Intrinsic Event (Multi DC OS)

4月8日

Overview

This indicator implements an event-based approach to analyze price movements in the foreign exchange market, inspired by the intrinsic time framework introduced in Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller et al. (1995). It identifies significant price events using an intrinsic time perspective and supports multi-agent analysis to reflect the heterogeneous nature of financial markets. The script plots these events as lines and labels on the chart, offering a visual tool for traders to understand market dynamics at different scales.

Key Features

1. Intrinsic Events: The indicator detects directional change (DC) and overshoot (OS) events based on user-defined thresholds (delta), aligning with the paper’s concept of intrinsic time (Section 6). Intrinsic time redefines time based on market activity, expanding during volatile periods and contracting during inactive ones, rather than relying on a physical clock.
   
   
2. Multi-Agent Analysis: Supports up to five agents, each with its own threshold and color settings, reflecting the heterogeneous market hypothesis (Section 5). This allows the indicator to capture the perspectives of market participants with different time horizons, such as short-term FX dealers and long-term central banks.
   
   

How It Works

• Intrinsic Events Detection: The script identifies two types of events using intrinsic time principles:
  
  Directional Change (DC): Triggered when the price reverses by the threshold (delta) against the current trend (e.g., a drop by delta in an uptrend signals a "Down DC").
  
  Overshoot (OS): Occurs when the price continues in the trend direction by the threshold (e.g., a rise by delta in an uptrend signals an "Up OS").
  
  
  
  DC events are plotted as solid lines, and OS events as dashed lines, with labels like "Up DC" or "OS Down" for clarity. The label style adjusts based on the trend to ensure visibility.
  
  
• Multi-Agent Setup: Each agent operates independently with its own threshold, mimicking market participants with varying time horizons (Section 5). Smaller thresholds detect frequent, short-term events, while larger thresholds capture broader, long-term movements.
  
  

Settings

Each agent can be configured with:

• Enable Agent: Toggle the agent on or off.
  
  
• Threshold (%): The percentage threshold (delta) for detecting DC and OS events (default values: 0.1%, 0.2%, 0.5%, 1%, 2% for agents 1–5).
  
  
• Up Mode Color: Color for lines and labels in up mode (DC events).
  
  
• Down Mode Color: Color for lines and labels in down mode (OS events).
  
  

Usage Notes

This indicator is designed for the foreign exchange market, leveraging its high liquidity, as noted in the paper (Section 1). Adjust the threshold values based on the instrument’s volatility—higher volatility leads to more intrinsic events (Section 4). It can be adapted to other markets where event-based analysis applies.

Reference

The methodology is based on:

Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller, M. M. Dacorogna, R. D. Davé, O. V. Pictet, R. B. Olsen, and J. R. Ward (June 28, 1995). Olsen & Associates Preprint.

4月8日

發行說明

Overview

This indicator implements an event-based approach to analyze price movements in the foreign exchange market, inspired by the intrinsic time framework introduced in Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller et al. (1995). It identifies significant price events using an intrinsic time perspective and supports multi-agent analysis to reflect the heterogeneous nature of financial markets. The script plots these events as lines and labels on the chart, offering a visual tool for traders to understand market dynamics at different scales.

Key Features

1. [] Intrinsic Events: The indicator detects directional change (DC) and overshoot (OS) events based on user-defined thresholds (delta), aligning with the paper’s concept of intrinsic time (Section 6). Intrinsic time redefines time based on market activity, expanding during volatile periods and contracting during inactive ones, rather than relying on a physical clock.
   
   [] Multi-Agent Analysis: Supports up to five agents, each with its own threshold and color settings, reflecting the heterogeneous market hypothesis (Section 5). This allows the indicator to capture the perspectives of market participants with different time horizons, such as short-term FX dealers and long-term central banks.
   
   

How It Works

• [] Intrinsic Events Detection: The script identifies two types of events using intrinsic time principles:
  
  
  • [] Directional Change (DC): Triggered when the price reverses by the threshold (delta) against the current trend (e.g., a drop by delta in an uptrend signals a "Down DC").
    
    [] Overshoot (OS): Occurs when the price continues in the trend direction by the threshold (e.g., a rise by delta in an uptrend signals an "Up OS").
    
    
  
  
  DC events are plotted as solid lines, and OS events as dashed lines, with labels like "Up DC" or "OS Down" for clarity. The label style adjusts based on the trend to ensure visibility.
  
  [] Multi-Agent Setup: Each agent operates independently with its own threshold, mimicking market participants with varying time horizons (Section 5). Smaller thresholds detect frequent, short-term events, while larger thresholds capture broader, long-term movements.
  
  

Settings

Each agent can be configured with:

• [] Enable Agent: Toggle the agent on or off.
  
  [] Threshold (%): The percentage threshold (delta) for detecting DC and OS events (default values: 0.1%, 0.2%, 0.5%, 1%, 2% for agents 1–5).
  
  [] Up Mode Color: Color for lines and labels in up mode (DC events).
  
  [] Down Mode Color: Color for lines and labels in down mode (OS events).
  
  

Usage Notes

This indicator is designed for the foreign exchange market, leveraging its high liquidity, as noted in the paper (Section 1). Adjust the threshold values based on the instrument’s volatility—higher volatility leads to more intrinsic events (Section 4). It can be adapted to other markets where event-based analysis applies.

Reference

The methodology is based on:

Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller, M. M. Dacorogna, R. D. Davé, O. V. Pictet, R. B. Olsen, and J. R. Ward (June 28, 1995). Olsen & Associates Preprint.

5月27日

發行說明

WHAT'S NEW?

1. Next OS Price: Predicts the price of next OS. Draws a line 3 bars long at the average of OS length measured from last DC price. Time axis is repainted. Price can go beyond the line, meaning price goes above the average. Color is following OS color.
   
2. Next DC Price: Shows where next DC will occur. Draws a line 3 bars long at the average of DC length measured from the current extreme during period. Time axis is repainted. Color is following DC color.
   
3. Predict OS: Predicts the time and price of next OS. Draws a line 15 bars long at the average of OS length measured from last DC price. Time axis is based on average OS time, it is not repainted and updated every DC event occurs. Color is following OS color.
   
4. Predict DC: Predicts the time and price of next DC. Draws a line 15 bars long at the average of DC length measured from last DC price. Time axis is based on average OS time + average DC time, it is not repainted and updated every DC event occurs. Color is following DC color.
   
5. Statistic: Calculates Total Events, Total OS, Total DC, Average OS Length (%), Last OS Length (%), Last OS Time (Bar), Last DC Time (Bar), Average Time OS (Bar), Average Time DC (Bar), Standard Deviation OS Length, Standard Deviation OS Time, Standard Deviation DC Time.
   

Overview

This indicator implements an event-based approach to analyze price movements in the foreign exchange market, inspired by the intrinsic time framework introduced by Golub Anton and Glattfelder, James and Olsen, Richard B. It identifies significant price events using an intrinsic time perspective and supports multi-agent analysis to reflect the heterogeneous nature of financial markets. The script plots these events as lines and labels on the chart, offering a visual tool for traders to understand market dynamics at different scales.

Key Features

• Intrinsic Events: The indicator detects directional change (DC) and overshoot (OS) events based on user-defined thresholds (delta). Intrinsic time redefines time based on market activity, expanding during volatile periods and contracting during inactive ones, rather than relying on a physical clock.
  
  
• Multi-Agent Analysis: Supports up to five agents, each with its own threshold and color settings, reflecting the heterogeneous market hypothesis. This allows the indicator to capture the perspectives of market participants with different time horizons.
  

How It Works

Intrinsic Events Detection: The script identifies two types of events using intrinsic time principles:

• Directional Change (DC): Triggered when the price reverses by the threshold (delta) against the current trend (e.g., a drop by delta in an uptrend signals a "Down DC").
  
  
• Overshoot (OS): Occurs when the price continues in the trend direction by the threshold (e.g., a rise by delta in an uptrend signals an "Up OS").
  

DC events are plotted as solid lines, and OS events as dashed lines, with labels like "Up DC" or "OS Down" for clarity. The label style adjusts based on the trend to ensure visibility.

Multi-Agent Setup: Each agent operates independently with its own threshold, mimicking market participants with varying time horizons. Smaller thresholds detect frequent, short-term events, while larger thresholds capture broader, long-term movements.


Settings

Each agent can be configured with:

Enable Agent: Toggle the agent on or off.

Threshold (%): The percentage threshold (delta) for detecting DC and OS events (default values: 0.1%, 0.2%, 0.5%, 1%, 2% for agents 1–5).

Reference

The methodology is based on:
Golub, Anton and Glattfelder, James and Olsen, Richard B., The Alpha Engine: Designing an Automated Trading Algorithm (April 5, 2017). High Performance Computing in Finance, Chapman & Hall/CRC Series in Mathematical Finance, 2017