One Shot One Kill ICT [TradingFinder] Liquidity MMXM + CISD OTE🔵 Introduction
The One Shot One Kill trading setup is one of the most advanced methods in the field of Smart Money Concept (SMC) and ICT. Designed with a focus on concepts such as Liquidity Hunt, Discount Market, and Premium Market, this strategy emphasizes precise Price Action analysis and market structure shifts. It enables traders to identify key entry and exit points using a structured Trading Model.
The core process of this setup begins with a Liquidity Hunt. Initially, the price targets areas like the Previous Day High and Previous Day Low to absorb liquidity. Once the Change in State of Delivery(CISD)is broken, the market structure shifts, signaling readiness for trade entry. At this stage, Fibonacci retracement levels are drawn, and the trader enters a position as the price retraces to the 0.618 Fibonacci level.
Part of the Smart Money approach, this setup combines liquidity analysis with technical tools, creating an opportunity for traders to enter high-accuracy trades. By following this setup, traders can identify critical market moves and capitalize on reversal points effectively.
Bullish :
Bearish :
🔵 How to Use
The One Shot One Kill setup is a structured and advanced trading strategy based on Liquidity Hunt, Fibonacci retracement, and market structure shifts (CISD). With a focus on precise Price Action analysis, this setup helps traders identify key market movements and plan optimal trade entries and exits. It operates in two scenarios: Bullish and Bearish, each with distinct steps.
🟣 Bullish One Shot One Kill
In the Bullish scenario, the process starts with the price moving toward the Previous Day Low, where liquidity is absorbed. At this stage, retail sellers are trapped as they enter short trades at lower levels. Following this, the market reverses upward and breaks the CISD, signaling a shift in market structure toward bullishness.
Once this shift is identified, traders draw Fibonacci levels from the lowest point to the highest point of the move. When the price retraces to the 0.618 Fibonacci level, conditions for a buy position are met. The target for this trade is typically the Previous Day High or other significant liquidity zones where major buyers are positioned, offering a high probability of price reversal.
🟣 Bearish One Shot One Kill
In the Bearish scenario, the price initially moves toward the Previous Day High to absorb liquidity. Retail buyers are trapped as they enter long trades near the highs. After the liquidity hunt, the market reverses downward, breaking the CISD, which signals a bearish shift in market structure. Following this confirmation, Fibonacci levels are drawn from the highest point to the lowest point of the move.
When the price retraces to the 0.618 Fibonacci level, a sell position is initiated. The target for this trade is usually the Previous Day Low or other key liquidity zones where major sellers are active.
This setup provides a precise and logical framework for traders to identify market movements and enter trades at critical reversal points.
🔵 Settings
🟣 CISD Logical settings
Bar Back Check : Determining the return of candles to identify the CISD level.
CISD Level Validity : CISD level validity period based on the number of candles.
🟣 LIQUIDITY Logical settings
Swing period : You can set the swing detection period.
Max Swing Back Method : It is in two modes "All" and "Custom". If it is in "All" mode, it will check all swings, and if it is in "Custom" mode, it will check the swings to the extent you determine.
Max Swing Back : You can set the number of swings that will go back for checking.
🟣 CISD Display settings
Displaying or not displaying swings and setting the color of labels and lines.
🟣 LIQUIDITY Display settings
Displaying or not displaying swings and setting the color of labels and lines.
🔵 Conclusion
The One Shot One Kill setup is one of the most effective and well-structured trading strategies for identifying and capitalizing on key market movements. By incorporating concepts such as Liquidity Hunt, CISD, and Fibonacci retracement, this setup allows traders to enter trades with high precision at optimal points.
The strategy emphasizes detailed Price Action analysis and the identification of Smart Money behavior, helping traders to execute successful trades against the general market trend.
With a focus on identifying liquidity in the Previous Day High and Low and aligning it with Fibonacci retracement levels, this setup provides a robust framework for entering both bullish and bearish trades.
The combination of liquidity analysis and Fibonacci retracement at the 0.618 level enables traders to minimize risk and exploit major market moves effectively.
Ultimately, success with the One Shot One Kill setup requires practice, patience, and strict adherence to its rules. By mastering its concepts and focusing on high-probability setups, traders can enhance their decision-making skills and build a sustainable and professional trading approach.
Educational
Professional GBP/JPY Analysis ToolThe foundation of professional trading begins with analyzing individual currencies first, not just currency pairs. By understanding the relative strength of each currency in the pair, traders can anticipate potential market moves with greater accuracy.
This indicator simplifies that process by:
Analyzing Individual Currency Strength:
The strength of GBP is calculated by averaging its performance across seven major GBP currency pairs:
GBP/EUR
GBP/USD
GBP/CAD
GBP/CHF
GBP/AUD
GBP/NZD
GBP/JPY
The strength of JPY is calculated by averaging its performance across seven major JPY currency pairs:
JPY/USD
JPY/CAD
JPY/EUR
JPY/GBP
JPY/AUD
JPY/NZD
JPY/CHF
The values are normalized to allow direct comparison on the same scale.
Identifying Correlation Between GBP and JPY:
The histogram displays the correlation between GBP and JPY strength:
Positive Correlation (Green): Both GBP and JPY are trending up or down together, indicating a less strong trend. This is a market condition to avoid, as both currencies are strengthening or weakening simultaneously.
Negative Correlation (Red): One currency is strong while the other is weak, indicating a stronger trend in GBP/JPY. This scenario presents a better trading opportunity, as you are trading one strong currency against one weak currency, amplifying the potential for a clearer price movement in GBP/JPY.
Visualizing Long/Short Bias:
GBP Strength > JPY Strength: Bullish bias for GBP/JPY (green background).
JPY Strength > GBP Strength: Bearish bias for GBP/JPY (red background).
This indicator equips traders with a deeper understanding of GBP/JPY dynamics by first breaking down the individual currencies. With insights into currency strength, their correlation, and the optimal conditions for trading, it provides a solid foundation for making informed trading decisions.
How to Use:
Check the Histogram for Correlation:
Wait for the histogram to be red. This indicates that GBP and JPY are moving in opposite directions, signaling a stronger trend where you're trading a strong currency against a weak one—a more favorable setup.
Align with Background Color for Confirmation:
Wait for the background color to match your trade plan:
Green Background: Confirms a bullish bias, supporting long positions on the GBP/JPY pair.
Red Background: Confirms a bearish bias, supporting short positions on the GBP/JPY pair.
By following these steps, you can identify stronger trade opportunities and align them with your strategy.
Demand and Supply Zones Intraday Strategy(SAMARESH PANDA)Explanation:
Input Parameters:
length: Determines the number of bars to look back for identifying the highest and lowest prices to mark demand and supply zones.
zoneBuffer: A buffer in pips to place the stop and limit orders near the identified demand and supply zones.
Demand and Supply Zones:
The script identifies demand zones as the lowest low over the past length bars and supply zones as the highest high over the same period.
Buy (Long) Condition:
A buy signal is generated when the price crosses above the demand zone from below and the price closes above the demand zone.
Sell (Short) Condition:
A sell signal is generated when the price crosses below the supply zone from above and the price closes below the supply zone.
Stop and Limit Orders:
For long trades, the stop is placed just below the demand zone (zoneBuffer), and the limit is placed just above the demand zone.
For short trades, the stop is placed just above the supply zone (zoneBuffer), and the limit is placed just below the supply zone.
Exit Conditions:
The long position is closed when the price moves above the supply zone.
The short position is closed when the price moves below the demand zone.
Additional Notes:
Customization: You can modify the length and zoneBuffer values to adjust how sensitive the strategy is to price action and the zone buffers.
Risk Management: You may want to add additional filters for volume, indicators like RSI/MACD, or other market conditions to filter out false signals.
Testing & Optimization: As with any strategy, it's important to backtest and optimize it according to your preferred market conditions and risk tolerance.
Using the Strategy:
Apply the script to a 1-hour or 30-minute chart for identifying the demand and supply zones.
For better precision in entry, you can switch to a lower timeframe (like 5-minute or 15-minute) to execute your trades more accurately when the price reaches a demand or supply zone.
The buy and sell signals will appear on the chart as green (BUY) and red (SELL) labels.
Example of Execution:
Long Entry: The price closes above the demand zone and crosses up. A long position is opened with a stop below the demand zone and a limit near it.
Short Entry: The price closes below the supply zone and crosses down. A short position is opened with a stop above the supply zone and a limit near it.
This strategy works best in trending markets where price tends to respect these zones. Make sure to monitor the strategy closely and adjust it based on market volatility or changes in the broader trend.
You said:
create a pine script pivot point standard for buy sell
ChatGPT said:
ChatGPT
Sure! Below is a Pine Script that implements the Pivot Point Standard for intraday trading, with buy and sell signals based on the pivot point strategy.
Pivot points are calculated based on the previous day’s high, low, and close prices, and they are widely used in intraday trading to determine potential support and resistance levels.
Intraday Chaos FilterIntraday trading requires precise tools and strategies to navigate the chaotic fluctuations of the market. The "Intraday Chaos Filter" indicator is designed to provide traders with a systematic approach to managing market noise. This Pine Script™ indicator helps identify potential trend shifts and provides a visual representation of price action, aiding in making informed trading decisions. By focusing on reducing market chaos, it aims to provide a clearer picture of the underlying trend, regardless of the volatility.
At the heart of the indicator lies the multiplier input, a customizable setting that adjusts the sensitivity of the filter. The multiplier directly influences the chaos threshold by determining the percentage deviation allowed before signaling a trend reversal. A higher multiplier results in a less sensitive filter, reducing the number of signals during minor price fluctuations. This flexibility allows traders to adapt the indicator to different trading styles and market conditions, making it versatile for a range of intraday strategies.
The "Intraday Chaos Filter" operates by tracking two key price levels: src1 and src2, representing the highest and lowest prices during a trend. These levels are dynamically adjusted based on the closing price, ensuring that the indicator adapts in real time. The trend direction is determined by comparing the current price against a calculated filter level. When the price crosses the threshold in either direction, it signals a potential trend reversal, marked by a change in candle colors on the chart. Green represents an upward trend, while red signifies a downward trend, making it visually intuitive for traders.
This indicator's simplicity and clarity make it a valuable addition to any trader's toolkit. By focusing on trend direction and filtering out minor fluctuations, it helps traders stay focused on significant market movements. The integration of customizable settings ensures adaptability to various trading preferences, providing both novice and experienced traders with a reliable tool for intraday decision-making. The "Intraday Chaos Filter" offers an effective way to navigate market noise, enabling traders to identify opportunities with confidence.
Dynamic EMA CrossoverThe Dynamic EMA Crossover indicator is designed to help traders identify trend transitions, visually understand market direction, and detect sideways consolidation zones. It simplifies decision-making by dynamically changing colors and highlighting areas of interest.
Key Features:
1. Dynamic EMA Crossovers:
• Uses two EMAs (default: 9 and 26 ) to identify bullish and bearish trends.
• EMAs and the area between them turn green during bullish trends and red during bearish trends for easy visualization.
2. Sideways Market Detection:
• Automatically detects periods of market consolidation when EMAs overlap for 10 consecutive candles and the price movement remains narrow.
• Sideways zones are highlighted with grey background, helping traders avoid false breakouts and trendless markets.
3. Customizable Inputs:
• Adjust the lengths of the two EMAs and the sensitivity of the overlap detection to match your trading style and market conditions.
How It Works:
• Trend Identification:
• When the shorter EMA crosses above the longer EMA, a bullish trend is indicated.
• When the shorter EMA crosses below the longer EMA, a bearish trend is indicated.
• The indicator dynamically adjusts the colors of the EMAs and fills the area between them for clear trend visibility.
• Sideways Market Detection:
• When the shorter EMA and longer EMA stay close (within a customizable sensitivity) for a fixed period (hardcoded to 10 candles), the indicator identifies a sideways market.
• This feature helps traders avoid entering trades during choppy or indecisive market conditions.
Who Is This For?
This indicator is ideal for:
• Trend traders looking for clear signals of trend direction.
• Swing traders who want to avoid trading in sideways markets.
• Scalpers who need quick and reliable visual cues for short-term market behavior.
Use Cases:
1. Bullish/Bearish Trends:
• Enter trades in the direction of the trend as the crossover occurs and colors change.
2. Sideways Zones:
• Avoid trades during periods of consolidation and wait for a clear breakout.
Mashup Logic:
This indicator combines:
1. EMA Crossovers:
• A tried-and-tested method for trend detection using two moving averages.
• Dynamic visual cues for bullish and bearish market phases.
2. Sideways Market Detection:
• Innovative logic to highlight sideways zones based on EMA overlap and price range analysis.
• Helps reduce noise and avoid trading during trendless periods.
3. Customization and Flexibility:
• Fully adjustable EMA lengths and overlap sensitivity to adapt to different markets and trading styles.
Lost Bar Locator v1 [Yaphott]Lost Bar Locator v1 helps you locate missing data on your chart.
It does this by looking for consecutive bars that have a delta time greater than the current interval.
Two lines are drawn for each group of one or more missing bars:
Bar before the missing bar(s).
Bar after the missing bar(s).
Buy Signal Forex & Crypto v0 ImprovedPurpose of the Script:
This script is designed to generate buy and sell signals for trading Forex and cryptocurrencies by analyzing price trends using exponential moving averages (EMAs), volatility, and volume filters. The signals are displayed as arrows on the chart.
What the Script Does
Input Settings:
The script allows the user to configure various settings, such as the lengths of EMAs, a higher timeframe for trend confirmation, and thresholds for volume and volatility (ATR - Average True Range).
Key settings:
5 EMA Length – Length of the short-term EMA.
13 EMA Length – Length of the medium-term EMA.
26 EMA Length – Length of the long-term EMA.
21 EMA Length – Used for trend confirmation on a higher timeframe.
Higher Timeframe – Lets you select a timeframe (e.g., daily) for confirming the overall trend.
ATR Threshold – Filters out signals when the market's volatility is too low.
Volume Filter – Ensures sufficient trading activity before generating signals.
Calculating EMAs (Exponential Moving Averages):
Four EMAs are calculated:
ema5 (short-term), ema13 (medium-term), ema26 (long-term), and ema21 (higher timeframe confirmation).
These EMAs help determine price trends and crossovers, which are critical for identifying buy and sell opportunities.
Trend Confirmation Using a Higher Timeframe:
The 21 EMA on the higher timeframe (e.g., daily) is used to confirm the overall direction of the market.
Defining Signal Conditions:
Buy Signal:
A buy signal is generated when:
ema5 crosses above ema13 (indicating a bullish trend).
ema5 crosses above ema26 (stronger bullish confirmation).
The closing price is above ema5, ema13, ema26, and the 21 EMA on the higher timeframe.
The market's volatility (ATR) is above the defined threshold.
The volume meets the conditions or volume filtering is disabled.
Sell Signal:
A sell signal is generated when:
ema5 crosses below ema13 (indicating a bearish trend).
ema5 crosses below ema26 (stronger bearish confirmation).
The closing price is below ema5, ema13, ema26, and the 21 EMA on the higher timeframe.
The market's volatility (ATR) is above the defined threshold.
The volume meets the conditions or volume filtering is disabled.
Volume Filtering:
Ensures there’s enough trading activity by comparing the current volume to a 20-period moving average of volume.
Persistent Variables:
These variables (crossed13 and crossed13Sell) help track whether the short-term EMA (ema5) has crossed the medium-term EMA (ema13). This prevents false or repeated signals.
Displaying Signals on the Chart:
Buy signals are displayed as green upward arrows below the price.
Sell signals are displayed as red downward arrows above the price.
How It Helps Traders:
This script provides visual cues for potential entry and exit points by combining moving average crossovers, volatility, volume, and higher timeframe trend confirmation. It works well for trending markets and ensures signals are filtered for stronger conditions to reduce noise.
Chart InfoOVERVIEW
What would a general summary of the symbol on the chart look like? Here’s an example: This script was created to help you easily access the essential details of a symbol, which I believe are critical for daily use.
CONCEPTS
When using any indicator or analysing price movement, the characteristics of the chart become important. Each symbol has a unique character and the more we can quickly find out about it, the better. Instead of embedding those details within each individual indicator, it is often more practical to access these data through an external tool. This indicator presents the following results related to the symbol on your chart in a table format:
ID : Ticker ID (Exchange, Base Currency, and Quote Currency)
TIMEFRAME : The chart's time period
START : The starting date of the chart
FINISH : The finishing date of the chart
INTERVAL : The total time between the start and finish dates (based on timeframe). The current bar is not included in the total time until it is closed.
BAR INDEX : The total number of bars on the chart (can also be viewed in both forward and backward directions in the data window as a series type).
VOLATILITY : Percentage ratio of 14-bar ATR to close.
CHANGE : The daily percentage change.
HODL : The percentage return that would be gained if the symbol had been bought and held since the first bar.
DAILY BUY : The percentage return that would be gained if the same amount of buying was made daily (a kind of DCA).
MECHANICS
This is a very simple script. I didn't add user-defined timestamp inputs because I didn’t want to overwhelm the indicator with parameters. However, if requested, i can make improvements in this direction in a second version.
NOTES
I live in Istanbul, so I designed the default timezone offset as GMT+3. Please remember to adjust it according to your own timezone to ensure the date results are accurate.
I hope it helps everyone. Do not forget to manage your risk. And trade as safely as possible. Best of luck!
Monthly, Quarterly OPEX & Vix expirations
OPEX Indicator:
The OPEX indicator is designed to provide traders with a visual representation of key options expiration dates, particularly for monthly, quarterly, and VIX options expirations. This indicator can be particularly helpful for market participants who focus on options-based strategies or those who track the impact of options expiration on price action.
The indicator overlays vertical lines and labels on the chart to highlight three key types of expiration events:
Monthly Equity and Index Expiration (OPEX): This marks the standard monthly options expiration dates for equity and index options.
Quarterly Index Expiration (Q): This indicates the quarterly expiration dates for index options, which tend to have a larger impact on the market.
Monthly VIX Expiration (VIXEX): This marks the monthly expiration of VIX options and futures, which are important for volatility traders.
How to Use the OPEX Indicator:
Expiration Dates on the Chart: The OPEX indicator marks expiration dates with vertical lines and labels that appear on the chart. These are customizable, allowing you to adjust the line and label colors to suit your preferences. The lines and labels will appear at specific times, such as the closing of the market on expiration days, allowing traders to prepare for potential volatility or other market dynamics associated with these events.
Customizable Colors and Label Positions: The indicator offers flexibility in customizing the appearance of expiration lines and labels. For each expiration type (OPEX, Quarterly, and VIXEX), you can adjust the line color, label color, and label text color. Additionally, the label text size and position can be customized (e.g., above the bar, below the bar, top or bottom of the chart). This allows for a tailored display that suits your trading style and chart layout.
Visualizing Impact of Expiration Events: Traders who track the influence of expiration events can use this indicator to spot potential market moves around expiration dates. For example, significant price swings often occur near expiration days as options traders adjust their positions. With this indicator, you can visualize these dates on your chart and analyze market behavior in the lead-up to, during, and after the expirations.
Input Options:
Expiration Types:
Monthly Equity, Index Expiration (OPEX): Turn on or off the monthly equity expiration markers.
Quarterly Index Expiration (Q): Turn on or off the quarterly expiration markers.
Monthly VIX Expiration (VIXEX): Turn on or off the VIX expiration markers.
Line and Label Customization:
Line Color: Adjust the color of the vertical lines marking the expiration events.
Label Color: Customize the color of the expiration labels.
Label Text Color: Adjust the color of the text inside the labels.
Label Position: Choose the position of the labels (e.g., top, bottom, above bar, below bar).
Use Cases:
Options Traders: Track options expiration dates to assess potential price swings or liquidity changes.
Volatility Traders: Watch for patterns around VIX options expirations.
Index Traders: Monitor quarterly expirations for potential market-moving events.
Example Use:
As a trader, you can apply this indicator to your chart and observe how price action reacts near expiration dates. For instance, on the monthly OPEX expiration day, you might notice increased volatility or an uptick in options-related price moves. By observing this trend over time, you can align your trades to capitalize on predictable movements around key expiration days.
Additionally, you may use the quarterly expiration markers to assess whether there’s typically a market shift during these periods, providing insights for long-term traders.
This indicator can be a helpful tool for preparing and managing trades around critical options expiration dates, helping to forecast potential market behavior based on historical patterns.
TradingView Community Guidelines Compliance: This script complies with TradingView's community guidelines by offering a clear and valuable function for traders, providing customizable inputs for enhanced usability. The script is focused on chart visualizations without manipulating or misrepresenting market data. It serves as an educational tool and a functional indicator, with no claims or misleading functionality. The indicator does not promote financial products or services and focuses solely on charting for better trading decision-making.
IU Trailing Stop Loss MethodsThe 'IU Trailing Stop Loss Methods' it's a risk management tool which allows users to apply 12 trailing stop-loss (SL) methods for risk management of their trades and gives live alerts when the trailing Stop loss has hit. Below is a detailed explanation of each input and the working of the Script.
Main Inputs:
- bar_time: Specifies the date from which the trade begins and entry price will be the open of the first candle.
- entry_type: Choose between 'Long' or 'Short' positions.
- trailing_method: Select the trailing stop-loss method. Options include ATR, Parabolic SAR, Supertrend, Point/Pip based, Percentage, EMA, Highest/Lowest, Standard Deviation, and multiple target-based methods.
- exit_after_close: If checked, exits the trade only after the candle closes.
Optional Inputs:
ATR Settings:
- atr_Length: Length for the ATR calculation.
- atr_factor: ATR multiplier for SL calculation.
Parabolic SAR Settings:
- start, increment, maximum: Parameters for the Parabolic SAR indicator.
Supertrend Settings:
- supertrend_Length, supertrend_factor: Length and factor for the Supertrend indicator.
Point/Pip Based:
- point_base: Set trailing SL in points/pips.
Percentage Based:
- percentage_base: Set SL as a percentage of entry price.
EMA Settings:
- ema_Length: Length for EMA calculation.
Standard Deviation Settings:
- std_Length, std_factor: Length and factor for standard deviation calculation.
Highest/Lowest Settings:
- highest_lowest_Length: Length for the highest/lowest SL calculation.
Target-Based Inputs:
- ATR, Point, Percentage, and Standard Deviation based target SL settings with customizable lengths and multipliers.
Entry Logic:
- Trades initiate based on the entry_type selected and the specified bar_time.
- If Long is selected, a long trade is initiated when the conditions match, and vice versa for Short.
Trailing Stop-Loss (SL) Methods Explained:
The strategy dynamically adjusts stop-loss based on the chosen method. Each method has its calculation logic:
- ATR: Stop-loss calculated using ATR multiplied by a user-defined factor.
- Parabolic SAR: Uses the Parabolic SAR indicator for trailing stop-loss.
- Supertrend: Utilizes the Supertrend indicator as the stop-loss line.
- Point/Pip Based: Fixed point-based stop-loss.
- Percentage Based: SL set as a percentage of entry price.
- EMA: SL based on the Exponential Moving Average.
- Highest/Lowest: Uses the highest high or lowest low over a specified period.
- Standard Deviation: SL calculated using standard deviation.
Exit Conditions:
- If exit_after_close is enabled, the position will only close after the candle confirms the stop-loss hit.
- If exit_after_close is disabled, the strategy will close the trade immediately when the SL is breached.
Visualization:
The script plots the chosen trailing stop-loss method on the chart for easy visualization.
Target-Based Trailing SL Logic:
- When a position is opened, the strategy calculates the initial stop-loss and progressively adjusts it as the price moves favorably.
- Each SL adjustment is stored in an array for accurate tracking and visualization.
Alerts and Labels:
- When the Entry or trailing stop loss is hit this scripts draws a label and give alert to the user that trailing stop has been hit for the trade.
Note - on the historical data The Script will show nothing if the entry and the exit has happened on the same candle, because we don't know what was hit first SL or TP (basically how the candle was formed on the lower timeframe).
Summary:
This script offers flexible trailing stop-loss options for traders who want dynamic risk management in their strategies. By offering multiple methods like ATR, SAR, Supertrend, and EMA, it caters to various trading styles and risk preferences.
Candle 1 2 3 on XAUUSD (by Veronica)Description
Discover the Candle 1 2 3 Strategy, a simple yet effective trading method tailored exclusively for XAUUSD on the 15-minute timeframe. Designed by Veronica, this strategy focuses on identifying key reversal and continuation patterns during the London and New York sessions, making it ideal for traders who prioritise high-probability entries during these active market hours.
Key Features:
1. Session-Specific Trading:
The strategy operates strictly during London (03:00–06:00 UTC) and New York (08:30–12:30 UTC) sessions, where XAUUSD tends to show higher volatility and clearer price movements.
Pattern Criteria:
- Works best if the first candle is NOT a pin bar or a doji.
- Third candle should either:
a. Be a marubozu (large body with minimal wicks).
a. Have a significant body with wicks, ensuring the close of the third candle is above Candle 2 (for Buy) or below Candle 2 (for Sell).
Callout Labels and Alerts:
Automatic Buy and Sell labels are displayed on the chart during qualifying sessions, ensuring clarity for decision-making.
Integrated alerts notify you of trading opportunities in real-time.
Risk Management:
Built-in Risk Calculator to estimate lot sizes based on your account size, risk percentage, and stop-loss levels.
Customizable Table:
Displays your calculated lot size for various stop-loss pip values, making risk management seamless and efficient.
How to Use:
1. Apply the indicator to XAUUSD (M15).
2. Focus on setups appearing within the London and New York sessions only.
3. Ensure the first candle is neither a pin bar nor a doji.
4. Validate the third candle's body placement:
For a Buy, the third candle’s close must be above the second candle.
For a Sell, the third candle’s close must be below the second candle.
5. Use the generated alerts to streamline your entry process.
Notes:
This strategy is meant to complement your existing knowledge of market structure and price action.
Always backtest thoroughly and adjust parameters to fit your personal trading style and risk tolerance.
Credit:
This strategy is the intellectual property of Veronica, developed specifically for XAUUSD (M15) traders seeking precision entries during high-volume sessions.
00 Averaging Down Backtest Strategy by RPAlawyer v21FOR EDUCATIONAL PURPOSES ONLY! THE CODE IS NOT YET FULLY DEVELOPED, BUT IT CAN PROVIDE INTERESTING DATA AND INSIGHTS IN ITS CURRENT STATE.
This strategy is an 'averaging down' backtester strategy. The goal of averaging/doubling down is to buy more of an asset at a lower price to reduce your average entry price.
This backtester code proves why you shouldn't do averaging down, but the code can be developed (and will be developed) further, and there might be settings even in its current form that prove that averaging down can be done effectively.
Different averaging down strategies exist:
- Linear/Fixed Amount: buy $1000 every time price drops 5%
- Grid Trading: Placing orders at price levels, often with increasing size, like $1000 at -5%, $2000 at -10%
- Martingale: doubling the position size with each new entry
- Reverse Martingale: decreasing position size as price falls: $4000, then $2000, then $1000
- Percentage-Based: position size based on % of remaining capital, like 10% of available funds at each level
- Dynamic/Adaptive: larger entries during high volatility, smaller during low
- Logarithmic: position sizes increase logarithmically as price drops
Unlike the above average costing strategies, it applies averaging down (I use DCA as a synonym) at a very strong trend reversal. So not at a certain predetermined percentage negative PNL % but at a trend reversal signaled by an indicator - hence it most closely resembles a dynamically moving grid DCA strategy.
Both entering the trade and averaging down assume a strong trend. The signals for trend detection are provided by an indicator that I published under the name '00 Parabolic SAR Trend Following Signals by RPAlawyer', but any indicator that generates numeric signals of 1 and -1 for buy and sell signals can be used.
The indicator must be connected to the strategy: in the strategy settings under 'External Source' you need to select '00 Parabolic SAR Trend Following Signals by RPAlawyer: Connector'. From this point, the strategy detects when the indicator generates buy and sell signals.
The strategy considers a strong trend when a buy signal appears above a very conservative ATR band, or a sell signal below the ATR band. The conservative ATR is chosen to filter ranging markets. This very conservative ATR setting has a default multiplier of 8 and length of 40. The multiplier can be increased up to 10, but there will be very few buy and sell signals at that level and DCA requirements will be very high. Trade entry and DCA occur at these strong trends. In the settings, the 'ATR Filter' setting determines the entry condition (e.g., ATR Filter multiplier of 9), and the 'DCA ATR' determines when DCA will happen (e.g., DCA ATR multiplier of 6).
The DCA levels and DCA amounts are determined as follows:
The first DCA occurs below the DCA Base Deviation% level (see settings, default 3%) which acts as a threshold. The thick green line indicates the long position avg price, and the thin red line below the green line indicates the 3% DCA threshold for long positions. The thick red line indicates the short position avg price, and the thin red line above the thick red line indicates the short position 3% DCA threshold. DCA size multiplier defines the DCA amount invested.
If the loss exceeds 3% AND a buy signal arrives below the lower ATR band for longs, or a sell signal arrives above the upper ATR band for shorts, then the first DCA will be executed. So the first DCA won't happen at 3%, rather 3% is a threshold where the additional condition is that the price must close above or below the ATR band (let's say the first DCA occured at 8%) – this is why the code resembles a dynamic grid strategy, where the grid moves such that alongside the first 3% threshold, a strong trend must also appear for DCA. At this point, the thick green/red line moves because the avg price is modified as a result of the DCA, and the thin red line indicating the next DCA level also moves. The next DCA level is determined by the first DCA level, meaning modified avg price plus an additional +8% + (3% * the Step Scale Multiplier in the settings). This next DCA level will be indicated by the modified thin red line, and the price must break through this level and again break through the ATR band for the second DCA to occur.
Since all this wasn't complicated enough, and I was always obsessed by the idea that when we're sitting in an underwater position for days, doing DCA and waiting for the price to correct, we can actually enter a short position on the other side, on which we can realize profit (if the broker allows taking hedge positions, Binance allows this in Europe).
This opposite position in this strategy can open from the point AFTER THE FIRST DCA OF THE BASE POSITION OCCURS. This base position first DCA actually indicates that the price has already moved against us significantly so time to earn some money on the other side. Breaking through the ATR band is also a condition for entry here, so the hedge position entry is not automatic, and the condition for further DCA is breaking through the DCA Base Deviation (default 3%) and breaking through the ATR band. So for the 'hedge' or rather opposite position, the entry and further DCA conditions are the same as for the base position. The hedge position avg price is indicated by a thick black line and the Next Hedge DCA Level is indicated by a thin black line.
The TPs are indicated by green labels for base positions and red labels for hedge positions.
No SL built into the strategy at this point but you are free to do your coding.
Summary data can be found in the upper right corner.
The fantastic trend reversal indicator Machine learning: Lorentzian Classification by jdehorty can be used as an external indicator, choose 'backtest stream' for the external source. The ATR Band multiplicators need to be reduced to 5-6 when using Lorentz.
The code can be further developed in several aspects, and as I write this, I already have a few ideas 😊
Best of Option Indicator - Manoj WadekarPlot this indicator for both CALL and PUT options and buy only when color of candle is YELLOW and above BLACK line.
4EMAs+OpenHrs+FOMC+CPIThis script displays 4 custom EMAs of your choice based on the Pine script standard ema function.
Additionally the following events are shown
1. Opening hours for New York Stock exchange
2. Opening Time for London Stock exchange
3. US CPI Release Dates
4. FOMC press conference dates
5. FOMC meeting minutes release dates
I have currently added FOMC and CPI Dates for 2025 but will keep updating in January of every year (at least as long as I stay in the game :D)
India VIXThe VIX chart represents the Volatility Index, commonly referred to as the "Fear Gauge" of the stock market. It measures the market's expectations of future volatility over the next 30 days, based on the implied volatility of NSE index options. The VIX is often used as an indicator of investor sentiment, reflecting the level of fear or uncertainty in the market.
Here’s a breakdown of what you might observe on a typical VIX chart:
VIX Value: The y-axis typically represents the VIX index value, with higher values indicating higher levels of expected market volatility (more fear or uncertainty), and lower values signaling calm or stable market conditions.
VIX Spikes: Large spikes in the VIX often correspond to market downturns or periods of heightened uncertainty, such as during financial crises or major geopolitical events. A high VIX is often associated with a drop in the stock market.
VIX Drops: A decline in the VIX indicates a reduction in expected market volatility, usually linked with periods of market calm or rising stock prices.
Trend Analysis: Technical traders might use moving averages or other indicators on the VIX chart to assess the potential for future market movements.
Inverse Relationship with the Stock Market: Typically, there is an inverse correlation between the VIX and the stock market. When stocks fall sharply, volatility increases, and the VIX tends to rise. Conversely, when the stock market rallies or remains stable, the VIX tends to fall.
A typical interpretation would be that when the VIX is low, the market is relatively stable, and when the VIX is high, the market is perceived to be uncertain or volatile.
Combined Multi-Timeframe EMA OscillatorThis script aims to visualize the strength of bullish or bearish trends by utilizing a mix of 200 EMA across multiple timeframes. I've observed that when the multi-timeframe 200 EMA ribbon is aligned and expanding, the uptrend usually lasts longer and is safer to enter at a pullback for trend continuation. Similarly, when the bands are expanding in reverse order, the downtrend holds longer, making it easier to sell the pullbacks.
In this script, I apply a purely empirical and experimental method: a) Ranking the position of each of the above EMAs and turning it into an oscillator. b) Taking each 200 EMA on separate timeframes, turning it into a stochastic-like oscillator, and then averaging them to compute an overall stochastic.
To filter a bullish signal, I use the bullish crossover between these two aggregated oscillators (default: yellow and blue on the chart) which also plots a green shadow area on the screen and I look for buy opportunities/ ignore sell opportunities while this signal is bullish. Similarly, a bearish crossover gives us a bearish signal which also plots a red shadow area on the screen and I only look for sell opportunities/ ignore any buy opportunities while this signal is bearish.
Note that directly buying the signal as it prints can lead to suboptimal entries. The idea behind the above is that these crossovers point on average to a stronger trend; however, a trade should be initiated on the pullbacks with confirmation from momentum and volume indicators and in confluence with key areas of support and resistance and risk management should be used in order to protect your position.
Disclaimer: This script does not constitute certified financial advice, the current work is purely experimental, use at your own discretion.
Accurate Bollinger Bands mcbw_ [True Volatility Distribution]The Bollinger Bands have become a very important technical tool for discretionary and algorithmic traders alike over the last decades. It was designed to give traders an edge on the markets by setting probabilistic values to different levels of volatility. However, some of the assumptions that go into its calculations make it unusable for traders who want to get a correct understanding of the volatility that the bands are trying to be used for. Let's go through what the Bollinger Bands are said to show, how their calculations work, the problems in the calculations, and how the current indicator I am presenting today fixes these.
--> If you just want to know how the settings work then skip straight to the end or click on the little (i) symbol next to the values in the indicator settings window when its on your chart <--
--------------------------- What Are Bollinger Bands ---------------------------
The Bollinger Bands were formed in the 1980's, a time when many retail traders interacted with their symbols via physically printed charts and computer memory for personal computer memory was measured in Kb (about a factor of 1 million smaller than today). Bollinger Bands are designed to help a trader or algorithm see the likelihood of price expanding outside of its typical range, the further the lines are from the current price implies the less often they will get hit. With a hands on understanding many strategies use these levels for designated levels of breakout trades or to assist in defining price ranges.
--------------------------- How Bollinger Bands Work ---------------------------
The calculations that go into Bollinger Bands are rather simple. There is a moving average that centers the indicator and an equidistant top band and bottom band are drawn at a fixed width away. The moving average is just a typical moving average (or common variant) that tracks the price action, while the distance to the top and bottom bands is a direct function of recent price volatility. The way that the distance to the bands is calculated is inspired by formulas from statistics. The standard deviation is taken from the candles that go into the moving average and then this is multiplied by a user defined value to set the bands position, I will call this value 'the multiple'. When discussing Bollinger Bands, that trading community at large normally discusses 'the multiple' as a multiplier of the standard deviation as it applies to a normal distribution (gaußian probability). On a normal distribution the number of standard deviations away (which trades directly use as 'the multiple') you are directly corresponds to how likely/unlikely something is to happen:
1 standard deviation equals 68.3%, meaning that the price should stay inside the 1 standard deviation 68.3% of the time and be outside of it 31.7% of the time;
2 standard deviation equals 95.5%, meaning that the price should stay inside the 2 standard deviation 95.5% of the time and be outside of it 4.5% of the time;
3 standard deviation equals 99.7%, meaning that the price should stay inside the 3 standard deviation 99.7% of the time and be outside of it 0.3% of the time.
Therefore when traders set 'the multiple' to 2, they interpret this as meaning that price will not reach there 95.5% of the time.
---------------- The Problem With The Math of Bollinger Bands ----------------
In and of themselves the Bollinger Bands are a great tool, but they have become misconstrued with some incorrect sense of statistical meaning, when they should really just be taken at face value without any further interpretation or implication.
In order to explain this it is going to get a bit technical so I will give a little math background and try to simplify things. First let's review some statistics topics (distributions, percentiles, standard deviations) and then with that understanding explore the incorrect logic of how Bollinger Bands have been interpreted/employed.
---------------- Quick Stats Review ----------------
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(If you are comfortable with statistics feel free to skip ahead to the next section)
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-------- I: Probability distributions --------
When you have a lot of data it is helpful to see how many times different results appear in your dataset. To visualize this people use "histograms", which just shows how many times each element appears in the dataset by stacking each of the same elements on top of each other to form a graph. You may be familiar with the bell curve (also called the "normal distribution", which we will be calling it by). The normal distribution histogram looks like a big hump around zero and then drops off super quickly the further you get from it. This shape (the bell curve) is very nice because it has a lot of very nifty mathematical properties and seems to show up in nature all the time. Since it pops up in so many places, society has developed many different shortcuts related to it that speed up all kinds of calculations, including the shortcut that 1 standard deviation = 68.3%, 2 standard deviations = 95.5%, and 3 standard deviations = 99.7% (these only apply to the normal distribution). Despite how handy the normal distribution is and all the shortcuts we have for it are, and how much it shows up in the natural world, there is nothing that forces your specific dataset to look like it. In fact, your data can actually have any possible shape. As we will explore later, economic and financial datasets *rarely* follow the normal distribution.
-------- II: Percentiles --------
After you have made the histogram of your dataset you have built the "probability distribution" of your own dataset that is specific to all the data you have collected. There is a whole complicated framework for how to accurately calculate percentiles but we will dramatically simplify it for our use. The 'percentile' in our case is just the number of data points we are away from the "middle" of the data set (normally just 0). Lets say I took the difference of the daily close of a symbol for the last two weeks, green candles would be positive and red would be negative. In this example my dataset of day by day closing price difference is:
week 1:
week 2:
sorting all of these value into a single dataset I have:
I can separate the positive and negative returns and explore their distributions separately:
negative return distribution =
positive return distribution =
Taking the 25th% percentile of these would just be taking the value that is 25% towards the end of the end of these returns. Or akin the 100%th percentile would just be taking the vale that is 100% at the end of those:
negative return distribution (50%) = -5
positive return distribution (50%) = +4
negative return distribution (100%) = -10
positive return distribution (100%) = +20
Or instead of separating the positive and negative returns we can also look at all of the differences in the daily close as just pure price movement and not account for the direction, in this case we would pool all of the data together by ignoring the negative signs of the negative reruns
combined return distribution =
In this case the 50%th and 100%th percentile of the combined return distribution would be:
combined return distribution (50%) = 4
combined return distribution (100%) = 10
Sometimes taking the positive and negative distributions separately is better than pooling them into a combined distribution for some purposes. Other times the combined distribution is better.
Most financial data has very different distributions for negative returns and positive returns. This is encapsulated in sayings like "Price takes the stairs up and the elevator down".
-------- III: Standard Deviation --------
The formula for the standard deviation (refereed to here by its shorthand 'STDEV') can be intimidating, but going through each of its elements will illuminate what it does. The formula for STDEV is equal to:
square root ( (sum ) / N )
Going back the the dataset that you might have, the variables in the formula above are:
'mean' is the average of your entire dataset
'x' is just representative of a single point in your dataset (one point at a time)
'N' is the total number of things in your dataset.
Going back to the STDEV formula above we can see how each part of it works. Starting with the '(x - mean)' part. What this does is it takes every single point of the dataset and measure how far away it is from the mean of the entire dataset. Taking this value to the power of two: '(x - mean) ^ 2', means that points that are very far away from the dataset mean get 'penalized' twice as much. Points that are very close to the dataset mean are not impacted as much. In practice, this would mean that if your dataset had a bunch of values that were in a wide range but always stayed in that range, this value ('(x - mean) ^ 2') would end up being small. On the other hand, if your dataset was full of the exact same number, but had a couple outliers very far away, this would have a much larger value since the square par of '(x - mean) ^ 2' make them grow massive. Now including the sum part of 'sum ', this just adds up all the of the squared distanced from the dataset mean. Then this is divided by the number of values in the dataset ('N'), and then the square root of that value is taken.
There is nothing inherently special or definitive about the STDEV formula, it is just a tool with extremely widespread use and adoption. As we saw here, all the STDEV formula is really doing is measuring the intensity of the outliers.
--------------------------- Flaws of Bollinger Bands ---------------------------
The largest problem with Bollinger Bands is the assumption that price has a normal distribution. This is assumption is massively incorrect for many reasons that I will try to encapsulate into two points:
Price return do not follow a normal distribution, every single symbol on every single timeframe has is own unique distribution that is specific to only itself. Therefore all the tools, shortcuts, and ideas that we use for normal distributions do not apply to price returns, and since they do not apply here they should not be used. A more general approach is needed that allows each specific symbol on every specific timeframe to be treated uniquely.
The distributions of price returns on the positive and negative side are almost never the same. A more general approach is needed that allows positive and negative returns to be calculated separately.
In addition to the issues of the normal distribution assumption, the standard deviation formula (as shown above in the quick stats review) is essentially just a tame measurement of outliers (a more aggressive form of outlier measurement might be taking the differences to the power of 3 rather than 2). Despite this being a bit of a philosophical question, does the measurement of outlier intensity as defined by the STDEV formula really measure what we want to know as traders when we're experiencing volatility? Or would adjustments to that formula better reflect what we *experience* as volatility when we are actively trading? This is an open ended question that I will leave here, but I wanted to pose this question because it is a key part of what how the Bollinger Bands work that we all assume as a given.
Circling back on the normal distribution assumption, the standard deviation formula used in the calculation of the bands only encompasses the deviation of the candles that go into the moving average and have no knowledge of the historical price action. Therefore the level of the bands may not really reflect how the price action behaves over a longer period of time.
------------ Delivering Factually Accurate Data That Traders Need------------
In light of the problems identified above, this indicator fixes all of these issue and delivers statistically correct information that discretionary and algorithmic traders can use, with truly accurate probabilities. It takes the price action of the last 2,000 candles and builds a huge dataset of distributions that you can directly select your percentiles from. It also allows you to have the positive and negative distributions calculated separately, or if you would like, you can pool all of them together in a combined distribution. In addition to this, there is a wide selection of moving averages directly available in the indicator to choose from.
Hedge funds, quant shops, algo prop firms, and advanced mechanical groups all employ the true return distributions in their work. Now you have access to the same type of data with this indicator, wherein it's doing all the lifting for you.
------------------------------ Indicator Settings ------------------------------
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---- Moving average ----
Select the type of moving average you would like and its length
---- Bands ----
The percentiles that you enter here will be pulled directly from the return distribution of the last 2,000 candles. With the typical Bollinger Bands, traders would select 2 standard deviations and incorrectly think that the levels it highlights are the 95.5% levels. Now, if you want the true 95.5% level, you can just enter 95.5 into the percentile value here. Each of the three available bands takes the true percentile you enter here.
---- Separate Positive & Negative Distributions----
If this box is checked the positive and negative distributions are treated indecently, completely separate from each other. You will see that the width of the top and bottom bands will be different for each of the percentiles you enter.
If this box is unchecked then all the negative and positive distributions are pooled together. You will notice that the width of the top and bottom bands will be the exact same.
---- Distribution Size ----
This is the number of candles that the price return is calculated over. EG: to collect the price return over the last 33 candles, the difference of price from now to 33 candles ago is calculated for the last 2,000 candles, to build a return distribution of 2000 points of price differences over 33 candles.
NEGATIVE NUMBERS(<0) == exact number of candles to include;
EG: setting this value to -20 will always collect volatility distributions of 20 candles
POSITIVE NUMBERS(>0) == number of candles to include as a multiple of the Moving Average Length value set above;
EG: if the Moving Average Length value is set to 22, setting this value to 2 will use the last 22*2 = 44 candles for the collection of volatility distributions
MORE candles being include will generally make the bands WIDER and their size will change SLOWER over time.
I wish you focus, dedication, and earnest success on your journey.
Happy trading :)
Consistency Rule CalculatorThis script, titled "Consistency Rule Calculator" is designed for use on the TradingView platform. It allows traders to input specific values related to their account, daily highest profit, and a consistency rule (as a decimal).
The script then calculates the "Amount Needed to Withdraw" based on the user's input. This value is calculated using the formula:
Amount Needed to Withdraw = (Daily Highest Profit/Consistency Rule )+ Account Type
Each prop firm has its own consistency rule. Follow their rule, and you will be second to payout!
Additionally, it displays the input values and the calculated amount in a customizable table on the chart. The table is formatted with colors for clarity, and it provides a motivational quote about successful trading. Plus, user can adjust the table's position on the screen.
Year-over-Year % Change for PCEPILFEHello, traders!
This indicator is specifically for FRED:PCEPILFE , which is a 'Personal Consumption Expenditures (PCE) Index excluding food and energy.'
What this indicator does is compare the monthly data to that of the same month last year to see how it has changed over the year. This comparison method is widely known as YoY(Year-over-Year).
While I made this indicator to use for FRED:PCEPILFE , you may use it for different charts as long as they show monthly data.
FRED:PCEPILFE is one of the main measures of inflation the Federal Reserve uses.
You can see the YoY % change of the PCE Index excluding food and energy in the official website for the Bureau of Labor Statistics, but unfortunately, I couldn't find one in TradingView.
So instead, I decided to make my own indicator showing the changes using FRED:PCEPILFE .
The code is very simple: it compares the data to the data 12 points ago because 12 points would mean 12 months in this chart. We then multiply the result by 100 for percentage.
Doing so, we compare the current month to the same month of the previous year.
Because I am only interested in the YoY % Change of the index, I pulled the indicator all the way up, covering the original chart data entirely. (Or you could achieve the same by simply moving your indicator to the pane above. But this way, the original chart data is also visible.)
I hope this indicator helps you with your analysis. Feel free to ask questions if have any!
God bless!
Compare Symbol [LuxmiAI]This indicator allows users to plot candles or bars for a selected symbol and add a moving average of their choice as an underlay. Users can customize the moving average type and length, making it versatile for a wide range of trading strategies.
This script is designed to offer flexibility, letting traders select the symbol, timeframe, candle style, and moving average type directly from the input options. The moving averages include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Weighted Moving Average (WMA), and Volume-Weighted Moving Average (VWMA).
Features of the Script
This indicator provides the following key features:
1. Symbol Selection: Users can input the ticker symbol for which they want to plot the data.
2. Timeframe Selection: The script allows users to choose a timeframe for the symbol data.
3. Candle Styles: Users can select from three styles - regular candles, bars, or Heikin-Ashi candles.
4. Moving Average Options: Users can choose between EMA, SMA, WMA, and VWMA for added trend analysis.
5. Customizable Moving Average Length: The length of the moving average can be adjusted to suit individual trading strategies.
How the Script Works
The script starts by taking user inputs for the symbol and timeframe. It then retrieves the open, high, low, and close prices of the selected symbol and timeframe using the request.security function. Users can select between three candle styles: standard candles, bars, and Heikin-Ashi candles. If Heikin-Ashi candles are selected, the script calculates the Heikin-Ashi open, high, low, and close values.
To add further analysis capabilities, the script includes a moving average. Traders can select the moving average type from EMA, SMA, WMA, or VWMA and specify the desired length. The selected moving average is then plotted on the chart to provide a clear visualization of the trend.
Step-by-Step Implementation
1. Input Options: The script starts by taking inputs for the symbol, timeframe, candle style, moving average type, and length.
2. Data Retrieval: The script fetches OHLC data for the selected symbol and timeframe using request.security.
3. Candle Style Logic: It determines which candle style to plot based on the user’s selection. If Heikin-Ashi is selected, the script calculates Heikin-Ashi values.
4. Moving Average Calculation: Depending on the user’s choice, the script calculates the selected moving average.
5. Visualization: The script plots the candles or bars and overlays the moving average on the chart.
Benefits of Using This Indicator
This custom indicator provides multiple benefits for traders. It allows for quick comparisons between symbols and timeframes, helping traders identify trends and patterns. The flexibility to choose different candle styles and moving averages enhances its adaptability to various trading strategies. Additionally, the ability to customize the moving average length makes it suitable for both short-term and long-term analysis.
Session Bar/Candle ColoringChange the color of candles within a user-defined trading session. Borders and wicks can be changed as well, not just the body color.
PREFACE
This script can be used an educational resource for those who are interested in learning Pine Script. Therefore, the script is published open source and is organized in a manner that follows the recommended Style Guide .
While the main premise of the indicator is rather simple, the script showcases various things that can be achieved such as conditional plotting, alignment of indicator settings, user input validation, script optimization, and more. The script also has examples of taking into consideration the chart timeframe and/or different chart types (Heikin Ashi, Renko, etc.) that a user might be running it on. Note: for complete beginners, I strongly suggest going through the Pine Script User Manual (possibly more than once).
FEATURES
Besides being able to select a specific time window, the indicator also provides additional color settings for changing the background color or changing the colors of neutral/indecisive candles, as shown in the image below.
This allows for a higher level of customization beyond the TradingView chart settings or other similar scripts that are currently available.
HOW TO USE
First, define the intraday trading session that will contain the candles to modify. The session can be limited to specific days of the week.
Next, select the parts of the candles that should be modified: Body, Borders, Wick, and/or Background.
For each of the candle parts that were enabled, you can select the colors that will be used depending on whether a candle is bullish (⇧), bearish (⇩), or neutral (⇆).
All other indicator settings will have a detailed tooltip to describe its usage and/or effect.
LIMITATIONS
The indicator is not intended to function on Daily or higher timeframes due to the intraday nature of session time windows.
The indicator cannot always automatically detect the chart type being used, therefore the user is requested to manually input the chart type via the " Chart Style " setting.
Depending on the available historical data and the selected choice for the " Portion of bar in session " setting, the indicator may not be able to update very old candles on the chart.
EXAMPLE USAGE
This section will show examples of different scenarios that the indicator can be used for.
Emphasizing a main trading session.
Defining a "Pre/post market hours background" like is available for some symbols (e.g., NASDAQ:AAPL ).
Highlighting in which bar the midnight candle occurs.
Hiding indecision bars (neutral candles).
Showing only "Regular Trading Hours" for a chart that does not have the option to toggle ETH/RTH. To achieve this, the actual chart data is hidden, and only the indicator is visible; alternatively, a 2nd instance of the indicator could change colors to match the chart background.
Using a combination of Bars and Japanese Candlesticks. Alternatively, this could be done by hiding the main chart data and using 2 instances of the indicator (one with " Chart Style " setting as Bars , and the other set to Candles ).
Using a combination of thin and thick bars on Range charts. Note: requires disabling the "Thin Bars" setting for Bar charts in the TradingView chart settings.
NOTES
If using more than one instance of this indicator on the same chart, you can use the TradingView "Save Indicator Template" feature to avoid having to re-configure the multiple indicators at a later time.
This indicator is intended to work "out-of-the-box" thanks to the behind_chart option introduced to Pine Script in October 2024. But you can always manually bring the indicator to the front just in case the color changes are not being seen (using the "More" option in the indicator status line: More > Visual Order > Bring to front ).
Many thanks to fikira for their help and inspiring me to create open source scripts.
Any feedback including bug reports or suggestions for improving the indicator (or source code itself) are always welcome in the comments section.
MMXM ICT [TradingFinder] Market Maker Model PO3 CHoCH/CSID + FVG🔵 Introduction
The MMXM Smart Money Reversal leverages key metrics such as SMT Divergence, Liquidity Sweep, HTF PD Array, Market Structure Shift (MSS) or (ChoCh), CISD, and Fair Value Gap (FVG) to identify critical turning points in the market. Designed for traders aiming to analyze the behavior of major market participants, this setup pinpoints strategic areas for making informed trading decisions.
The document introduces the MMXM model, a trading strategy that identifies market maker activity to predict price movements. The model operates across five distinct stages: original consolidation, price run, smart money reversal, accumulation/distribution, and completion. This systematic approach allows traders to differentiate between buyside and sellside curves, offering a structured framework for interpreting price action.
Market makers play a pivotal role in facilitating these movements by bridging liquidity gaps. They continuously quote bid (buy) and ask (sell) prices for assets, ensuring smooth trading conditions.
By maintaining liquidity, market makers prevent scenarios where buyers are left without sellers and vice versa, making their activity a cornerstone of the MMXM strategy.
SMT Divergence serves as the first signal of a potential trend reversal, arising from discrepancies between the movements of related assets or indices. This divergence is detected when two or more highly correlated assets or indices move in opposite directions, signaling a likely shift in market trends.
Liquidity Sweep occurs when the market targets liquidity in specific zones through false price movements. This process allows major market participants to execute their orders efficiently by collecting the necessary liquidity to enter or exit positions.
The HTF PD Array refers to premium and discount zones on higher timeframes. These zones highlight price levels where the market is in a premium (ideal for selling) or discount (ideal for buying). These areas are identified based on higher timeframe market behavior and guide traders toward lucrative opportunities.
Market Structure Shift (MSS), also referred to as ChoCh, indicates a change in market structure, often marked by breaking key support or resistance levels. This shift confirms the directional movement of the market, signaling the start of a new trend.
CISD (Change in State of Delivery) reflects a transition in price delivery mechanisms. Typically occurring after MSS, CISD confirms the continuation of price movement in the new direction.
Fair Value Gap (FVG) represents zones where price imbalance exists between buyers and sellers. These gaps often act as price targets for filling, offering traders opportunities for entry or exit.
By combining all these metrics, the Smart Money Reversal provides a comprehensive tool for analyzing market behavior and identifying key trading opportunities. It enables traders to anticipate the actions of major players and align their strategies accordingly.
MMBM :
MMSM :
🔵 How to Use
The Smart Money Reversal operates in two primary states: MMBM (Market Maker Buy Model) and MMSM (Market Maker Sell Model). Each state highlights critical structural changes in market trends, focusing on liquidity behavior and price reactions at key levels to offer precise and effective trading opportunities.
The MMXM model expands on this by identifying five distinct stages of market behavior: original consolidation, price run, smart money reversal, accumulation/distribution, and completion. These stages provide traders with a detailed roadmap for interpreting price action and anticipating market maker activity.
🟣 Market Maker Buy Model
In the MMBM state, the market transitions from a bearish trend to a bullish trend. Initially, SMT Divergence between related assets or indices reveals weaknesses in the bearish trend. Subsequently, a Liquidity Sweep collects liquidity from lower levels through false breakouts.
After this, the price reacts to discount zones identified in the HTF PD Array, where major market participants often execute buy orders. The market confirms the bullish trend with a Market Structure Shift (MSS) and a change in price delivery state (CISD). During this phase, an FVG emerges as a key trading opportunity. Traders can open long positions upon a pullback to this FVG zone, capitalizing on the bullish continuation.
🟣 Market Maker Sell Model
In the MMSM state, the market shifts from a bullish trend to a bearish trend. Here, SMT Divergence highlights weaknesses in the bullish trend. A Liquidity Sweep then gathers liquidity from higher levels.
The price reacts to premium zones identified in the HTF PD Array, where major sellers enter the market and reverse the price direction. A Market Structure Shift (MSS) and a change in delivery state (CISD) confirm the bearish trend. The FVG then acts as a target for the price. Traders can initiate short positions upon a pullback to this FVG zone, profiting from the bearish continuation.
Market makers actively bridge liquidity gaps throughout these stages, quoting continuous bid and ask prices for assets. This ensures that trades are executed seamlessly, even during periods of low market participation, and supports the structured progression of the MMXM model.
The price’s reaction to FVG zones in both states provides traders with opportunities to reduce risk and enhance precision. These pullbacks to FVG zones not only represent optimal entry points but also create avenues for maximizing returns with minimal risk.
🔵 Settings
Higher TimeFrame PD Array : Selects the timeframe for identifying premium/discount arrays on higher timeframes.
PD Array Period : Specifies the number of candles for identifying key swing points.
ATR Coefficient Threshold : Defines the threshold for acceptable volatility based on ATR.
Max Swing Back Method : Choose between analyzing all swings ("All") or a fixed number ("Custom").
Max Swing Back : Sets the maximum number of candles to consider for swing analysis (if "Custom" is selected).
Second Symbol for SMT : Specifies the second asset or index for detecting SMT divergence.
SMT Fractal Periods : Sets the number of candles required to identify SMT fractals.
FVG Validity Period : Defines the validity duration for FVG zones.
MSS Validity Period : Sets the validity duration for MSS zones.
FVG Filter : Activates filtering for FVG zones based on width.
FVG Filter Type : Selects the filtering level from "Very Aggressive" to "Very Defensive."
Mitigation Level FVG : Determines the level within the FVG zone (proximal, 50%, or distal) that price reacts to.
Demand FVG : Enables the display of demand FVG zones.
Supply FVG : Enables the display of supply FVG zones.
Zone Colors : Allows customization of colors for demand and supply FVG zones.
Bottom Line & Label : Enables or disables the SMT divergence line and label from the bottom.
Top Line & Label : Enables or disables the SMT divergence line and label from the top.
Show All HTF Levels : Displays all premium/discount levels on higher timeframes.
High/Low Levels : Activates the display of high/low levels.
Color Options : Customizes the colors for high/low lines and labels.
Show All MSS Levels : Enables display of all MSS zones.
High/Low MSS Levels : Activates the display of high/low MSS levels.
Color Options : Customizes the colors for MSS lines and labels.
🔵 Conclusion
The Smart Money Reversal model represents one of the most advanced tools for technical analysis, enabling traders to identify critical market turning points. By leveraging metrics such as SMT Divergence, Liquidity Sweep, HTF PD Array, MSS, CISD, and FVG, traders can predict future price movements with precision.
The price’s interaction with key zones such as PD Array and FVG, combined with pullbacks to imbalance areas, offers exceptional opportunities with favorable risk-to-reward ratios. This approach empowers traders to analyze the behavior of major market participants and adopt professional strategies for entry and exit.
By employing this analytical framework, traders can reduce errors, make more informed decisions, and capitalize on profitable opportunities. The Smart Money Reversal focuses on liquidity behavior and structural changes, making it an indispensable tool for financial market success.
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.
