cannot take series and we send this function to retrieve our high's and lows.
// On the fly function to calculate daily highlows instead of tv inbuilt because tv's length cannot take series
f_highlow(int last) =>
bardiff = last
float _low = low, float _high = high
for i = bardiff to 0 by 1
if high > _high
_high := high
if low < _low
_low := low
With doing calculations at the bars of day ending points we can retrieve the correct points and values and push them for our polylines array so it can be used in best way possible.
// Daily change points
changeD = timeframe.change("D")
// When new day starts fill polyline arrays with previous day values for polylines to draw on chart
// We also update prevtime values with current ones after we pushed to the arrays
if changeD
f_arrFill(cpArrHigh, cpArrLow, prevArrh, prevArrl, prevArrc, prevMarh, prevMarl)
valHolder.unshift(valueHold.new(_high, _low, _high, _close, _low, time, pr_h, pr_l))
The rest of the code is annotated and commented. You can let me know in comments if you have any questions. Happy trading.
AI Momentum [YinYang]Overview:
AI Momentum is a kernel function based momentum Indicator. It uses Rational Quadratics to help smooth out the Moving Averages, this may give them a more accurate result. This Indicator has 2 main uses, first it displays ‘Zones’ that help you visualize the potential movement areas and when the price is out of bounds (Overvalued or Undervalued). Secondly it creates signals that display the momentum of the current trend.
The Zones are composed of the Highest Highs and Lowest lows turned into a Rational Quadratic over varying lengths. These create our Rational High and Low zones. There is however a second zone. The second zone is composed of the avg of the Inner High and Inner Low zones (yellow line) and the Rational Quadratic of the current Close. This helps to create a second zone that is within the High and Low bounds that may represent momentum changes within these zones. When the Rationalized Close crosses above the High and Low Zone Average it may signify a bullish momentum change and vice versa when it crosses below.
There are 3 different signals created to display momentum:
Bullish and Bearish Momentum. These signals display when there is current bullish or bearish momentum happening within the trend. When the momentum changes there will likely be a lull where there are neither Bullish or Bearish momentum signals. These signals may be useful to help visualize when the momentum has started and stopped for both the bulls and the bears. Bullish Momentum is calculated by checking if the Rational Quadratic Close > Rational Quadratic of the Highest OHLC4 smoothed over a VWMA. The Bearish Momentum is calculated by checking the opposite.
Overly Bullish and Bearish Momentum. These signals occur when the bar has Bullish or Bearish Momentum and also has an Rationalized RSI greater or less than a certain level. Bullish is >= 57 and Bearish is <= 43. There is also the option to ‘Factor Volume’ into these signals. This means, the Overly Bullish and Bearish Signals will only occur when the Rationalized Volume > VWMA Rationalized Volume as well as the previously mentioned factors above. This can be useful for removing ‘clutter’ as volume may dictate when these momentum changes will occur, but it can also remove some of the useful signals and you may miss the swing too if the volume just was low. Overly Bullish and Bearish Momentum may dictate when a momentum change will occur. Remember, they are OVERLY Bullish and Bearish, meaning there is a chance a correction may occur around these signals.
Bull and Bear Crosses. These signals occur when the Rationalized Close crosses the Gaussian Close that is 2 bars back. These signals may show when there is a strong change in momentum, but be careful as more often than not they’re predicting that the momentum may change in the opposite direction.
Tutorial:
As we can see in the example above, generally what happens is we get the regular Bullish or Bearish momentum, followed by the Rationalized Close crossing the Zone average and finally the Overly Bullish or Bearish signals. This is normally the order of operations but isn’t always how it happens as sometimes momentum changes don’t make it that far; also the Rationalized Close and Zone Average don’t follow any of the same math as the Signals which can result in differing appearances. The Bull and Bear Crosses are also quite sporadic in appearance and don’t generally follow any sort of order of operations. However, they may occur as a Predictor between Bullish and Bearish momentum, signifying the beginning of the momentum change.
The Bull and Bear crosses may be a Predictor of momentum change. They generally happen when there is no Bullish or Bearish momentum happening; and this helps to add strength to their prediction. When they occur during momentum (orange circle) there is a less likely chance that it will happen, and may instead signify the exact opposite; it may help predict a large spike in momentum in the direction of the Bullish or Bearish momentum. In the case of the orange circle, there is currently Bearish Momentum and therefore the Bull Cross may help predict a large momentum movement is about to occur in favor of the Bears.
We have disabled signals here to properly display and talk about the zones. As you can see, Rationalizing the Highest Highs and Lowest Lows over 2 different lengths creates inner and outer bounds that help to predict where parabolic movement and momentum may move to. Our Inner and Outer zones are great for seeing potential Support and Resistance locations.
The secondary zone, which can cross over and change from Green to Red is also a very important zone. Let's zoom in and talk about it specifically.
The Middle Zone Crosses may help deduce where parabolic movement and strong momentum changes may occur. Generally what may happen is when the cross occurs, you will see parabolic movement to the High / Low zones. This may be the Inner zone but can sometimes be the outer zone too. The hard part is sometimes it can be a Fakeout, like displayed with the Blue Circle. The Cross doesn’t mean it may move to the opposing side, sometimes it may just be predicting Parabolic movement in a general sense.
When we turn the Momentum Signals back on, we can see where the Fakeout occurred that it not only almost hit the Inner Low Zone but it also exhibited 2 Overly Bearish Signals. Remember, Overly bearish signals mean a momentum change in favor of the Bulls may occur soon and overly Bullish signals mean a momentum change in favor of the Bears may occur soon.
You may be wondering, well what does “may occur soon” mean and how do we tell?
The purpose of the momentum signals is not only to let you know when Momentum has occurred and when it is still prevalent. It also matters A LOT when it has STOPPED!
In this example above, we look at when the Overly Bullish and Bearish Momentum has STOPPED. As you can see, when the Overly Bullish or Bearish Momentum stopped may be a strong predictor of potential momentum change in the opposing direction.
We will conclude our Tutorial here, hopefully this Indicator has been helpful for showing you where momentum is occurring and help predict how far it may move. We have been dabbling with and are planning on releasing a Strategy based on this Indicator shortly.
Settings:
1. Momentum:
Show Signals: Sometimes it can be difficult to visualize the zones with signals enabled.
Factor Volume: Factor Volume only applies to Overly Bullish and Bearish Signals. It's when the Volume is > VWMA Volume over the Smoothing Length.
Zone Inside Length: The Zone Inside is the Inner zone of the High and Low. This is the length used to create it.
Zone Outside Length: The Zone Outside is the Outer zone of the High and Low. This is the length used to create it.
Smoothing length: Smoothing length is the length used to smooth out our Bullish and Bearish signals, along with our Overly Bullish and Overly Bearish Signals.
2. Kernel Settings:
Lookback Window: The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50.
Relative Weighting: Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25.
Start Regression at Bar: Bar index on which to start regression. The first bars of a chart are often highly volatile, and omission of these initial bars often leads to a better overall fit. Recommended range: 5-25.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
blackOrb CandleAddressing the Shortcomings of Conventional Candle Charts
I. Surmounting Volatility Challenges
In the realm of combined heightened or subdued volatility and erratic market conditions, traditional candlestick charts are susceptible to deficiencies in isolating extraneous data noise (e.g. high/low wicks, given their frequent incongruity with pivotal market dynamics or arbitrary green/red coloring of candle bodies).
II. Precision in Application
Novice traders may erroneously construe and misemploy traditional candlestick patterns, culminating in erroneous trading determinations. In addressing this challenge, this indicator can help to identify critical signal confluences, enhancing potential signals accuracy.
III. Strategy at the Core
Relying solely on candlestick charts lacks potency without an underpinning well-knit strategy. blackOrb's methodology integrates discernment of pivotal chart configurations with the meticulous construction of comprehensive strategies to mirror a comprehension of potential market dynamics.
blackOrb's Aspirations: Overcoming Enunciated Challenges of Traditional Candle Charts
- Customizable Data Analysis
Engendering the evolution of candle charts involves the judicious adjustment of multifarious open/high/low/close iterations coupled with evaluative mechanisms such as Heikin Ashi and MA smoothing, combined with stochastic calculations.
- Holistic Perspective
Seamless deployment of trading strategies is engendered through salient facets, encompassing up- and downside ratios as well as adaptable true range visualizations, attuned to unfolding price dynamics.
- Personalized Approach
Adaptations in trading styles are seamlessly accommodated, as this indicator offers stochastic candle coloring with customizable stochastic look-back evaluation phases. A selection of over 20 color schemes accommodates individual preferences to differentiate various chart setups at first glance.
Note: However, it's important to recognize that the efficacy of evaluation coloring might be compromised during periods of lateral price movement, characterized by less prominent market trends.
- Ghost Mode for Comparative Insights
Unveiling correlations and divergences, the Ghost Mode overlays two candle charts, which can reveal price trajectories and reactions (e.g. Apple stock's potential response to the NASDAQ 100 Technology Sector Index).
Note: This approach may not capture nuanced correlations during intricate market scenarios.
Technical Methodology
At its core, the stochastic calculation methodology of this indicator centers around the following formula:
100 * (close - lowest(low, length)) / (highest(high, length) - lowest(low, length))
This key formula employs a stochastic calculation methodology that assesses the percentage deviation of the closing price from the lowest low over a specified timeframe (length), relative to the span between the highest high and the lowest low. The outcome is normalized within a range of 0 to 100, providing insights into the relative position of the closing price within the high-low range. Traders can define the specific periods over which the stochastic calculation is performed.
Based on this stochastic analysis, the indicator integrates candle coloring, affording users the flexibility to adjust the sensitivity of candle coloring according to customized stochastic look-back evaluation phases. Consequently, the coloration of candles by length evaluation can mirror a comprehension of market dynamics.
By allowing traders to designate specific periods for the stochastic calculation, it fosters adaptability in combination with the following technical features:
- Conjoining optional transparent Heikin Ashi and/or a weighted MA alternative to harness the virtues of smoothing sans confounding authentic price data and candle dynamics
- Individual electable focus range encompassing retrospection and real-time alignments
- Intra-temporal evaluations, sub-domains & amalgamated value permutations
- Prioritizing individually chosen focus time intervals within the realm of real price highs and lows
- Elaborate price display (e.g. high/low/ohlc4/close) upon chart-hover, accentuating close price implications
- Features offering diverse scaling options, alongside adaptable and customizable price display
- Unveiling uncluttered and directed candle body visualization, implementing wicks to the transparent candle body, given their frequent incongruity of high/low data with pivotal market dynamics
Note on Usability
This indicator isn't intended for standalone trading application. Instead, it offers an alternative approach to traditional candle charts, serving as a supplementary tool for orientation within broader trading strategies.
Irrespective of market conditions, it can harmonize with a wider range of trading styles and instruments/trading pairs/indices like Stocks, Gold, EURUSDSPX500, GBPUSD, BTCUSD and Oil.
Inspiration and Publishing
Taking genesis from the inspirations amongst others provided by TradingView Pine Script Wizard Kodify, blackOrb Candles is an multi-encompassing script meticulously forged from scratch. It aspires to furnish a comprehensive candle chart approach, borne out of personal experiences and a strong dedication in supporting the trading community. We eagerly await valuable feedback to refine and further enhance
LIT - TimingIntroduction
This Script displays the Asia Session Range, the London Open Inducement Window, the NY Open Inducement Window, the Previous Week's high and low, the Previous Day's highs and lows, and the Day Open price in the cleanest way possible.
Description
The Indicator is based on UTC -7 timing but displays the Session Boxes automatically correct at your chart so you do not have to adjust any timings based on your Time Zone and don't have to do any calculations based on your UTC. It is already perfect.
You will see on default settings the purple Asia Box and 2 grey boxes, the first one is for the London Open Inducement Window (1 hour) and the second grey box is for the NY Open Inducement Window (also 1 hour)
Asia Range comes with default settings with the Asia Range high, low, and midline, you can remove these 3 lines in the settings "style" and untick the "Lines" box, that way you only will have the boxes displayed.
Special Feature
Most Timing-based Indicators have "bugged" boxes or don't show clean boxes at all and don't adjust at daylight savings times, we made sure that everything automatically gets adjusted so you don't have to! So the timings will always display at the correct time regarding the daylight savings times.
Combining Timing with Liquidity Zones the right way and in a clear, clean, and simple format.
Different than others this script also shows the "true" Asia range as it respects the "day open gap" which affects the Asia range in other scripts and it also covers the full 8 hours of Asia Session.
Additions
You can add in the settings menu the last week's high and low, the previous day's high and low, and also the day's open price by ticking the boxes in the settings menu
All colors of the boxes are fully adjustable and customizable for your personal preferences. Same for the previous weeks and day highs and lows. Just go to "Style" and you can adjust the Line types or colors to your preferred choice.
Recommended Use
The most beautiful display is on the M5 Timeframe as you have a clear overview of all sessions without losing the intraday view. You can also use it on the M1 for more details or the M15 for the bigger picture. The Template can hide on higher time frames starting from the H1 to not flood your chart with boxes.
How to use the Asia Session Range Box
Use the Asia Range Box as your intraday Guide, keep in mind that a Breakout of Asia high or low induces Liquidity and a common price behavior is a reversal after the fake breakout of that range.
How to use the London Open and NY Open Inducement Windows
Both grey boxes highlight the Open of either London Open or NY Open and you should keep an eye out for potential Liquditiy Graps or Mitigations during that times as this is when they introduce major Liquidity for the regarding Session.
How to use the Asia high, low and midline and day open price
After Asia Range got taken out in one direction, often price comes back to those levels to mitigate or bounce off, so you can imagine those zones as support and resistance on some occasions, recommended in combination with Imbalances.
How to use the previous day and week's highs and lows
Once added in the settings, you can display those price levels, you can use them either as Liquidity Targets or as Inducement Levels once they are taken out.
Enjoy!
Structure High LowsThis indicator identifies and tracks swing highs and lows in any market and timeframe, plotting them as solid lines on a chart. It offers customizable line features and can follow rules to update highs and lows based on bullish or bearish conditions.
In bullish ranges, the indicator updates the high until a swing high forms, while in bearish ranges, it updates the low until a swing low forms. These swings determine the final high or low. The indicator's unique approach incorporates market structure insights to potentially identify price movement trends and validate strategies across timeframes.
In bearish conditions, the indicator updates the low until a swing low forms, while the high adjusts when a candle's body surpasses the prior high. Swing highs entail lower highs on both sides, and swing lows involve higher lows. This indicator's innovation lies in its use of market structure to track price movement and validate trend strategies across timeframes.
GKD-BT Optimizer SCC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer SCC Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Optimizer SCC Backtest
The Optimizer SCC Backtest is a Solo Confirmation Complex backtest that allows traders to test single GKD-C Confirmation indicator with GKD-B Baseline and GKD-V Volatility/Volume filtering across 10 varying inputs. The purpose of this backtest is to enable traders to optimize a GKD-C indicator given varying inputs.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting treshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline indicator into the GKD-BT Optimizer SCC Backtest.
2. Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Optimizer SCC Backtest.
3. Select the "Optimizer" option in the GKD-C Confirmation indicator
4. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Signals" into the GKD-C Indicator Signals dropdown
5. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Start" into the GKD-C Indicator Start dropdown
6. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Skip" into the GKD-C Indicator Skip dropdown
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C Confirmation" indicator.
Import GKD-C Continuation: Imports the "GKD-C Continuation" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Optimizer Full GKD Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transofrm as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Optimizer SCC Backtest
GKD-BT Optimizer SCC Backtest
GKD-BT Optimizer SCC Backtest
GKD-C GKD-BT Optimizer Full GKD Backtest
GKD-BT Optimizer SCS Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer SCS Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Optimizer SCS Backtest
The Optimizer SCS Backtest is a Solo Confirmation Simple backtest that allows traders to test single GKD-C confirmation indicators across 10 varying inputs. The purpose of this backtest is to enable traders to optimize a GKD-C indicator given varying inputs.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting treshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Signals" into the GKD-C Indicator Signals dropdown
1. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Start" into the GKD-C Indicator Start dropdown
1. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Skip" into the GKD-C Indicator Skip dropdown
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C Confirmation" indicator.
Import GKD-C Continuation: Imports the "GKD-C Continuation" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Optimizer Full GKD Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transofrm as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Optimizer SCC Backtest
GKD-BT Optimizer SCS Backtest
GKD-BT Optimizer SCS Backtest
GKD-C GKD-BT Optimizer Full GKD Backtest
GKD-BT Multi-Ticker Full GKD Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker Full GKD Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Multi-Ticker Full GKD Backtest
The Multi-Ticker Full GKD Backtest is a Full GKD backtest that allows traders to test single GKD-C Confirmation indicator filtered by a GKD-B Multi-Ticker Baseline, GKD-V Volatility/Volume, and GKD-C Confirmation 2 indicator across 1-10 tickers. In addition. this module adds on various other long and short signls that fall outside the normal GKD standard long and short signals. These additional signals are formed using the GKD-B Multi-Ticker Baseline, GKD-V Volatility/Volume, GKD-C Confirmation 2, and GKD-C Continuation indicators. The purpose of this backtest is to enable traders to quickly evaluate a Baseline, Volatility/Volume, Confirmation 2, and Continuation indicators filtered GKD-C Confirmation 1 indicator across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting threshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import 1-10 tickers into the GKD-B Multi-Ticker Baseline indicator
2. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-B Multi-Ticker Baseline indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
3. Select the "Multi-ticker" option in the GKD-V Volatility/Volume indicator
4. Import 1-10 tickers into the GKD-V Volatility/Volume indicator
5. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
6. Select the "Multi-ticker" option in the GKD-C Confirmation 1 indicator.
7. Import 1-10 tickers into the GKD-C Confirmation 1 indicator.
8. Import the same 1-10 indicators into the GKD-BT Multi-Ticker Full GKD Backtest.
9. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation 1 indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
10. Import 1-10 tickers into the GKD-C Confirmation 2 indicator.
11. Import the same 1-10 indicators into the GKD-BT Multi-Ticker Full GKD Backtest.
12. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation 2 indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
13. Import 1-10 tickers into the GKD-C Continuation indicator.
14. Import the same 1-10 indicators into the GKD-BT Multi-Ticker Full GKD Backtest.
15. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Continuation indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
16. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
17. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C" indicator.
Activate Baseline: Activates the GKD-B Multi-Ticker Baseline.
Activate Goldie Locks Zone Minimum Threshold: Activates the inner Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Goldie Locks Zone Maximum Threshold: Activates the outer Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Volatility/Volume: Activates the GKD-V Volatility/Volume indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker Full GKD Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Transform as shown on the chart above
Confirmation 2: uf2018 as shown on the chart above
Continuation: Coppock Curve as shown on the chart above
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Multi-Ticker SCC Backtest
GKD-BT Multi-Ticker SCS Backtest
