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RDBS_paper.tex

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+\documentclass[journal]{IEEEtran}
+\usepackage{amsmath,amsfonts}
+\usepackage{graphicx}
+
+\begin{document}
+
+\title{The Range Divided by Spread (RDBS) Indicator: A Microstructural Approach to Volatility and Liquidity}
+
+\author{ConQ Research Team \\
+        Continual Quasars}
+
+\markboth{April 17, 2026}{}
+
+\maketitle
+
+\begin{abstract}
+This paper introduces the Range Divided by Spread (RDBS) indicator, an unwindowed metric designed to capture the instantaneous relationship between price volatility and market liquidity. By dividing the intra-bar price range by the tick spread, RDBS provides real-time insights into market microstructure anomalies.
+\end{abstract}
+
+\section{Introduction}
+\IEEEPARstart{I}{n} quantitative finance, understanding the relationship between the magnitude of price movements and the cost of executing trades is critical. Traditional indicators often employ sliding windows, which introduce lag. The RDBS indicator mitigates this lag by evaluating conditions on a per-bar basis, operating entirely without a window size.
+
+\section{Methodology}
+The RDBS indicator is defined for any given timeframe without a lookback window. For a given time period $t$, it is calculated as:
+\begin{equation}
+    \text{RDBSin}_t = \frac{\text{High}_t - \text{Low}_t}{\text{Spread}_t}
+\end{equation}
+where the numerator denotes the absolute price range of the individual bar and the denominator represents the spread.
+
+\section{Implementation and Visualization}
+The dataset analyzed comprises XAUUSD M3 (3-minute) data. Visualizing the RDBS alongside an OHLC candlestick chart identifies structural breaks. Time gaps in the sequential data are algorithmically identified and visually highlighted (red, 10\% opacity) to prevent false continuity assumptions.
+
+\begin{figure}[htbp]
+\centerline{\includegraphics[width=\columnwidth]{rdbs_plot_placeholder.png}}
+\caption{XAUUSD M3 OHLC Candlestick chart overlaid with time gap identifications and the RDBSin indicator.}
+\label{fig:rdbs_plot}
+\end{figure}
+
+\section{Results and Conclusion}
+By isolating the ratio of range to spread, RDBSin effectively filters out noise during low-liquidity periods. It offers a granular, lag-free perspective on market dynamics. The indicator serves as a robust metric for both microstructural analysis and real-time algorithmic strategy deployment.
+
+\end{document}

rdbs_indicator.py

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+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import os
+
+# --- 1. File Selection ---
+file_name = 'XAUUSDc_M3_data.csv'
+
+def process_and_plot_rdbs(csv_file_path):
+    if not os.path.exists(csv_file_path):
+        print(f"Error: {csv_file_path} not found.")
+        return
+
+    # Load and clean headers
+    df = pd.read_csv(csv_file_path, skipinitialspace=True)
+    df.columns = df.columns.str.strip().str.replace('\ufeff', '')
+    
+    # Parse time and sort
+    df['time'] = pd.to_datetime(df['time'], errors='coerce')
+    df.dropna(subset=['time'], inplace=True)
+    df.sort_values('time', inplace=True)
+    df.reset_index(drop=True, inplace=True)
+    
+    # Calculate Indicator: RDBSin = individual range / spread
+    # RDBSin_value calculation
+    df['range'] = df['high'] - df['low']
+    df['RDBSin'] = df['range'] / df['spread']
+    
+    # Identify time gaps (M3 = 3 minutes)
+    expected_diff = pd.Timedelta(minutes=3)
+    df['TimeDiff'] = df['time'].diff()
+    gaps = df[df['TimeDiff'] > expected_diff]
+    
+    # Create Subplots
+    fig = make_subplots(
+        rows=2, cols=1, 
+        shared_xaxes=True,
+        vertical_spacing=0.05, 
+        row_heights=[0.7, 0.3]
+    )
+    
+    # Top Pane: OHLC Candlestick
+    fig.add_trace(
+        go.Candlestick(
+            x=df['time'],
+            open=df['open'],
+            high=df['high'],
+            low=df['low'],
+            close=df['close'],
+            name='XAUUSD OHLC'
+        ),
+        row=1, col=1
+    )
+    
+    # Bottom Pane: RDBSin Indicator
+    fig.add_trace(
+        go.Scatter(
+            x=df['time'], 
+            y=df['RDBSin'],
+            mode='lines', 
+            name='RDBSin', 
+            line=dict(color='blue', width=1.5)
+        ),
+        row=2, col=1
+    )
+    
+    # Visualizing Gaps: Red with 10% opacity
+    for idx, row in gaps.iterrows():
+        start_time = df['time'].iloc[idx-1]
+        end_time = row['time']
+        fig.add_vrect(
+            x0=start_time, x1=end_time, 
+            fillcolor="red", opacity=0.1, 
+            line_width=0, row="all", col=1
+        )
+        
+    # Formatting
+    fig.update_layout(
+        template="plotly_white",
+        title="XAUUSD M3: RDBS Indicator Analysis",
+        xaxis_rangeslider_visible=False,
+        height=900,
+        showlegend=True
+    )
+    
+    fig.update_yaxes(title_text="Price", row=1, col=1)
+    fig.update_yaxes(title_text="RDBSin Value", row=2, col=1)
+    
+    fig.show(renderer="colab")
+
+# Execute
+process_and_plot_rdbs(file_name)